From ecf3a2e383f3f37a84e6d7ec480ea0006863bcfd Mon Sep 17 00:00:00 2001
From: Ibra Lujumba <ibra.lujumba@gmail.com>
Date: Tue, 10 Mar 2020 12:56:43 +0300
Subject: [PATCH 01/10] Kaggle assignment progress

---
 .DS_Store                                        | Bin 0 -> 6148 bytes
 .../Ibra Lujumba_kaggle assignment.ipynb         |   1 +
 ...Current AI Platforms and where to start.ipynb |   2 +-
 3 files changed, 2 insertions(+), 1 deletion(-)
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 create mode 100644 Assignment Colab/Ibra Lujumba_kaggle assignment.ipynb

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diff --git a/Assignment Colab/Ibra Lujumba_kaggle assignment.ipynb b/Assignment Colab/Ibra Lujumba_kaggle assignment.ipynb
new file mode 100644
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+++ b/Assignment Colab/Ibra Lujumba_kaggle assignment.ipynb	
@@ -0,0 +1 @@
+{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Exploratory Data Analysis "},{"metadata":{},"cell_type":"markdown","source":"### Importing python modules for data analysis and visualization"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np # manipulation of arrays\nimport pandas as pd # manipulating dataframes\nimport matplotlib.pyplot as plt # data visualisation\nimport seaborn as sb # data visualisation,it is based on plt","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#ignoring warnings that may arise\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":4,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Importing the datasets"},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input/ace-class-assignment/\n\n# reading in the data\ndata = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\nnew = pd.read_csv('../input/ace-class-assignment/Test.csv')","execution_count":5,"outputs":[{"output_type":"stream","text":"AMP_TrainSet.csv  Test.csv\r\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Checking the dimensions of the data as well as the datatype of each column"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking dimensions of the datasets\ndata.shape, new.shape","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"((3038, 12), (758, 11))"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the datatypes of the variables\ndata.dtypes, new.dtypes","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"(FULL_Charge           float64\n FULL_AcidicMolPerc    float64\n FULL_AURR980107       float64\n FULL_DAYM780201       float64\n FULL_GEOR030101       float64\n FULL_OOBM850104       float64\n NT_EFC195               int64\n AS_MeanAmphiMoment    float64\n AS_DAYM780201         float64\n AS_FUKS010112         float64\n CT_RACS820104         float64\n CLASS                   int64\n dtype: object,\n FULL_Charge           float64\n FULL_AcidicMolPerc    float64\n FULL_AURR980107       float64\n FULL_DAYM780201       float64\n FULL_GEOR030101       float64\n FULL_OOBM850104       float64\n NT_EFC195               int64\n AS_MeanAmphiMoment    float64\n AS_DAYM780201         float64\n AS_FUKS010112         float64\n CT_RACS820104         float64\n dtype: object)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"All the values in all the variables exists as either floats or integers.\n\nProceeding to work with the training dataset to build the classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the descriptive statistics of the train dataset such as arithmetic mean, \n# standard deviation, quartiles and number of non-NA values in each column \ndata.describe()","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\ncount  3038.000000         3038.000000      3038.000000      3038.000000   \nmean      2.060237            8.521520         0.971410        73.668760   \nstd       3.819929            7.586652         0.107413         8.527489   \nmin     -16.000000            0.000000         0.684000        42.750000   \n25%       0.000000            2.516000         0.895000        68.294000   \n50%       2.000000            7.143000         0.963000        74.059500   \n75%       4.000000           13.158000         1.041000        79.343750   \nmax      30.000000           46.667000         1.451000       101.682000   \n\n       FULL_GEOR030101  FULL_OOBM850104    NT_EFC195  AS_MeanAmphiMoment  \\\ncount      3038.000000      3038.000000  3038.000000         3038.000000   \nmean          0.994007        -2.432927     0.088545           15.683233   \nstd           0.031333         1.707223     0.284133           11.575665   \nmin           0.866000       -10.432000     0.000000            0.041000   \n25%           0.974000        -3.606000     0.000000            5.587500   \n50%           0.994000        -2.296500     0.000000           14.988500   \n75%           1.011000        -1.283250     0.000000           26.807750   \nmax           1.196000         3.576000     1.000000           51.280000   \n\n       AS_DAYM780201  AS_FUKS010112  CT_RACS820104        CLASS  \ncount    3038.000000    3038.000000    3038.000000  3038.000000  \nmean       73.650828       5.911361       1.235255     0.500000  \nstd         9.166092       0.693689       0.210012     0.500082  \nmin        42.778000       3.533000       0.785000     0.000000  \n25%        67.556000       5.459250       1.082000     0.000000  \n50%        73.697000       5.925500       1.184000     0.500000  \n75%        79.778000       6.382000       1.351000     1.000000  \nmax       103.167000       8.662000       2.192000     1.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FULL_Charge</th>\n      <th>FULL_AcidicMolPerc</th>\n      <th>FULL_AURR980107</th>\n      <th>FULL_DAYM780201</th>\n      <th>FULL_GEOR030101</th>\n      <th>FULL_OOBM850104</th>\n      <th>NT_EFC195</th>\n      <th>AS_MeanAmphiMoment</th>\n      <th>AS_DAYM780201</th>\n      <th>AS_FUKS010112</th>\n      <th>CT_RACS820104</th>\n      <th>CLASS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2.060237</td>\n      <td>8.521520</td>\n      <td>0.971410</td>\n      <td>73.668760</td>\n      <td>0.994007</td>\n      <td>-2.432927</td>\n      <td>0.088545</td>\n      <td>15.683233</td>\n      <td>73.650828</td>\n      <td>5.911361</td>\n      <td>1.235255</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.819929</td>\n      <td>7.586652</td>\n      <td>0.107413</td>\n      <td>8.527489</td>\n      <td>0.031333</td>\n      <td>1.707223</td>\n      <td>0.284133</td>\n      <td>11.575665</td>\n      <td>9.166092</td>\n      <td>0.693689</td>\n      <td>0.210012</td>\n      <td>0.500082</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-16.000000</td>\n      <td>0.000000</td>\n      <td>0.684000</td>\n      <td>42.750000</td>\n      <td>0.866000</td>\n      <td>-10.432000</td>\n      <td>0.000000</td>\n      <td>0.041000</td>\n      <td>42.778000</td>\n      <td>3.533000</td>\n      <td>0.785000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>2.516000</td>\n      <td>0.895000</td>\n      <td>68.294000</td>\n      <td>0.974000</td>\n      <td>-3.606000</td>\n      <td>0.000000</td>\n      <td>5.587500</td>\n      <td>67.556000</td>\n      <td>5.459250</td>\n      <td>1.082000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2.000000</td>\n      <td>7.143000</td>\n      <td>0.963000</td>\n      <td>74.059500</td>\n      <td>0.994000</td>\n      <td>-2.296500</td>\n      <td>0.000000</td>\n      <td>14.988500</td>\n      <td>73.697000</td>\n      <td>5.925500</td>\n      <td>1.184000</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>4.000000</td>\n      <td>13.158000</td>\n      <td>1.041000</td>\n      <td>79.343750</td>\n      <td>1.011000</td>\n      <td>-1.283250</td>\n      <td>0.000000</td>\n      <td>26.807750</td>\n      <td>79.778000</td>\n      <td>6.382000</td>\n      <td>1.351000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>30.000000</td>\n      <td>46.667000</td>\n      <td>1.451000</td>\n      <td>101.682000</td>\n      <td>1.196000</td>\n      <td>3.576000</td>\n      <td>1.000000</td>\n      <td>51.280000</td>\n      <td>103.167000</td>\n      <td>8.662000</td>\n      <td>2.192000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"raw","source":"From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\nAS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\nFor all the variables, the data points are not widely spread"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the proprotions of classses\ndata.groupby('CLASS').size()","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"CLASS\n0    1519\n1    1519\ndtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"raw","source":"Both classes have an equal number of entries"},{"metadata":{"trusted":true},"cell_type":"code","source":"# obtaining pairwise correlation values for the variables in the train dataset\n# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\npearsoncorr = data.corr(method='pearson')\n\n# visualizing the correlation matrix as a heatmap to make interpretation easier\nplt.figure(figsize=(10,10))\ntop_corr = pearsoncorr.index\nsb.heatmap(pearsoncorr, \n            xticklabels=pearsoncorr.columns,\n            yticklabels=pearsoncorr.columns,\n            cmap='RdYlGn',\n            annot=True,\n            linewidth=0.5)","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7fca1aa3f2e8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x720 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."},{"metadata":{},"cell_type":"markdown","source":"You can get the p-values associated with the correlation values using the code below.\n\n`from scipy.stats import pearsonr`\n\n`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n\nIn this example, all values were too low to be informative"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using a scatter plot matrix to visualise correlations\n# plt.figure(figsize=(60,60))\n# sb.pairplot(data)","execution_count":11,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\nThese variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n\nVariables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"},{"metadata":{"trusted":true},"cell_type":"code","source":"len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n\n#this confirms that NT_EFC195 is a categorical variable","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"(2830, array([0, 1]))"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"   CLASS  NT_EFC195\n0      1          0\n1      1          1\n2      1          0\n3      1          0\n4      1          0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CLASS</th>\n      <th>NT_EFC195</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the associated p-values. The value of 1 at the bottom should be ignored \nfrom scipy.stats import pearsonr\ndata.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"FULL_Charge           3.404241e-224\nFULL_AcidicMolPerc    4.220004e-295\nFULL_AURR980107       1.887905e-277\nFULL_DAYM780201       6.997934e-245\nFULL_GEOR030101        2.669597e-48\nFULL_OOBM850104       7.441087e-154\nNT_EFC195              2.189203e-48\nAS_MeanAmphiMoment     0.000000e+00\nAS_DAYM780201         4.911002e-142\nAS_FUKS010112          6.540694e-02\nCT_RACS820104          5.329249e-51\nCLASS                  1.000000e+00\nName: CLASS, dtype: float64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the distribution and skewness of variables\nplt.figure(figsize=(10,6))\ndata.skew().plot(kind='bar')","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7fca0f8a75f8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x432 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.groupby('NT_EFC195').size() # majority of the instances are of Class 0.","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"NT_EFC195\n0    2769\n1     269\ndtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fca133b2be0>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca133dbe48>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca1338c2e8>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fca13334748>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca132deba8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca1330f048>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fca132b74a8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca132dd940>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca132dd978>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fca132900b8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca0deb3518>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca0de5b978>]],\n      dtype=object)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x720 with 12 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n\nTranformation possibilities\n* using the minimum and maximum scaler\n* standardisation"},{"metadata":{},"cell_type":"markdown","source":"### Data transformation"},{"metadata":{"trusted":true},"cell_type":"code","source":"# converting Pandas dataframe to ndArray\ndataArray = data.to_numpy()\n\n# seperating the predictor and response variables\ntarget = dataArray[:,11]\npredictors = dataArray[:,0:11]","execution_count":18,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using minMaxScaler to set all values between 0 and 1\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler(feature_range=(0,1))\nrescaledPredictors = scaler.fit_transform(predictors)\n                        \n\n# using StandardScaler\nfrom sklearn.preprocessing import StandardScaler\nscaler1 = StandardScaler().fit(predictors)\nstandardizedPredictors = scaler1.transform(predictors)\n                        \n","execution_count":19,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Feature selection"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n\n# untransformed data\nfrom sklearn.feature_selection import SelectKBest, f_classif\nbestFeatures = SelectKBest(score_func=f_classif, k=7)\nfit = bestFeatures.fit(predictors, target)\n\nscores = pd.DataFrame(fit.scores_) \npvalues = pd.DataFrame(fit.pvalues_)\ncolumns = pd.DataFrame(data.columns[0:11])\n\nfeatureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\nfeatureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n\nprint(featureValues.nlargest(7, 'score'))","execution_count":20,"outputs":[{"output_type":"stream","text":"            predictor        score         pvalue\n7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n2     FULL_AURR980107  1572.280677  1.887905e-277\n3     FULL_DAYM780201  1350.300743  6.997934e-245\n0         FULL_Charge  1214.902838  3.404241e-224\n5     FULL_OOBM850104   785.124798  7.441087e-154\n8       AS_DAYM780201   717.317408  4.911002e-142\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the transformed data\n\n# rescaledPredictors\nreBestFeatures = SelectKBest(score_func=f_classif, k=7)\nreFit = reBestFeatures.fit(rescaledPredictors, target)\n\nreScores = pd.DataFrame(reFit.scores_) \nrePvalues = pd.DataFrame(reFit.pvalues_)\nreColumns = pd.DataFrame(data.columns[0:11])\n\nreFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\nreFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n\n\n\n# standardizedPredictors\nstBestFeatures = SelectKBest(score_func=f_classif, k=7)\nstFit = stBestFeatures.fit(standardizedPredictors, target)\n\nstScores = pd.DataFrame(stFit.scores_) \nstPvalues = pd.DataFrame(stFit.pvalues_)\nstColumns = pd.DataFrame(data.columns[0:11])\n\nstFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\nstFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n\nprint(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))","execution_count":21,"outputs":[{"output_type":"stream","text":"         re_predictor     re_score      re_pvalue\n7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n2     FULL_AURR980107  1572.280677  1.887905e-277\n3     FULL_DAYM780201  1350.300743  6.997934e-245\n0         FULL_Charge  1214.902838  3.404241e-224\n5     FULL_OOBM850104   785.124798  7.441087e-154\n8       AS_DAYM780201   717.317408  4.911002e-142\n         st_predictor     st_score      st_pvalue\n7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n2     FULL_AURR980107  1572.280677  1.887905e-277\n3     FULL_DAYM780201  1350.300743  6.997934e-245\n0         FULL_Charge  1214.902838  3.404241e-224\n5     FULL_OOBM850104   785.124798  7.441087e-154\n8       AS_DAYM780201   717.317408  4.911002e-142\n","name":"stdout"},{"output_type":"execute_result","execution_count":21,"data":{"text/plain":"(None, None)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using feature importance\nfrom sklearn.ensemble import ExtraTreesClassifier\nmodel = ExtraTreesClassifier()\nmodel.fit(predictors, target)\nprint(model.feature_importances_)\n\n# visualising feature importance\nimportances = pd.Series(model.feature_importances_, index=data.columns[0:11])\nimportances.nlargest(10).plot(kind='barh')\nplt.show()","execution_count":22,"outputs":[{"output_type":"stream","text":"[0.07902812 0.14524468 0.09617523 0.09684783 0.05136757 0.07509526\n 0.03643717 0.28509498 0.05087749 0.03093695 0.05289472]\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"### Building the classification model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\nfrom sklearn.model_selection import train_test_split # random split\nfrom sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\np_train, p_test, t_train, t_test = train_test_split(predictors, target, \n                                                    test_size=0.30,random_state=42)\n\nlogit = LogisticRegression() # making instance of model\n\n# fitting the model on untransformed data\nlogit.fit(p_train, t_train)","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n                   multi_class='auto', n_jobs=None, penalty='l2',\n                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n                   warm_start=False)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Measuring model performance\n\nWe can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# predict on test data\npredictions = logit.predict(p_test) ","execution_count":24,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# the confusion matrix\nfrom sklearn import metrics\ncm = metrics.confusion_matrix(t_test, predictions)\ncm\nsb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n          square=True, cmap='Blues') \nplt.ylabel('Actual label'); plt.xlabel('Predicted label')\n","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"Text(0.5, 15.0, 'Predicted label')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\nprint(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\nprint(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, higher values are better\n# not affected by unbalanced classes\n\n\n","execution_count":26,"outputs":[{"output_type":"stream","text":"Accuracy:  91.77631578947368\nPrecision:  92.82608695652173\nRecall:  91.04477611940298\nMCC:  0.8356480321235562\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ROC curve of true positive rate against false positive rate\n# shows tradeoff between sensitivy and specificity\n\npred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\nfpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\nauc = metrics.roc_auc_score(t_test, pred_probs)\nplt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\nplt.legend(loc=4)\nplt.ylabel('tpr'), plt.xlabel('fpr')\nplt.show()","execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data\nnew_pred = logit.predict(new.values)\n\npred_df = pd.DataFrame(new_pred) \npred_df.columns=[\"CLASS\"]\npred_df.index.name=\"Index\" \npred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df.to_csv(\"ilujumba.csv\") \nprint(pred_df['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df.groupby('CLASS').size()[0].sum())\nprint(pred_df.groupby('CLASS').size()[1].sum())","execution_count":28,"outputs":[{"output_type":"stream","text":"['False' 'True']\n386\n372\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on scaled data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit1 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit1.fit(p1_train, t1_train)\n\n# predict on test data\npredictions1 = logit1.predict(p1_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\nprint(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\nprint(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t1_test, predictions1))\n\n# rescaling new data\nnewArray = new.to_numpy()\nrescaledNew = scaler.fit_transform(newArray)\n","execution_count":29,"outputs":[{"output_type":"stream","text":"Accuracy:  91.66666666666666\nPrecision:  92.81045751633987\nRecall:  90.8315565031983\nMCC:  0.8335025900424667\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data (rescaled)\nnew_pred1 = logit1.predict(rescaledNew)\n\npred_df1 = pd.DataFrame(new_pred1) \npred_df1.columns=[\"CLASS\"]\npred_df1.index.name=\"Index\" \npred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df1.to_csv(\"ilujumba1.csv\") \nprint(pred_df1['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df1.groupby('CLASS').size()[0].sum())\nprint(pred_df1.groupby('CLASS').size()[1].sum())","execution_count":30,"outputs":[{"output_type":"stream","text":"['False' 'True']\n378\n380\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on standardized data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit2 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit2.fit(p2_train, t2_train)\n\n# predict on test data\npredictions2 = logit2.predict(p2_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\nprint(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\nprint(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\nprint('MCC: ',matthews_corrcoef(t2_test, predictions2))\n\n# standardizing new data\nstandardizedNew = scaler1.transform(newArray)\n\n# performance on new data (standaridized)\nnew_pred2 = logit2.predict(standardizedNew)\n\npred_df2 = pd.DataFrame(new_pred2) \npred_df2.columns=[\"CLASS\"]\npred_df2.index.name=\"Index\" \npred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df2.to_csv(\"ilujumba2.csv\") \nprint(pred_df2['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df2.groupby('CLASS').size()[0].sum())\nprint(pred_df2.groupby('CLASS').size()[1].sum())","execution_count":31,"outputs":[{"output_type":"stream","text":"Accuracy:  91.8859649122807\nPrecision:  93.21663019693655\nRecall:  90.8315565031983\nMCC:  0.8379994044962448\n['False' 'True']\n388\n370\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Using selected features\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n                                                        test_size=0.30,random_state=42)\n\nlogit3 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit3.fit(p3_train, t3_train)\n\n# predict on test data\npredictions3 = logit3.predict(p3_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\nprint(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\nprint(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t3_test, predictions3))\n\n# rescaling new data\n# newArray = new.to_numpy()\n# rescaledNew = scaler.fit_transform(newArray)\n\n# performance on new data (rescaled)\nnew_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n\npred_df3 = pd.DataFrame(new_pred3) \npred_df3.columns=[\"CLASS\"]\npred_df3.index.name=\"Index\" \npred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df3.to_csv(\"ilujumba3.csv\") \nprint(pred_df3['CLASS'].unique())\n\n\n#printing the numbers of False and True\nprint(pred_df3.groupby('CLASS').size()[0].sum())\nprint(pred_df3.groupby('CLASS').size()[1].sum())","execution_count":32,"outputs":[{"output_type":"stream","text":"Accuracy:  90.5701754385965\nPrecision:  91.36069114470843\nRecall:  90.19189765458422\nMCC:  0.8113912389992642\n['False' 'True']\n390\n368\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Using cross-validation and Logistic Regression"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\n\nkfold = KFold(n_splits=10, random_state=42)\nmodel6 = LogisticRegression()\nmodel6.fit(predictors, target)\n\nresults = cross_val_score(model6, predictors, target)\nprint(results.mean())\n\nmodel6_pred = model6.predict(rescaledNew)\ndf6 = pd.DataFrame(model6_pred)\ndf6.columns = ['CLASS']\ndf6.index.name = 'Index'\ndf6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n\ndf6.to_csv('ilujumba7.csv')\nprint(df6['CLASS'].unique())\n\n\n","execution_count":33,"outputs":[{"output_type":"stream","text":"0.8387328752276078\n[ True]\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier with kfold cross-validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel7 = GaussianNB()\nmodel7.fit(predictors, target)\n\nresults = cross_val_score(model7, predictors, target)\nprint(results.mean())\n\nmodel7_pred = model7.predict(newArray)\ndf7 = pd.DataFrame(model7_pred)\ndf7.columns = ['CLASS']\ndf7.index.name = 'Index'\ndf7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf7.to_csv('ilujumba7.csv')\nprint(df7['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df7.groupby('CLASS').size()[0].sum())\nprint(df7.groupby('CLASS').size()[1].sum())","execution_count":34,"outputs":[{"output_type":"stream","text":"0.887773129281193\n['True' 'False']\n370\n388\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier on rescaled features"},{"metadata":{"trusted":true},"cell_type":"code","source":"kfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel8 = GaussianNB()\nmodel8.fit(rescaledPredictors, target)\n\nresults1 = cross_val_score(model8, rescaledPredictors, target)\nprint(results1.mean())\n# print('MCC: ', matthewscorrcoef())\n\nmodel8_pred = model8.predict(rescaledNew)\ndf8 = pd.DataFrame(model8_pred)\ndf8.columns = ['CLASS']\ndf8.index.name = 'Index'\ndf8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf8.to_csv('ilujumba8.csv')\nprint(df8['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df8.groupby('CLASS').size()[0].sum())\nprint(df8.groupby('CLASS').size()[1].sum())","execution_count":35,"outputs":[{"output_type":"stream","text":"0.887773129281193\n['True' 'False']\n347\n411\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import cross_val_predict\nfrom sklearn.naive_bayes import GaussianNB\n\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel9 = GaussianNB()\nmodel9.fit(predictors, target)\n\nresults = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\nprint('mean for results', results.mean())\n\npredic = cross_val_predict(model9, predictors, target, cv =10)\naccuracy = metrics.r2_score(target, predic)\nprint('cross-predicted accuracy ', accuracy)\n\nmodel9_pred = model9.predict(newArray)\ndf9 = pd.DataFrame(model9_pred)\ndf9.columns = ['CLASS']\ndf9.index.name = 'Index'\ndf9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf9.to_csv('ilujumba9.csv')\nprint(df9['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df9.groupby('CLASS').size()[0].sum())\nprint(df9.groupby('CLASS').size()[1].sum())","execution_count":36,"outputs":[{"output_type":"stream","text":"mean for results 0.9101496004863645\ncross-predicted accuracy  0.6405529953917051\n['True' 'False']\n370\n388\n","name":"stdout"}]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}
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From 4305eb68df044e66f3f69590e8eb4b076b119122 Mon Sep 17 00:00:00 2001
From: Ibra Lujumba <ibra.lujumba@gmail.com>
Date: Wed, 11 Mar 2020 20:08:04 +0300
Subject: [PATCH 02/10] Notebook from Kaggle assignment

Visualised decision boundaries of 10 sklearn classifiers and their accuracies
---
 Assignment Colab/Ibra Lujumba_kaggle assignment final.ipynb | 1 +
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+{"cells":[{"metadata":{},"cell_type":"markdown","source":"## ACE_Uganda Kaggle competition 1\n### by Ibra Lujumba"},{"metadata":{},"cell_type":"markdown","source":"#### Exploratory Data Analysis "},{"metadata":{},"cell_type":"markdown","source":"##### Importing python modules for data analysis and visualization"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np # manipulation of arrays\nimport pandas as pd # manipulating dataframes\nimport matplotlib.pyplot as plt # data visualisation\nimport seaborn as sb # data visualisation,it is based on plt","execution_count":546,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#ignoring warnings that may arise\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":547,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"###### Importing the datasets"},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input/ace-class-assignment/\n\n# reading in the data\ndata = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\nnew = pd.read_csv('../input/ace-class-assignment/Test.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"##### Checking the dimensions of the data as well as the datatype of each column"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking dimensions of the datasets\ndata.shape, new.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the datatypes of the variables\ndata.dtypes, new.dtypes","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"All the values in all the variables exists as either floats or integers.\n\nProceeding to work with the training dataset to build the classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the descriptive statistics of the train dataset such as arithmetic mean, \n# standard deviation, quartiles and number of non-NA values in each column \ndata.describe()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"raw","source":"From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\nAS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\nFor all the variables, the data points are not widely spread"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the proprotions of classses\ndata.groupby('CLASS').size()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"raw","source":"Both classes have an equal number of entries"},{"metadata":{"trusted":true},"cell_type":"code","source":"# obtaining pairwise correlation values for the variables in the train dataset\n# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\npearsoncorr = data.corr(method='pearson')\n\n# visualizing the correlation matrix as a heatmap to make interpretation easier\nplt.figure(figsize=(10,10))\ntop_corr = pearsoncorr.index\nsb.heatmap(pearsoncorr, \n            xticklabels=pearsoncorr.columns,\n            yticklabels=pearsoncorr.columns,\n            cmap='RdYlGn',\n            annot=True,\n            linewidth=0.5)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."},{"metadata":{},"cell_type":"markdown","source":"You can get the p-values associated with the correlation values using the code below.\n\n`from scipy.stats import pearsonr`\n\n`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n\nIn this example, all values were too low to be informative"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using a scatter plot matrix to visualise correlations\n# plt.figure(figsize=(60,60))\n# sb.pairplot(data)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\nThese variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n\nVariables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"},{"metadata":{"trusted":true},"cell_type":"code","source":"len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n\n#this confirms that NT_EFC195 is a categorical variable","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the associated p-values. The value of 1 at the bottom should be ignored \nfrom scipy.stats import pearsonr\ndata.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the distribution and skewness of variables\nplt.figure(figsize=(10,6))\ndata.skew().plot(kind='bar')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.groupby('NT_EFC195').size() # majority of the instances are of Class 0.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n\nTranformation possibilities\n* using the minimum and maximum scaler\n* standardisation"},{"metadata":{},"cell_type":"markdown","source":"#### Data transformation"},{"metadata":{"trusted":true},"cell_type":"code","source":"# converting Pandas dataframe to ndArray\ndataArray = data.to_numpy()\n\n# seperating the predictor and response variables\ntarget = dataArray[:,11]\npredictors = dataArray[:,0:11]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using minMaxScaler to set all values between 0 and 1\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler(feature_range=(0,1))\nrescaledPredictors = scaler.fit_transform(predictors)\n                        \n\n# using StandardScaler\nfrom sklearn.preprocessing import StandardScaler\nscaler1 = StandardScaler().fit(predictors)\nstandardizedPredictors = scaler1.transform(predictors)\n                        \n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Feature selection"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n\n# untransformed data\nfrom sklearn.feature_selection import SelectKBest, f_classif\nbestFeatures = SelectKBest(score_func=f_classif, k=7)\nfit = bestFeatures.fit(predictors, target)\n\nscores = pd.DataFrame(fit.scores_) \npvalues = pd.DataFrame(fit.pvalues_)\ncolumns = pd.DataFrame(data.columns[0:11])\n\nfeatureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\nfeatureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n\nprint(featureValues.nlargest(7, 'score'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the transformed data\n\n# rescaledPredictors\nreBestFeatures = SelectKBest(score_func=f_classif, k=7)\nreFit = reBestFeatures.fit(rescaledPredictors, target)\n\nreScores = pd.DataFrame(reFit.scores_) \nrePvalues = pd.DataFrame(reFit.pvalues_)\nreColumns = pd.DataFrame(data.columns[0:11])\n\nreFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\nreFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n\n\n\n# standardizedPredictors\nstBestFeatures = SelectKBest(score_func=f_classif, k=7)\nstFit = stBestFeatures.fit(standardizedPredictors, target)\n\nstScores = pd.DataFrame(stFit.scores_) \nstPvalues = pd.DataFrame(stFit.pvalues_)\nstColumns = pd.DataFrame(data.columns[0:11])\n\nstFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\nstFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n\nprint(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using feature importance\nfrom sklearn.ensemble import ExtraTreesClassifier\nmodel = ExtraTreesClassifier()\nmodel.fit(predictors, target)\nprint(model.feature_importances_)\n\n# visualising feature importance\nimportances = pd.Series(model.feature_importances_, index=data.columns[0:11])\nimportances.nlargest(10).plot(kind='barh')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"#### Building the classification model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\nfrom sklearn.model_selection import train_test_split # random split\nfrom sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\np_train, p_test, t_train, t_test = train_test_split(predictors, target, \n                                                    test_size=0.30,random_state=42)\n\nlogit = LogisticRegression() # making instance of model\n\n# fitting the model on untransformed data\nlogit.fit(p_train, t_train)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Measuring model performance\n\nWe can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# predict on test data\npredictions = logit.predict(p_test) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# the confusion matrix\nfrom sklearn import metrics\ncm = metrics.confusion_matrix(t_test, predictions)\ncm\nsb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n          square=True, cmap='Blues') \nplt.ylabel('Actual label'); plt.xlabel('Predicted label')\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\nprint(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\nprint(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n                                                      # higher values are better\n# not affected by unbalanced classes\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ROC curve of true positive rate against false positive rate\n# shows tradeoff between sensitivy and specificity\n\npred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\nfpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\nauc = metrics.roc_auc_score(t_test, pred_probs)\nplt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\nplt.legend(loc=4)\nplt.ylabel('tpr'), plt.xlabel('fpr')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data\nnew_pred = logit.predict(new.values)\n\npred_df = pd.DataFrame(new_pred) \npred_df.columns=[\"CLASS\"]\npred_df.index.name=\"Index\" \npred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df.to_csv(\"ilujumba.csv\") \nprint(pred_df['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df.groupby('CLASS').size()[0].sum())\nprint(pred_df.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on rescaled data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit1 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit1.fit(p1_train, t1_train)\n\n# predict on test data\npredictions1 = logit1.predict(p1_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\nprint(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\nprint(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t1_test, predictions1))\n\n# rescaling new data\nnewArray = new.to_numpy()\nrescaledNew = scaler.fit_transform(newArray)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data (rescaled)\nnew_pred1 = logit1.predict(rescaledNew)\n\npred_df1 = pd.DataFrame(new_pred1) \npred_df1.columns=[\"CLASS\"]\npred_df1.index.name=\"Index\" \npred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df1.to_csv(\"ilujumba1.csv\") \nprint(pred_df1['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df1.groupby('CLASS').size()[0].sum())\nprint(pred_df1.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on standardized data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit2 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit2.fit(p2_train, t2_train)\n\n# predict on test data\npredictions2 = logit2.predict(p2_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\nprint(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\nprint(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\nprint('MCC: ',matthews_corrcoef(t2_test, predictions2))\n\n# standardizing new data\nstandardizedNew = scaler1.transform(newArray)\n\n# performance on new data (standaridized)\nnew_pred2 = logit2.predict(standardizedNew)\n\npred_df2 = pd.DataFrame(new_pred2) \npred_df2.columns=[\"CLASS\"]\npred_df2.index.name=\"Index\" \npred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df2.to_csv(\"ilujumba2.csv\") \nprint(pred_df2['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df2.groupby('CLASS').size()[0].sum())\nprint(pred_df2.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Using selected features, rescaled data and Logistic regression\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n                                                        test_size=0.30,random_state=42)\n\nlogit3 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit3.fit(p3_train, t3_train)\n\n# predict on test data\npredictions3 = logit3.predict(p3_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\nprint(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\nprint(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t3_test, predictions3))\n\n# rescaling new data\n# newArray = new.to_numpy()\n# rescaledNew = scaler.fit_transform(newArray)\n\n# performance on new data (rescaled)\nnew_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n\npred_df3 = pd.DataFrame(new_pred3) \npred_df3.columns=[\"CLASS\"]\npred_df3.index.name=\"Index\" \npred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df3.to_csv(\"ilujumba3.csv\") \nprint(pred_df3['CLASS'].unique())\n\n\n#printing the numbers of False and True\nprint(pred_df3.groupby('CLASS').size()[0].sum())\nprint(pred_df3.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Using cross-validation and Logistic Regression"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\n\nkfold = KFold(n_splits=10, random_state=42)\nmodel6 = LogisticRegression()\nmodel6.fit(predictors, target)\n\nresults = cross_val_score(model6, predictors, target)\nprint(results.mean())\n\nmodel6_pred = model6.predict(rescaledNew)\ndf6 = pd.DataFrame(model6_pred)\ndf6.columns = ['CLASS']\ndf6.index.name = 'Index'\ndf6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n\ndf6.to_csv('ilujumba7.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier with kfold cross-validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel7 = GaussianNB()\nmodel7.fit(predictors, target)\n\nresults = cross_val_score(model7, predictors, target)\nprint(results.mean())\n\nmodel7_pred = model7.predict(newArray)\ndf7 = pd.DataFrame(model7_pred)\ndf7.columns = ['CLASS']\ndf7.index.name = 'Index'\ndf7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf7.to_csv('ilujumba7.csv')\nprint(df7['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df7.groupby('CLASS').size()[0].sum())\nprint(df7.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier on rescaled features\n\nAssumes that all features are independent of each other and each feature contributes equally to the resulting class"},{"metadata":{"trusted":true},"cell_type":"code","source":"kfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel8 = GaussianNB()\nmodel8.fit(rescaledPredictors, target)\n\nresults1 = cross_val_score(model8, rescaledPredictors, target)\nprint(results1.mean())\n\nmodel8_pred = model8.predict(rescaledNew)\ndf8 = pd.DataFrame(model8_pred)\ndf8.columns = ['CLASS']\ndf8.index.name = 'Index'\ndf8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf8.to_csv('ilujumba8.csv')\nprint(df8['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df8.groupby('CLASS').size()[0].sum())\nprint(df8.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes and kfold validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import cross_val_predict\nfrom sklearn.naive_bayes import GaussianNB\n\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel9 = GaussianNB()\nmodel9.fit(predictors, target)\n\nresults = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\nprint('mean for results', results.mean())\n\npredic = cross_val_predict(model9, predictors, target, cv =10)\naccuracy = metrics.r2_score(target, predic)\nprint('cross-predicted accuracy ', accuracy)\n\nmodel9_pred = model9.predict(newArray)\ndf9 = pd.DataFrame(model9_pred)\ndf9.columns = ['CLASS']\ndf9.index.name = 'Index'\ndf9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf9.to_csv('ilujumba9.csv')\nprint(df9['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df9.groupby('CLASS').size()[0].sum())\nprint(df9.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Comparing several algorithms to look at the nature of the decision boundaries created"},{"metadata":{"trusted":true},"cell_type":"code","source":"#importing classifiers from the sklearn library\n\nfrom matplotlib.colors import ListedColormap\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neural_network import MLPClassifier #1\nfrom sklearn.neighbors import KNeighborsClassifier #2\nfrom sklearn.svm import SVC #3\nfrom sklearn.gaussian_process import GaussianProcessClassifier #4\nfrom sklearn.gaussian_process.kernels import RBF #5\nfrom sklearn.tree import DecisionTreeClassifier #6\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\nfrom sklearn.naive_bayes import GaussianNB #9\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\nfrom sklearn.linear_model import LogisticRegression #11\n\nnames = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n\nclassifiers = [\n    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n    DecisionTreeClassifier(),\n    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n    MLPClassifier(max_iter=1000), #iterations until converge\n    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n    GaussianNB(),\n    QuadraticDiscriminantAnalysis(),\n    LogisticRegression()]\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Dimensionality reduction\nhttps://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"},{"metadata":{"trusted":true},"cell_type":"code","source":"# preprocessing the dataset\ndataArray = data.to_numpy()\nX, y = dataArray[:,0:11], dataArray[0:,11]\nX = StandardScaler().fit_transform(X)\n\n# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\nfrom sklearn.decomposition import PCA\npca = PCA()\npca.fit_transform(X)\npca_variance = pca.explained_variance_\nplt.figure(figsize=(8, 6))\nplt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\nplt.legend()\nplt.ylabel('Variance ratio')\nplt.xlabel('Principal components')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\nninety_five = pca2.fit_transform(X)\nninety_five.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Eight features explain 95% of the variance in the dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"pca2 = PCA(3) # keeping features three principal components\nprincipalComponents = pca2.fit_transform(X)\n\nfrom mpl_toolkits.mplot3d import Axes3D\nplt.figure(figsize=(10,6))\nax = plt.axes(projection='3d')\nax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n           linewidths=1, alpha=.5,\n           edgecolor='k', s= 200,\n           c=data['CLASS'])\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"principalComponents.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\nfinalDf = pd.concat([principalDf, data['CLASS']], axis = 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"finalDf.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Visualising the top 2 principal components"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = plt.figure(figsize = (6,6))\nax = fig.add_subplot(111) \nax.set(xlim=(-10,10), ylim=(-10,10))\nax.set_xlabel('Principal Component 1', fontsize = 15)\nax.set_ylabel('Principal Component 2', fontsize = 15)\nax.set_title('top 2 components', fontsize = 20)\n\ntargets = [0, 1]\ncolors = ['r', 'g']\n\nfor target, color in zip(targets,colors):\n    indices = finalDf['CLASS'] == target\n    ax.scatter(finalDf.loc[indices, 'PC1']\n               , finalDf.loc[indices, 'PC2']\n               , c = color\n               , s = 50)\nax.legend(targets)\nax.grid()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# splitting the into training and test part\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# creating mesh for the contour plot\n\nh = .02  # step size in the mesh\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pca4 = PCA(n_components=2)\n\n# applying PCA on training set\npca4.fit(X_train)\n\n#applying transform on training and testing sets\ntrain_ = pca4.transform(X_train)\ntest_ = pca4.transform(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_.shape, test_.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"figure = plt.figure(figsize=(27, 15))\ni = 1\n\ndatasets=[data]\n\n# just plot the dataset first\ncm = plt.cm.RdBu\ncm_bright = ListedColormap(['#FF0000', '#0000FF'])\nax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n\nif ds_cnt == 0:\n    ax.set_title(\"Input data\")\n    # Plot the top 2 principal components for training data\n    ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n                edgecolors='k')\n    # Plot the top 2 principal components for the testing data\n    ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n                edgecolors='k')\n    ax.set_xlim(xx.min(), xx.max())\n    ax.set_ylim(yy.min(), yy.max())\n    ax.set_xticks(())\n    ax.set_yticks(())\n    i += 1\n\n    # iterate over classifiers\n\nfor name, clf in zip(names, classifiers):\n    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n    clf.fit(train_, y_train)\n    score = clf.score(test_, y_test)\n\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n        \n    if hasattr(clf, \"decision_function\"):\n        Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n    else:\n        Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n    \n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n\n    # Plot the training points\n    ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n    # Plot the testing points\n    ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.6)\n\n    ax.set_xlim(xx.min(), xx.max())\n    ax.set_ylim(yy.min(), yy.max())\n    ax.set_xticks(())\n    ax.set_yticks(())\n    if ds_cnt == 0:\n        ax.set_title(name)\n        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n        i += 1\n\nplt.tight_layout()\nplt.show()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}
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From: Ibra Lujumba <ibra.lujumba@gmail.com>
Date: Thu, 12 Mar 2020 10:50:53 +0300
Subject: [PATCH 03/10] Deleted previous notebooks with errors

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-{"cells":[{"metadata":{},"cell_type":"markdown","source":"## ACE_Uganda Kaggle competition 1\n### by Ibra Lujumba"},{"metadata":{},"cell_type":"markdown","source":"#### Exploratory Data Analysis "},{"metadata":{},"cell_type":"markdown","source":"##### Importing python modules for data analysis and visualization"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np # manipulation of arrays\nimport pandas as pd # manipulating dataframes\nimport matplotlib.pyplot as plt # data visualisation\nimport seaborn as sb # data visualisation,it is based on plt","execution_count":546,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#ignoring warnings that may arise\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":547,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"###### Importing the datasets"},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input/ace-class-assignment/\n\n# reading in the data\ndata = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\nnew = pd.read_csv('../input/ace-class-assignment/Test.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"##### Checking the dimensions of the data as well as the datatype of each column"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking dimensions of the datasets\ndata.shape, new.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the datatypes of the variables\ndata.dtypes, new.dtypes","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"All the values in all the variables exists as either floats or integers.\n\nProceeding to work with the training dataset to build the classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the descriptive statistics of the train dataset such as arithmetic mean, \n# standard deviation, quartiles and number of non-NA values in each column \ndata.describe()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"raw","source":"From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\nAS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\nFor all the variables, the data points are not widely spread"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the proprotions of classses\ndata.groupby('CLASS').size()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"raw","source":"Both classes have an equal number of entries"},{"metadata":{"trusted":true},"cell_type":"code","source":"# obtaining pairwise correlation values for the variables in the train dataset\n# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\npearsoncorr = data.corr(method='pearson')\n\n# visualizing the correlation matrix as a heatmap to make interpretation easier\nplt.figure(figsize=(10,10))\ntop_corr = pearsoncorr.index\nsb.heatmap(pearsoncorr, \n            xticklabels=pearsoncorr.columns,\n            yticklabels=pearsoncorr.columns,\n            cmap='RdYlGn',\n            annot=True,\n            linewidth=0.5)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."},{"metadata":{},"cell_type":"markdown","source":"You can get the p-values associated with the correlation values using the code below.\n\n`from scipy.stats import pearsonr`\n\n`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n\nIn this example, all values were too low to be informative"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using a scatter plot matrix to visualise correlations\n# plt.figure(figsize=(60,60))\n# sb.pairplot(data)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\nThese variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n\nVariables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"},{"metadata":{"trusted":true},"cell_type":"code","source":"len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n\n#this confirms that NT_EFC195 is a categorical variable","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the associated p-values. The value of 1 at the bottom should be ignored \nfrom scipy.stats import pearsonr\ndata.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the distribution and skewness of variables\nplt.figure(figsize=(10,6))\ndata.skew().plot(kind='bar')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.groupby('NT_EFC195').size() # majority of the instances are of Class 0.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n\nTranformation possibilities\n* using the minimum and maximum scaler\n* standardisation"},{"metadata":{},"cell_type":"markdown","source":"#### Data transformation"},{"metadata":{"trusted":true},"cell_type":"code","source":"# converting Pandas dataframe to ndArray\ndataArray = data.to_numpy()\n\n# seperating the predictor and response variables\ntarget = dataArray[:,11]\npredictors = dataArray[:,0:11]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using minMaxScaler to set all values between 0 and 1\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler(feature_range=(0,1))\nrescaledPredictors = scaler.fit_transform(predictors)\n                        \n\n# using StandardScaler\nfrom sklearn.preprocessing import StandardScaler\nscaler1 = StandardScaler().fit(predictors)\nstandardizedPredictors = scaler1.transform(predictors)\n                        \n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Feature selection"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n\n# untransformed data\nfrom sklearn.feature_selection import SelectKBest, f_classif\nbestFeatures = SelectKBest(score_func=f_classif, k=7)\nfit = bestFeatures.fit(predictors, target)\n\nscores = pd.DataFrame(fit.scores_) \npvalues = pd.DataFrame(fit.pvalues_)\ncolumns = pd.DataFrame(data.columns[0:11])\n\nfeatureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\nfeatureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n\nprint(featureValues.nlargest(7, 'score'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the transformed data\n\n# rescaledPredictors\nreBestFeatures = SelectKBest(score_func=f_classif, k=7)\nreFit = reBestFeatures.fit(rescaledPredictors, target)\n\nreScores = pd.DataFrame(reFit.scores_) \nrePvalues = pd.DataFrame(reFit.pvalues_)\nreColumns = pd.DataFrame(data.columns[0:11])\n\nreFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\nreFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n\n\n\n# standardizedPredictors\nstBestFeatures = SelectKBest(score_func=f_classif, k=7)\nstFit = stBestFeatures.fit(standardizedPredictors, target)\n\nstScores = pd.DataFrame(stFit.scores_) \nstPvalues = pd.DataFrame(stFit.pvalues_)\nstColumns = pd.DataFrame(data.columns[0:11])\n\nstFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\nstFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n\nprint(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using feature importance\nfrom sklearn.ensemble import ExtraTreesClassifier\nmodel = ExtraTreesClassifier()\nmodel.fit(predictors, target)\nprint(model.feature_importances_)\n\n# visualising feature importance\nimportances = pd.Series(model.feature_importances_, index=data.columns[0:11])\nimportances.nlargest(10).plot(kind='barh')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"#### Building the classification model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\nfrom sklearn.model_selection import train_test_split # random split\nfrom sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\np_train, p_test, t_train, t_test = train_test_split(predictors, target, \n                                                    test_size=0.30,random_state=42)\n\nlogit = LogisticRegression() # making instance of model\n\n# fitting the model on untransformed data\nlogit.fit(p_train, t_train)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Measuring model performance\n\nWe can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# predict on test data\npredictions = logit.predict(p_test) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# the confusion matrix\nfrom sklearn import metrics\ncm = metrics.confusion_matrix(t_test, predictions)\ncm\nsb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n          square=True, cmap='Blues') \nplt.ylabel('Actual label'); plt.xlabel('Predicted label')\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\nprint(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\nprint(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n                                                      # higher values are better\n# not affected by unbalanced classes\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ROC curve of true positive rate against false positive rate\n# shows tradeoff between sensitivy and specificity\n\npred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\nfpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\nauc = metrics.roc_auc_score(t_test, pred_probs)\nplt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\nplt.legend(loc=4)\nplt.ylabel('tpr'), plt.xlabel('fpr')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data\nnew_pred = logit.predict(new.values)\n\npred_df = pd.DataFrame(new_pred) \npred_df.columns=[\"CLASS\"]\npred_df.index.name=\"Index\" \npred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df.to_csv(\"ilujumba.csv\") \nprint(pred_df['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df.groupby('CLASS').size()[0].sum())\nprint(pred_df.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on rescaled data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit1 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit1.fit(p1_train, t1_train)\n\n# predict on test data\npredictions1 = logit1.predict(p1_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\nprint(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\nprint(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t1_test, predictions1))\n\n# rescaling new data\nnewArray = new.to_numpy()\nrescaledNew = scaler.fit_transform(newArray)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data (rescaled)\nnew_pred1 = logit1.predict(rescaledNew)\n\npred_df1 = pd.DataFrame(new_pred1) \npred_df1.columns=[\"CLASS\"]\npred_df1.index.name=\"Index\" \npred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df1.to_csv(\"ilujumba1.csv\") \nprint(pred_df1['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df1.groupby('CLASS').size()[0].sum())\nprint(pred_df1.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on standardized data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit2 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit2.fit(p2_train, t2_train)\n\n# predict on test data\npredictions2 = logit2.predict(p2_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\nprint(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\nprint(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\nprint('MCC: ',matthews_corrcoef(t2_test, predictions2))\n\n# standardizing new data\nstandardizedNew = scaler1.transform(newArray)\n\n# performance on new data (standaridized)\nnew_pred2 = logit2.predict(standardizedNew)\n\npred_df2 = pd.DataFrame(new_pred2) \npred_df2.columns=[\"CLASS\"]\npred_df2.index.name=\"Index\" \npred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df2.to_csv(\"ilujumba2.csv\") \nprint(pred_df2['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df2.groupby('CLASS').size()[0].sum())\nprint(pred_df2.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Using selected features, rescaled data and Logistic regression\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n                                                        test_size=0.30,random_state=42)\n\nlogit3 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit3.fit(p3_train, t3_train)\n\n# predict on test data\npredictions3 = logit3.predict(p3_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\nprint(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\nprint(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t3_test, predictions3))\n\n# rescaling new data\n# newArray = new.to_numpy()\n# rescaledNew = scaler.fit_transform(newArray)\n\n# performance on new data (rescaled)\nnew_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n\npred_df3 = pd.DataFrame(new_pred3) \npred_df3.columns=[\"CLASS\"]\npred_df3.index.name=\"Index\" \npred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df3.to_csv(\"ilujumba3.csv\") \nprint(pred_df3['CLASS'].unique())\n\n\n#printing the numbers of False and True\nprint(pred_df3.groupby('CLASS').size()[0].sum())\nprint(pred_df3.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Using cross-validation and Logistic Regression"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\n\nkfold = KFold(n_splits=10, random_state=42)\nmodel6 = LogisticRegression()\nmodel6.fit(predictors, target)\n\nresults = cross_val_score(model6, predictors, target)\nprint(results.mean())\n\nmodel6_pred = model6.predict(rescaledNew)\ndf6 = pd.DataFrame(model6_pred)\ndf6.columns = ['CLASS']\ndf6.index.name = 'Index'\ndf6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n\ndf6.to_csv('ilujumba7.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier with kfold cross-validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel7 = GaussianNB()\nmodel7.fit(predictors, target)\n\nresults = cross_val_score(model7, predictors, target)\nprint(results.mean())\n\nmodel7_pred = model7.predict(newArray)\ndf7 = pd.DataFrame(model7_pred)\ndf7.columns = ['CLASS']\ndf7.index.name = 'Index'\ndf7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf7.to_csv('ilujumba7.csv')\nprint(df7['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df7.groupby('CLASS').size()[0].sum())\nprint(df7.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier on rescaled features\n\nAssumes that all features are independent of each other and each feature contributes equally to the resulting class"},{"metadata":{"trusted":true},"cell_type":"code","source":"kfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel8 = GaussianNB()\nmodel8.fit(rescaledPredictors, target)\n\nresults1 = cross_val_score(model8, rescaledPredictors, target)\nprint(results1.mean())\n\nmodel8_pred = model8.predict(rescaledNew)\ndf8 = pd.DataFrame(model8_pred)\ndf8.columns = ['CLASS']\ndf8.index.name = 'Index'\ndf8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf8.to_csv('ilujumba8.csv')\nprint(df8['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df8.groupby('CLASS').size()[0].sum())\nprint(df8.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes and kfold validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import cross_val_predict\nfrom sklearn.naive_bayes import GaussianNB\n\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel9 = GaussianNB()\nmodel9.fit(predictors, target)\n\nresults = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\nprint('mean for results', results.mean())\n\npredic = cross_val_predict(model9, predictors, target, cv =10)\naccuracy = metrics.r2_score(target, predic)\nprint('cross-predicted accuracy ', accuracy)\n\nmodel9_pred = model9.predict(newArray)\ndf9 = pd.DataFrame(model9_pred)\ndf9.columns = ['CLASS']\ndf9.index.name = 'Index'\ndf9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf9.to_csv('ilujumba9.csv')\nprint(df9['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df9.groupby('CLASS').size()[0].sum())\nprint(df9.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Comparing several algorithms to look at the nature of the decision boundaries created"},{"metadata":{"trusted":true},"cell_type":"code","source":"#importing classifiers from the sklearn library\n\nfrom matplotlib.colors import ListedColormap\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neural_network import MLPClassifier #1\nfrom sklearn.neighbors import KNeighborsClassifier #2\nfrom sklearn.svm import SVC #3\nfrom sklearn.gaussian_process import GaussianProcessClassifier #4\nfrom sklearn.gaussian_process.kernels import RBF #5\nfrom sklearn.tree import DecisionTreeClassifier #6\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\nfrom sklearn.naive_bayes import GaussianNB #9\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\nfrom sklearn.linear_model import LogisticRegression #11\n\nnames = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n\nclassifiers = [\n    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n    DecisionTreeClassifier(),\n    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n    MLPClassifier(max_iter=1000), #iterations until converge\n    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n    GaussianNB(),\n    QuadraticDiscriminantAnalysis(),\n    LogisticRegression()]\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Dimensionality reduction\nhttps://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"},{"metadata":{"trusted":true},"cell_type":"code","source":"# preprocessing the dataset\ndataArray = data.to_numpy()\nX, y = dataArray[:,0:11], dataArray[0:,11]\nX = StandardScaler().fit_transform(X)\n\n# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\nfrom sklearn.decomposition import PCA\npca = PCA()\npca.fit_transform(X)\npca_variance = pca.explained_variance_\nplt.figure(figsize=(8, 6))\nplt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\nplt.legend()\nplt.ylabel('Variance ratio')\nplt.xlabel('Principal components')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\nninety_five = pca2.fit_transform(X)\nninety_five.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Eight features explain 95% of the variance in the dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"pca2 = PCA(3) # keeping features three principal components\nprincipalComponents = pca2.fit_transform(X)\n\nfrom mpl_toolkits.mplot3d import Axes3D\nplt.figure(figsize=(10,6))\nax = plt.axes(projection='3d')\nax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n           linewidths=1, alpha=.5,\n           edgecolor='k', s= 200,\n           c=data['CLASS'])\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"principalComponents.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\nfinalDf = pd.concat([principalDf, data['CLASS']], axis = 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"finalDf.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Visualising the top 2 principal components"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = plt.figure(figsize = (6,6))\nax = fig.add_subplot(111) \nax.set(xlim=(-10,10), ylim=(-10,10))\nax.set_xlabel('Principal Component 1', fontsize = 15)\nax.set_ylabel('Principal Component 2', fontsize = 15)\nax.set_title('top 2 components', fontsize = 20)\n\ntargets = [0, 1]\ncolors = ['r', 'g']\n\nfor target, color in zip(targets,colors):\n    indices = finalDf['CLASS'] == target\n    ax.scatter(finalDf.loc[indices, 'PC1']\n               , finalDf.loc[indices, 'PC2']\n               , c = color\n               , s = 50)\nax.legend(targets)\nax.grid()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# splitting the into training and test part\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# creating mesh for the contour plot\n\nh = .02  # step size in the mesh\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pca4 = PCA(n_components=2)\n\n# applying PCA on training set\npca4.fit(X_train)\n\n#applying transform on training and testing sets\ntrain_ = pca4.transform(X_train)\ntest_ = pca4.transform(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_.shape, test_.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"figure = plt.figure(figsize=(27, 15))\ni = 1\n\ndatasets=[data]\n\n# just plot the dataset first\ncm = plt.cm.RdBu\ncm_bright = ListedColormap(['#FF0000', '#0000FF'])\nax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n\nif ds_cnt == 0:\n    ax.set_title(\"Input data\")\n    # Plot the top 2 principal components for training data\n    ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n                edgecolors='k')\n    # Plot the top 2 principal components for the testing data\n    ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n                edgecolors='k')\n    ax.set_xlim(xx.min(), xx.max())\n    ax.set_ylim(yy.min(), yy.max())\n    ax.set_xticks(())\n    ax.set_yticks(())\n    i += 1\n\n    # iterate over classifiers\n\nfor name, clf in zip(names, classifiers):\n    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n    clf.fit(train_, y_train)\n    score = clf.score(test_, y_test)\n\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n        \n    if hasattr(clf, \"decision_function\"):\n        Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n    else:\n        Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n    \n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n\n    # Plot the training points\n    ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n    # Plot the testing points\n    ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.6)\n\n    ax.set_xlim(xx.min(), xx.max())\n    ax.set_ylim(yy.min(), yy.max())\n    ax.set_xticks(())\n    ax.set_yticks(())\n    if ds_cnt == 0:\n        ax.set_title(name)\n        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n        i += 1\n\nplt.tight_layout()\nplt.show()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}
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-{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Exploratory Data Analysis "},{"metadata":{},"cell_type":"markdown","source":"### Importing python modules for data analysis and visualization"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np # manipulation of arrays\nimport pandas as pd # manipulating dataframes\nimport matplotlib.pyplot as plt # data visualisation\nimport seaborn as sb # data visualisation,it is based on plt","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#ignoring warnings that may arise\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":4,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Importing the datasets"},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input/ace-class-assignment/\n\n# reading in the data\ndata = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\nnew = pd.read_csv('../input/ace-class-assignment/Test.csv')","execution_count":5,"outputs":[{"output_type":"stream","text":"AMP_TrainSet.csv  Test.csv\r\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Checking the dimensions of the data as well as the datatype of each column"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking dimensions of the datasets\ndata.shape, new.shape","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"((3038, 12), (758, 11))"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the datatypes of the variables\ndata.dtypes, new.dtypes","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"(FULL_Charge           float64\n FULL_AcidicMolPerc    float64\n FULL_AURR980107       float64\n FULL_DAYM780201       float64\n FULL_GEOR030101       float64\n FULL_OOBM850104       float64\n NT_EFC195               int64\n AS_MeanAmphiMoment    float64\n AS_DAYM780201         float64\n AS_FUKS010112         float64\n CT_RACS820104         float64\n CLASS                   int64\n dtype: object,\n FULL_Charge           float64\n FULL_AcidicMolPerc    float64\n FULL_AURR980107       float64\n FULL_DAYM780201       float64\n FULL_GEOR030101       float64\n FULL_OOBM850104       float64\n NT_EFC195               int64\n AS_MeanAmphiMoment    float64\n AS_DAYM780201         float64\n AS_FUKS010112         float64\n CT_RACS820104         float64\n dtype: object)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"All the values in all the variables exists as either floats or integers.\n\nProceeding to work with the training dataset to build the classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the descriptive statistics of the train dataset such as arithmetic mean, \n# standard deviation, quartiles and number of non-NA values in each column \ndata.describe()","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\ncount  3038.000000         3038.000000      3038.000000      3038.000000   \nmean      2.060237            8.521520         0.971410        73.668760   \nstd       3.819929            7.586652         0.107413         8.527489   \nmin     -16.000000            0.000000         0.684000        42.750000   \n25%       0.000000            2.516000         0.895000        68.294000   \n50%       2.000000            7.143000         0.963000        74.059500   \n75%       4.000000           13.158000         1.041000        79.343750   \nmax      30.000000           46.667000         1.451000       101.682000   \n\n       FULL_GEOR030101  FULL_OOBM850104    NT_EFC195  AS_MeanAmphiMoment  \\\ncount      3038.000000      3038.000000  3038.000000         3038.000000   \nmean          0.994007        -2.432927     0.088545           15.683233   \nstd           0.031333         1.707223     0.284133           11.575665   \nmin           0.866000       -10.432000     0.000000            0.041000   \n25%           0.974000        -3.606000     0.000000            5.587500   \n50%           0.994000        -2.296500     0.000000           14.988500   \n75%           1.011000        -1.283250     0.000000           26.807750   \nmax           1.196000         3.576000     1.000000           51.280000   \n\n       AS_DAYM780201  AS_FUKS010112  CT_RACS820104        CLASS  \ncount    3038.000000    3038.000000    3038.000000  3038.000000  \nmean       73.650828       5.911361       1.235255     0.500000  \nstd         9.166092       0.693689       0.210012     0.500082  \nmin        42.778000       3.533000       0.785000     0.000000  \n25%        67.556000       5.459250       1.082000     0.000000  \n50%        73.697000       5.925500       1.184000     0.500000  \n75%        79.778000       6.382000       1.351000     1.000000  \nmax       103.167000       8.662000       2.192000     1.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FULL_Charge</th>\n      <th>FULL_AcidicMolPerc</th>\n      <th>FULL_AURR980107</th>\n      <th>FULL_DAYM780201</th>\n      <th>FULL_GEOR030101</th>\n      <th>FULL_OOBM850104</th>\n      <th>NT_EFC195</th>\n      <th>AS_MeanAmphiMoment</th>\n      <th>AS_DAYM780201</th>\n      <th>AS_FUKS010112</th>\n      <th>CT_RACS820104</th>\n      <th>CLASS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n      <td>3038.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2.060237</td>\n      <td>8.521520</td>\n      <td>0.971410</td>\n      <td>73.668760</td>\n      <td>0.994007</td>\n      <td>-2.432927</td>\n      <td>0.088545</td>\n      <td>15.683233</td>\n      <td>73.650828</td>\n      <td>5.911361</td>\n      <td>1.235255</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.819929</td>\n      <td>7.586652</td>\n      <td>0.107413</td>\n      <td>8.527489</td>\n      <td>0.031333</td>\n      <td>1.707223</td>\n      <td>0.284133</td>\n      <td>11.575665</td>\n      <td>9.166092</td>\n      <td>0.693689</td>\n      <td>0.210012</td>\n      <td>0.500082</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-16.000000</td>\n      <td>0.000000</td>\n      <td>0.684000</td>\n      <td>42.750000</td>\n      <td>0.866000</td>\n      <td>-10.432000</td>\n      <td>0.000000</td>\n      <td>0.041000</td>\n      <td>42.778000</td>\n      <td>3.533000</td>\n      <td>0.785000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>2.516000</td>\n      <td>0.895000</td>\n      <td>68.294000</td>\n      <td>0.974000</td>\n      <td>-3.606000</td>\n      <td>0.000000</td>\n      <td>5.587500</td>\n      <td>67.556000</td>\n      <td>5.459250</td>\n      <td>1.082000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2.000000</td>\n      <td>7.143000</td>\n      <td>0.963000</td>\n      <td>74.059500</td>\n      <td>0.994000</td>\n      <td>-2.296500</td>\n      <td>0.000000</td>\n      <td>14.988500</td>\n      <td>73.697000</td>\n      <td>5.925500</td>\n      <td>1.184000</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>4.000000</td>\n      <td>13.158000</td>\n      <td>1.041000</td>\n      <td>79.343750</td>\n      <td>1.011000</td>\n      <td>-1.283250</td>\n      <td>0.000000</td>\n      <td>26.807750</td>\n      <td>79.778000</td>\n      <td>6.382000</td>\n      <td>1.351000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>30.000000</td>\n      <td>46.667000</td>\n      <td>1.451000</td>\n      <td>101.682000</td>\n      <td>1.196000</td>\n      <td>3.576000</td>\n      <td>1.000000</td>\n      <td>51.280000</td>\n      <td>103.167000</td>\n      <td>8.662000</td>\n      <td>2.192000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"raw","source":"From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\nAS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\nFor all the variables, the data points are not widely spread"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the proprotions of classses\ndata.groupby('CLASS').size()","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"CLASS\n0    1519\n1    1519\ndtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"raw","source":"Both classes have an equal number of entries"},{"metadata":{"trusted":true},"cell_type":"code","source":"# obtaining pairwise correlation values for the variables in the train dataset\n# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\npearsoncorr = data.corr(method='pearson')\n\n# visualizing the correlation matrix as a heatmap to make interpretation easier\nplt.figure(figsize=(10,10))\ntop_corr = pearsoncorr.index\nsb.heatmap(pearsoncorr, \n            xticklabels=pearsoncorr.columns,\n            yticklabels=pearsoncorr.columns,\n            cmap='RdYlGn',\n            annot=True,\n            linewidth=0.5)","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7fca1aa3f2e8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x720 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."},{"metadata":{},"cell_type":"markdown","source":"You can get the p-values associated with the correlation values using the code below.\n\n`from scipy.stats import pearsonr`\n\n`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n\nIn this example, all values were too low to be informative"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using a scatter plot matrix to visualise correlations\n# plt.figure(figsize=(60,60))\n# sb.pairplot(data)","execution_count":11,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\nThese variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n\nVariables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"},{"metadata":{"trusted":true},"cell_type":"code","source":"len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n\n#this confirms that NT_EFC195 is a categorical variable","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"(2830, array([0, 1]))"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"   CLASS  NT_EFC195\n0      1          0\n1      1          1\n2      1          0\n3      1          0\n4      1          0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CLASS</th>\n      <th>NT_EFC195</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the associated p-values. The value of 1 at the bottom should be ignored \nfrom scipy.stats import pearsonr\ndata.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"FULL_Charge           3.404241e-224\nFULL_AcidicMolPerc    4.220004e-295\nFULL_AURR980107       1.887905e-277\nFULL_DAYM780201       6.997934e-245\nFULL_GEOR030101        2.669597e-48\nFULL_OOBM850104       7.441087e-154\nNT_EFC195              2.189203e-48\nAS_MeanAmphiMoment     0.000000e+00\nAS_DAYM780201         4.911002e-142\nAS_FUKS010112          6.540694e-02\nCT_RACS820104          5.329249e-51\nCLASS                  1.000000e+00\nName: CLASS, dtype: float64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the distribution and skewness of variables\nplt.figure(figsize=(10,6))\ndata.skew().plot(kind='bar')","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7fca0f8a75f8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x432 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.groupby('NT_EFC195').size() # majority of the instances are of Class 0.","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"NT_EFC195\n0    2769\n1     269\ndtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fca133b2be0>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca133dbe48>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca1338c2e8>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fca13334748>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca132deba8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca1330f048>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fca132b74a8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca132dd940>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca132dd978>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fca132900b8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca0deb3518>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fca0de5b978>]],\n      dtype=object)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x720 with 12 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n\nTranformation possibilities\n* using the minimum and maximum scaler\n* standardisation"},{"metadata":{},"cell_type":"markdown","source":"### Data transformation"},{"metadata":{"trusted":true},"cell_type":"code","source":"# converting Pandas dataframe to ndArray\ndataArray = data.to_numpy()\n\n# seperating the predictor and response variables\ntarget = dataArray[:,11]\npredictors = dataArray[:,0:11]","execution_count":18,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using minMaxScaler to set all values between 0 and 1\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler(feature_range=(0,1))\nrescaledPredictors = scaler.fit_transform(predictors)\n                        \n\n# using StandardScaler\nfrom sklearn.preprocessing import StandardScaler\nscaler1 = StandardScaler().fit(predictors)\nstandardizedPredictors = scaler1.transform(predictors)\n                        \n","execution_count":19,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Feature selection"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n\n# untransformed data\nfrom sklearn.feature_selection import SelectKBest, f_classif\nbestFeatures = SelectKBest(score_func=f_classif, k=7)\nfit = bestFeatures.fit(predictors, target)\n\nscores = pd.DataFrame(fit.scores_) \npvalues = pd.DataFrame(fit.pvalues_)\ncolumns = pd.DataFrame(data.columns[0:11])\n\nfeatureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\nfeatureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n\nprint(featureValues.nlargest(7, 'score'))","execution_count":20,"outputs":[{"output_type":"stream","text":"            predictor        score         pvalue\n7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n2     FULL_AURR980107  1572.280677  1.887905e-277\n3     FULL_DAYM780201  1350.300743  6.997934e-245\n0         FULL_Charge  1214.902838  3.404241e-224\n5     FULL_OOBM850104   785.124798  7.441087e-154\n8       AS_DAYM780201   717.317408  4.911002e-142\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the transformed data\n\n# rescaledPredictors\nreBestFeatures = SelectKBest(score_func=f_classif, k=7)\nreFit = reBestFeatures.fit(rescaledPredictors, target)\n\nreScores = pd.DataFrame(reFit.scores_) \nrePvalues = pd.DataFrame(reFit.pvalues_)\nreColumns = pd.DataFrame(data.columns[0:11])\n\nreFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\nreFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n\n\n\n# standardizedPredictors\nstBestFeatures = SelectKBest(score_func=f_classif, k=7)\nstFit = stBestFeatures.fit(standardizedPredictors, target)\n\nstScores = pd.DataFrame(stFit.scores_) \nstPvalues = pd.DataFrame(stFit.pvalues_)\nstColumns = pd.DataFrame(data.columns[0:11])\n\nstFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\nstFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n\nprint(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))","execution_count":21,"outputs":[{"output_type":"stream","text":"         re_predictor     re_score      re_pvalue\n7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n2     FULL_AURR980107  1572.280677  1.887905e-277\n3     FULL_DAYM780201  1350.300743  6.997934e-245\n0         FULL_Charge  1214.902838  3.404241e-224\n5     FULL_OOBM850104   785.124798  7.441087e-154\n8       AS_DAYM780201   717.317408  4.911002e-142\n         st_predictor     st_score      st_pvalue\n7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n2     FULL_AURR980107  1572.280677  1.887905e-277\n3     FULL_DAYM780201  1350.300743  6.997934e-245\n0         FULL_Charge  1214.902838  3.404241e-224\n5     FULL_OOBM850104   785.124798  7.441087e-154\n8       AS_DAYM780201   717.317408  4.911002e-142\n","name":"stdout"},{"output_type":"execute_result","execution_count":21,"data":{"text/plain":"(None, None)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using feature importance\nfrom sklearn.ensemble import ExtraTreesClassifier\nmodel = ExtraTreesClassifier()\nmodel.fit(predictors, target)\nprint(model.feature_importances_)\n\n# visualising feature importance\nimportances = pd.Series(model.feature_importances_, index=data.columns[0:11])\nimportances.nlargest(10).plot(kind='barh')\nplt.show()","execution_count":22,"outputs":[{"output_type":"stream","text":"[0.07902812 0.14524468 0.09617523 0.09684783 0.05136757 0.07509526\n 0.03643717 0.28509498 0.05087749 0.03093695 0.05289472]\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"### Building the classification model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\nfrom sklearn.model_selection import train_test_split # random split\nfrom sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\np_train, p_test, t_train, t_test = train_test_split(predictors, target, \n                                                    test_size=0.30,random_state=42)\n\nlogit = LogisticRegression() # making instance of model\n\n# fitting the model on untransformed data\nlogit.fit(p_train, t_train)","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n                   multi_class='auto', n_jobs=None, penalty='l2',\n                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n                   warm_start=False)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Measuring model performance\n\nWe can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# predict on test data\npredictions = logit.predict(p_test) ","execution_count":24,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# the confusion matrix\nfrom sklearn import metrics\ncm = metrics.confusion_matrix(t_test, predictions)\ncm\nsb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n          square=True, cmap='Blues') \nplt.ylabel('Actual label'); plt.xlabel('Predicted label')\n","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"Text(0.5, 15.0, 'Predicted label')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\nprint(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\nprint(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, higher values are better\n# not affected by unbalanced classes\n\n\n","execution_count":26,"outputs":[{"output_type":"stream","text":"Accuracy:  91.77631578947368\nPrecision:  92.82608695652173\nRecall:  91.04477611940298\nMCC:  0.8356480321235562\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ROC curve of true positive rate against false positive rate\n# shows tradeoff between sensitivy and specificity\n\npred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\nfpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\nauc = metrics.roc_auc_score(t_test, pred_probs)\nplt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\nplt.legend(loc=4)\nplt.ylabel('tpr'), plt.xlabel('fpr')\nplt.show()","execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data\nnew_pred = logit.predict(new.values)\n\npred_df = pd.DataFrame(new_pred) \npred_df.columns=[\"CLASS\"]\npred_df.index.name=\"Index\" \npred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df.to_csv(\"ilujumba.csv\") \nprint(pred_df['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df.groupby('CLASS').size()[0].sum())\nprint(pred_df.groupby('CLASS').size()[1].sum())","execution_count":28,"outputs":[{"output_type":"stream","text":"['False' 'True']\n386\n372\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on scaled data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit1 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit1.fit(p1_train, t1_train)\n\n# predict on test data\npredictions1 = logit1.predict(p1_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\nprint(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\nprint(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t1_test, predictions1))\n\n# rescaling new data\nnewArray = new.to_numpy()\nrescaledNew = scaler.fit_transform(newArray)\n","execution_count":29,"outputs":[{"output_type":"stream","text":"Accuracy:  91.66666666666666\nPrecision:  92.81045751633987\nRecall:  90.8315565031983\nMCC:  0.8335025900424667\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data (rescaled)\nnew_pred1 = logit1.predict(rescaledNew)\n\npred_df1 = pd.DataFrame(new_pred1) \npred_df1.columns=[\"CLASS\"]\npred_df1.index.name=\"Index\" \npred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df1.to_csv(\"ilujumba1.csv\") \nprint(pred_df1['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df1.groupby('CLASS').size()[0].sum())\nprint(pred_df1.groupby('CLASS').size()[1].sum())","execution_count":30,"outputs":[{"output_type":"stream","text":"['False' 'True']\n378\n380\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on standardized data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit2 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit2.fit(p2_train, t2_train)\n\n# predict on test data\npredictions2 = logit2.predict(p2_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\nprint(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\nprint(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\nprint('MCC: ',matthews_corrcoef(t2_test, predictions2))\n\n# standardizing new data\nstandardizedNew = scaler1.transform(newArray)\n\n# performance on new data (standaridized)\nnew_pred2 = logit2.predict(standardizedNew)\n\npred_df2 = pd.DataFrame(new_pred2) \npred_df2.columns=[\"CLASS\"]\npred_df2.index.name=\"Index\" \npred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df2.to_csv(\"ilujumba2.csv\") \nprint(pred_df2['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df2.groupby('CLASS').size()[0].sum())\nprint(pred_df2.groupby('CLASS').size()[1].sum())","execution_count":31,"outputs":[{"output_type":"stream","text":"Accuracy:  91.8859649122807\nPrecision:  93.21663019693655\nRecall:  90.8315565031983\nMCC:  0.8379994044962448\n['False' 'True']\n388\n370\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Using selected features\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n                                                        test_size=0.30,random_state=42)\n\nlogit3 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit3.fit(p3_train, t3_train)\n\n# predict on test data\npredictions3 = logit3.predict(p3_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\nprint(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\nprint(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t3_test, predictions3))\n\n# rescaling new data\n# newArray = new.to_numpy()\n# rescaledNew = scaler.fit_transform(newArray)\n\n# performance on new data (rescaled)\nnew_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n\npred_df3 = pd.DataFrame(new_pred3) \npred_df3.columns=[\"CLASS\"]\npred_df3.index.name=\"Index\" \npred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df3.to_csv(\"ilujumba3.csv\") \nprint(pred_df3['CLASS'].unique())\n\n\n#printing the numbers of False and True\nprint(pred_df3.groupby('CLASS').size()[0].sum())\nprint(pred_df3.groupby('CLASS').size()[1].sum())","execution_count":32,"outputs":[{"output_type":"stream","text":"Accuracy:  90.5701754385965\nPrecision:  91.36069114470843\nRecall:  90.19189765458422\nMCC:  0.8113912389992642\n['False' 'True']\n390\n368\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Using cross-validation and Logistic Regression"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\n\nkfold = KFold(n_splits=10, random_state=42)\nmodel6 = LogisticRegression()\nmodel6.fit(predictors, target)\n\nresults = cross_val_score(model6, predictors, target)\nprint(results.mean())\n\nmodel6_pred = model6.predict(rescaledNew)\ndf6 = pd.DataFrame(model6_pred)\ndf6.columns = ['CLASS']\ndf6.index.name = 'Index'\ndf6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n\ndf6.to_csv('ilujumba7.csv')\nprint(df6['CLASS'].unique())\n\n\n","execution_count":33,"outputs":[{"output_type":"stream","text":"0.8387328752276078\n[ True]\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier with kfold cross-validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel7 = GaussianNB()\nmodel7.fit(predictors, target)\n\nresults = cross_val_score(model7, predictors, target)\nprint(results.mean())\n\nmodel7_pred = model7.predict(newArray)\ndf7 = pd.DataFrame(model7_pred)\ndf7.columns = ['CLASS']\ndf7.index.name = 'Index'\ndf7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf7.to_csv('ilujumba7.csv')\nprint(df7['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df7.groupby('CLASS').size()[0].sum())\nprint(df7.groupby('CLASS').size()[1].sum())","execution_count":34,"outputs":[{"output_type":"stream","text":"0.887773129281193\n['True' 'False']\n370\n388\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier on rescaled features"},{"metadata":{"trusted":true},"cell_type":"code","source":"kfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel8 = GaussianNB()\nmodel8.fit(rescaledPredictors, target)\n\nresults1 = cross_val_score(model8, rescaledPredictors, target)\nprint(results1.mean())\n# print('MCC: ', matthewscorrcoef())\n\nmodel8_pred = model8.predict(rescaledNew)\ndf8 = pd.DataFrame(model8_pred)\ndf8.columns = ['CLASS']\ndf8.index.name = 'Index'\ndf8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf8.to_csv('ilujumba8.csv')\nprint(df8['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df8.groupby('CLASS').size()[0].sum())\nprint(df8.groupby('CLASS').size()[1].sum())","execution_count":35,"outputs":[{"output_type":"stream","text":"0.887773129281193\n['True' 'False']\n347\n411\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import cross_val_predict\nfrom sklearn.naive_bayes import GaussianNB\n\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel9 = GaussianNB()\nmodel9.fit(predictors, target)\n\nresults = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\nprint('mean for results', results.mean())\n\npredic = cross_val_predict(model9, predictors, target, cv =10)\naccuracy = metrics.r2_score(target, predic)\nprint('cross-predicted accuracy ', accuracy)\n\nmodel9_pred = model9.predict(newArray)\ndf9 = pd.DataFrame(model9_pred)\ndf9.columns = ['CLASS']\ndf9.index.name = 'Index'\ndf9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf9.to_csv('ilujumba9.csv')\nprint(df9['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df9.groupby('CLASS').size()[0].sum())\nprint(df9.groupby('CLASS').size()[1].sum())","execution_count":36,"outputs":[{"output_type":"stream","text":"mean for results 0.9101496004863645\ncross-predicted accuracy  0.6405529953917051\n['True' 'False']\n370\n388\n","name":"stdout"}]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}
\ No newline at end of file

From ba350c56e9f14b5306e49747a3ebe51c8bfca6d1 Mon Sep 17 00:00:00 2001
From: Ibra Lujumba <ibra.lujumba@gmail.com>
Date: Thu, 12 Mar 2020 10:54:19 +0300
Subject: [PATCH 04/10] Replaced deleted notebooks

Submission for Kaggle assignment
---
 .../Ibra Lujumba_kaggle_final.ipynb           | 2050 +++++++++++++++++
 1 file changed, 2050 insertions(+)
 create mode 100644 Assignment Colab/Ibra Lujumba_kaggle_final.ipynb

diff --git a/Assignment Colab/Ibra Lujumba_kaggle_final.ipynb b/Assignment Colab/Ibra Lujumba_kaggle_final.ipynb
new file mode 100644
index 0000000..70f0df8
--- /dev/null
+++ b/Assignment Colab/Ibra Lujumba_kaggle_final.ipynb	
@@ -0,0 +1,2050 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ACE_Uganda Kaggle competition 1\n",
+    "### by Ibra Lujumba"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Exploratory Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Importing python modules for data analysis and visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # manipulation of arrays\n",
+    "import pandas as pd # manipulating dataframes\n",
+    "import matplotlib.pyplot as plt # data visualisation\n",
+    "import seaborn as sb # data visualisation,it is based on plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ignoring warnings that may arise\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###### Importing the datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AMP_TrainSet.csv  Test.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls ../input/ace-class-assignment/\n",
+    "\n",
+    "# reading in the data\n",
+    "data = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\n",
+    "new = pd.read_csv('../input/ace-class-assignment/Test.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Checking the dimensions of the data as well as the datatype of each column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((3038, 12), (758, 11))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking dimensions of the datasets\n",
+    "data.shape, new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(FULL_Charge           float64\n",
+       " FULL_AcidicMolPerc    float64\n",
+       " FULL_AURR980107       float64\n",
+       " FULL_DAYM780201       float64\n",
+       " FULL_GEOR030101       float64\n",
+       " FULL_OOBM850104       float64\n",
+       " NT_EFC195               int64\n",
+       " AS_MeanAmphiMoment    float64\n",
+       " AS_DAYM780201         float64\n",
+       " AS_FUKS010112         float64\n",
+       " CT_RACS820104         float64\n",
+       " CLASS                   int64\n",
+       " dtype: object,\n",
+       " FULL_Charge           float64\n",
+       " FULL_AcidicMolPerc    float64\n",
+       " FULL_AURR980107       float64\n",
+       " FULL_DAYM780201       float64\n",
+       " FULL_GEOR030101       float64\n",
+       " FULL_OOBM850104       float64\n",
+       " NT_EFC195               int64\n",
+       " AS_MeanAmphiMoment    float64\n",
+       " AS_DAYM780201         float64\n",
+       " AS_FUKS010112         float64\n",
+       " CT_RACS820104         float64\n",
+       " dtype: object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the datatypes of the variables\n",
+    "data.dtypes, new.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the values in all the variables exists as either floats or integers.\n",
+    "\n",
+    "Proceeding to work with the training dataset to build the classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.060237</td>\n",
+       "      <td>8.521520</td>\n",
+       "      <td>0.971410</td>\n",
+       "      <td>73.668760</td>\n",
+       "      <td>0.994007</td>\n",
+       "      <td>-2.432927</td>\n",
+       "      <td>0.088545</td>\n",
+       "      <td>15.683233</td>\n",
+       "      <td>73.650828</td>\n",
+       "      <td>5.911361</td>\n",
+       "      <td>1.235255</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.819929</td>\n",
+       "      <td>7.586652</td>\n",
+       "      <td>0.107413</td>\n",
+       "      <td>8.527489</td>\n",
+       "      <td>0.031333</td>\n",
+       "      <td>1.707223</td>\n",
+       "      <td>0.284133</td>\n",
+       "      <td>11.575665</td>\n",
+       "      <td>9.166092</td>\n",
+       "      <td>0.693689</td>\n",
+       "      <td>0.210012</td>\n",
+       "      <td>0.500082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-16.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.684000</td>\n",
+       "      <td>42.750000</td>\n",
+       "      <td>0.866000</td>\n",
+       "      <td>-10.432000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.041000</td>\n",
+       "      <td>42.778000</td>\n",
+       "      <td>3.533000</td>\n",
+       "      <td>0.785000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.516000</td>\n",
+       "      <td>0.895000</td>\n",
+       "      <td>68.294000</td>\n",
+       "      <td>0.974000</td>\n",
+       "      <td>-3.606000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.587500</td>\n",
+       "      <td>67.556000</td>\n",
+       "      <td>5.459250</td>\n",
+       "      <td>1.082000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.143000</td>\n",
+       "      <td>0.963000</td>\n",
+       "      <td>74.059500</td>\n",
+       "      <td>0.994000</td>\n",
+       "      <td>-2.296500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.988500</td>\n",
+       "      <td>73.697000</td>\n",
+       "      <td>5.925500</td>\n",
+       "      <td>1.184000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>13.158000</td>\n",
+       "      <td>1.041000</td>\n",
+       "      <td>79.343750</td>\n",
+       "      <td>1.011000</td>\n",
+       "      <td>-1.283250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>26.807750</td>\n",
+       "      <td>79.778000</td>\n",
+       "      <td>6.382000</td>\n",
+       "      <td>1.351000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>46.667000</td>\n",
+       "      <td>1.451000</td>\n",
+       "      <td>101.682000</td>\n",
+       "      <td>1.196000</td>\n",
+       "      <td>3.576000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>51.280000</td>\n",
+       "      <td>103.167000</td>\n",
+       "      <td>8.662000</td>\n",
+       "      <td>2.192000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "count  3038.000000         3038.000000      3038.000000      3038.000000   \n",
+       "mean      2.060237            8.521520         0.971410        73.668760   \n",
+       "std       3.819929            7.586652         0.107413         8.527489   \n",
+       "min     -16.000000            0.000000         0.684000        42.750000   \n",
+       "25%       0.000000            2.516000         0.895000        68.294000   \n",
+       "50%       2.000000            7.143000         0.963000        74.059500   \n",
+       "75%       4.000000           13.158000         1.041000        79.343750   \n",
+       "max      30.000000           46.667000         1.451000       101.682000   \n",
+       "\n",
+       "       FULL_GEOR030101  FULL_OOBM850104    NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "count      3038.000000      3038.000000  3038.000000         3038.000000   \n",
+       "mean          0.994007        -2.432927     0.088545           15.683233   \n",
+       "std           0.031333         1.707223     0.284133           11.575665   \n",
+       "min           0.866000       -10.432000     0.000000            0.041000   \n",
+       "25%           0.974000        -3.606000     0.000000            5.587500   \n",
+       "50%           0.994000        -2.296500     0.000000           14.988500   \n",
+       "75%           1.011000        -1.283250     0.000000           26.807750   \n",
+       "max           1.196000         3.576000     1.000000           51.280000   \n",
+       "\n",
+       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104        CLASS  \n",
+       "count    3038.000000    3038.000000    3038.000000  3038.000000  \n",
+       "mean       73.650828       5.911361       1.235255     0.500000  \n",
+       "std         9.166092       0.693689       0.210012     0.500082  \n",
+       "min        42.778000       3.533000       0.785000     0.000000  \n",
+       "25%        67.556000       5.459250       1.082000     0.000000  \n",
+       "50%        73.697000       5.925500       1.184000     0.500000  \n",
+       "75%        79.778000       6.382000       1.351000     1.000000  \n",
+       "max       103.167000       8.662000       2.192000     1.000000  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# getting the descriptive statistics of the train dataset such as arithmetic mean, \n",
+    "# standard deviation, quartiles and number of non-NA values in each column \n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\n",
+    "AS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\n",
+    "For all the variables, the data points are not widely spread"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CLASS\n",
+       "0    1519\n",
+       "1    1519\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the proprotions of classses\n",
+    "data.groupby('CLASS').size()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Both classes have an equal number of entries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f817a5bfd68>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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l7lJKbbJWeqcrpYwV5DdKKbVFKbVlxowZ9jgkV0YFPyLK/vo1ODjgHhnK6T//Ydl1t5KyfistJlWuY3BpOVWwrExOrg3q4xIdTcaKleVCD495kaC7htJw/jyM7u7owsJqyKnCpGyeOcXVxzEiiuw/VlW4i9RPP+LIzT3I+nUR3rfdWak0KipMlM2swhhr0B1dYlj+djfmvxpPoI8z78yxzGO9tWMkwX4u3DZhHW99v5vmsb4YjZX4sq/oOJ2vmmIw4Hz3ixSumY9OtXTujS26Urh5Obmv3UnejLE4D33+PMf+Ii5wDErSqjho555TGAwG1ix8hJVzH+CL7zZzPCGDM5l5rFp7gJU/PsiahY+Qm1fIz7/uLr+PyqVX4cu84bZGfPbTHdzzaBvmfLEVAJNJs2d7Ik+P78bEGTezfvURtm9OqFQeFWRRPtfzvH1p458gacQN4OiEU5NWNutcOvUir5LVT0ub5Rst/zmvIMYa9MadDfl27QkGvrOR7DwTjtVUIT6v83xGF//yN3t2n+CeEV2Kl4WE+vDD/KdZuPQFflm4hdSUrOrJocIPle3T2FaBPDi9AyMmtyWmqR+LPyyZW+8V4MKIyW0ZNa09u35PJDsjH3H1uVYroK5KqW3Wx4e11rdc4fYaAdu01qbzrI8Dhmit71dK/QAMBL4GftJafwaglHodGAl8bN2mLtBDa22yTgP4TWv9llKqDzDKuk0DYDDQQWtdqJSaCgwFvirduNZ6BnCu56kfeGhm9bxqO4h7+E5i77fM80zdvBO3yJK5S24RIeSetK3E5KemU5Sdw/H5KwA49uMyao8cVK05BQ29k8DBtwGQvWMnTqElVUvHkBAKk21z8mjRHLdGjWj6+yqUgxEHPz/qff0V+++6m7z/DvPvvZZ5a84xMXh36VypnLwHDcG7v+V15u3ZhUNwyXFyCAqm6LRtTq5NmuFSvyEx85eDgxEHX3/Cp35RrtqZ9etiwt6fRtpnn1xSHt/8doS51jmajWO8SUwrqa4mpucRWKoKBOcqniUxSeklFdKAUhWk2+KjePBDy1QFB6OBMXc0Kl435M0/iQ52v6T8HDrcjMP1/QAwH9uP8gkCLB0z5ROAzqy4Uu50+1Po0wkUrZlfvMyxXR/ypr9o2dfRveDoBO7ecPbypgMEB3lyKrmk+pOYnFVuuNwSk0VIkBdFRWaysvPx8XJh0Yo9dGpXC0cHI/6+7rRsGs6ufadQShER5o2frxsAPTvXZevOBG7u3YjLFRDkTkpSyRzE1ORs/ALOf7zje9Zh2sR1xds2bhmKt/U9bdU+kkP7UmjWOvyy8wBw6zsIt579ASg8uAejfzDnfrIZ/YMwp1/gYrnCAvI3r8GlTTwF260XixmMuLTrSsoz91xWHt+sOc7cvywd6cZRXiSWqlomZuQT6G1bkQv2cSGp1PSTpIySCmntEHc+f6QlYBmO/2P3pQ1FX4qgYG+STpV8HpOSMggMKn+B1Ib1//L5jFX8b9ZDxcPuNvsJ8qZObDD//P0fPXtXvYLt6e9CZkrJ8chKzcejTBXT1cux+HGznuGsnl3mAkHA088Z/yh3ju/JoH774CrnVZ2kAnrtVkBztdbNrX/OdT7PN1HEHhNIDmutz3WI/wZirI8bK6XWKqV2Yuk4lj47/FiqQ9sR+B5Aa70MSLcu7w5cB2y2dri7A5c5EHl1OzD1W5a2GMDSFgM4sWAlte62XKnt37YZhWeybIbfz0n45XeCu7QFILj79WTuOVStOSV/8y27b76F3TffQvrKVfgPsJwQ3Zs3w5SVVW74/fS337O9Yzw7unZn7x1DyTtyhP133Q2Ag591zqFShD38IKe//75SOZ2Z+x3Hhg3k2LCBnF2zCq++lqvsXRo3xXz2bLnh9zM/zeHwjV05cksvTowaRsGxI8WdT8fIqOI4905dKTh6+JLzGNothvnj4pk/Lp7uLUJY+NcJtNZsO5SOp5uDzfA7QJCPC+4uDmw7lI7WmoV/naBbc8uJpPR80RX/JBIXbhmCzs03kZNvmZXy5+7TGA2K2DDPS8qv6M+fyZv0IHmTHsS0608cWvcAwBDdAJ2bXeHwu2Pf4SgXdwoW2M7fNacnY4xrAYAKigIHp8vufAI0qR/K0RPpnDiZQUGhiSWr9tKtY6xNTLeOcSxYYplK/uvqfbS7LgqlFKHBXmywzgfNyS1g++6T1I72JzTYi+27TpKbV4jWmvVbjlI7uvz8yEsR1yCQk8czSTyZSWGhiTUrDtEmPsom5qR1qB1gy5/HCIu0DNW2bBfBkYNp5OUVYSoys2vrKSJr+VJZOUvnkjJ6GCmjh5G3cQ2uXfsC4Fi3Meacs+WG35WLa8m8UIMR55btKTpxpHi9c7PWFCUcwZxafkrBhQyNj2T+C+2Y/0I7ujcNYuGmU5bP+eEzeLo42Ay/AwR5O1s+54fPWD7nm07RrYllrnhqVgEAZrPm02WHGdyxcp3zijRqHMmxYykknEilsKCIX5dso0tX2x8h+/Ym8MZr8/hgyr34+Zf8O0pKzCAvz9K9zzyTw7atR4ipFVQteYXGeZJ+KoeMpFxMhWb2rksitrXtPPWzaSVVzYObT+MfYfnRk5mSR2G+5dSYd7aQhL0Z+Idf2g9QYV/XagW0IqlA2W8+P6A6fm7uBpoppQznhuDLKD0+YALOTXqbBQzQWm9XSg0HupSKK31J5vl+SingS631mMokXVnfjhhPl7otCfDw4fibP/Pqos+Y+dcvV7zdk0v+IKxfZ246uAJTTi4b7n2xeF3frQtY2sLSOd36/CTaz36HlpNfJP90GhvutRwev1ZNiJ8/BSdfL8Jv6kqT1x5jSeMbq5TTmdV/4N05niarlltuw/RCSU6Nfp7P7psvXHz3v+kGgoZabmWVvnw5KXN/qlI+ADl/rsG9fTzR85ai8/JImlBytW/U7HkcGzbwgtsHPDIax6gYMJspTDxF8sTXKpVH56ZBrNmZTO8xv1tvw1RSObll3Brmj4sH4NVhTRjzueU2TJ2aBBZfAT/px73sO56JUhDu78a4uy1XcKdl5XPf+xsxGBRBPi5MvK95+cYvgWnPJowN2uI69ksoyCf/+0nF61ye+ZS8SQ+ivANw6jUUc9IxXJ62dECL1i6kaONSChZOx3nwaBw63wpAwXfvVioPBwcDLz/Vk5Gjf8Bs0gy8sQlxtQP56LO1NK4fQrdOcQy6sSnPTVhEr9un4+3lyvuvWX5g3HlrS158cwk33fU5Gri1XxPqxVqOX6+u9bj13lk4GA00qBvM4P6Vq1wZHQw8+Ex7Xn18KWazpsdN9Yiu7cfX07cQ1yCQtvHRLPpxN9s2J+DgYMDD05knX7VU8j28nBkwpAmjh89HKUWr9pG07hh1kRYvTf7ff+J8XXsCp81D5+dx5uMJxesC3p9NyuhhKGdXfMdMQjk6gsFIwc4t5PxaUsV26dizSsPvAJ0b+bNmTwq9x/+Fi6OBN+8q6eDd8vYG5r9guaXWq4PrF9+GqVMDf+IbWjrGi/9O5Ns1JwDo2SyQW9uFFW/f/dV1ZOcVUVikWbXzNP97uAWxoZd+MZmDg5Hnx97Cw6M+w2zW9L+lNXViQ5j68TIaNoqkS7dGfDBpETk5+Tz31GzAMuz+4ScjOPxfMu+/W/K9fvfwLsTVrfocdQCD0UDP++vxw2tb0WZo0j2UwCgP1n57iJBYL+LaBPL34uMc2JyCwahw9XDghscsd1lIPZHN77MOWs5+GtoMiCYwunIX2F1JUgEFdS1eIaaUOqu19iizLBjYCLTTWidar37/BmhgvWjoCNBKa51Sapvh1mWPXkKbPwD7gVe01lopFQc0BLYDi7TWja1xzwAeWutxSqkUa0w6sARI0FoPV0rNsm4z17rNJ8AxrfVEpVQv4FcgEAgCFmIZgk9WSvkBnlrr888yB60eaneB1fanp23gW1Xv4oF2dKfez+a4yt1370ppfWAfAAfaXv4w6pUSt9EyfG1e93QNZ1LC0PE9ALKf6lnDmdhy/2AFOuXqmv6iAkbwb8akiwfaUV2fZwA4dUvbGs6kROj8jQCYlz9Sw5mUMPSyTIvJKbryP/4vh5vDTczc83BNp2FjRMOpcP5CzhXhOa6H3TpfWeNWXpW9XamAWmmtk5RSTwBLlFIG4CyWeZmlK5Y7lFLnnv8A7ACGK6UGlIppp7U+UUET9wHvAQeVUjlYKq7PXiStl7F0io8CO4HzjSO+BnynlBoM/AGcArK01ilKqZeA5dbXVAg8Yt2fEEIIIWqAHe68c9W7JjugZaufpZYvxFIxrGhdzHl2N+sS28wE7j/P6sal4iaVejwNmFY2WGs9vMyiM0BvrXWRUup6oKvWOt8aOweYcyk5CiGEEELYwzXZAf0/KAr4wVrlLOD8HV0hhBBC1DCZAyod0GqnlNoIlL3r7TCt9c4r1abW+gDQ4krtXwghhBCiOkkHtJppra+emfFCCCGEEFch6YAKIYQQQtiRDMFfuzeiF0IIIYQQNUQqoEIIIYQQdiQVUKmACiGEEEIIO5MKqBBCCCGEHUkFVCqgQgghhBDCzqQCKoQQQghhR1IBlQqoEEIIIYSwM6mACiGEEELYkVRApQIqhBBCCCHsTCqgQgghhBB2JBVQqYAKIYQQQgg7U1rrms5BXF3kAyGEEOJaY9eSZNCHN9ntXJv8xC9XZblVhuBFOd+qejWdgo079X7UQ+1qOg0betoGmn51e02nYWPH3T8A8L/dD9dwJiXuazTV8uDMdzWbSGneQwD4/t9HazgRW3fUnYJpzj01nYYN4+AvOdC2UU2nYSNu424AVh4fU8OZlOgR+RYAeu/rNZxJCdXgJQDy37u1hjOx5fz0T/zgeHWdY24v3F/TKVyTpAMqhBBCCGFHSl2VRUm7kjmgQgghhBDCrqQDKoQQQggh7EqG4IUQQggh7EhuwyQVUCGEEEIIYWdSARVCCCGEsCOpgEoFVAghhBBC2JlUQIUQQggh7EgqoFIBFUIIIYQQdiYVUCGEEEIIOzJI+U8qoEIIIYQQwr6kAiqEEEIIYUdG+a84pQIqhBBCCCHsSyqgQgghhBB2ZJSr4KUCKoQQQggh7EsqoEIIIYQQdiRzQKUCKoQQQggh7EwqoKJSrvtwLGH9OlOUk8eG4S+QvnVPuRiDoyOtprxMUJc2aLNmx9gPOP7TcgI7teK6yS/i07Qef94xmuPzfr3i+X4+bCw3NulAclY6TSYMveLtndMhrBnPt74XgzLw08FVzNy10Gb9s63uoXVIIwBcHJzwc/Gm4/f3AvBky6HEh7cAYPrOefx6ZH215HT4n1RWzfwXbdY07RFG21tjbNbv+u0kq786iIefMwAt+0bQtGd48fr8nCJmPr6BuLaB9Li/XrXktGb9Ad54bxlms5nb+rdk1D2dbNZv/ucIb36wjP0Hk3j/9UH06W45Znv/PcW4txdzNjsfg1Hx0L3x9OvZuFpyOvB3Cks/2482a1r2DKfTbbVs1m9deZLlX/yLl7/lOLW5IZLrekcAMPvVfzix/wxRDXwY+mqLasnnHK01by45ypoD6bg6Gnnzljo0DHMvFzd55TF+3pbCmbwi/n6pTbn1v+5O5ak5B/jhgcY0Dveocl6Bo8fg1j4enZdL0oSx5O/fe97Y0Hen4BgewbE7BwDg98BjeHTqClpTlJ5K0vixmFJOVymf3ZsSmTt1B2azpkPfGHoNqfiz+s+aBD4fv5HnPulKdD1fUhOzmTBiBUGRngDUauDHkCcv7z1c+08Cb/xvC2azZlDPWEYNtP1MFhSaeH7yn+w+lIaPpxPvPxNPRLDlPZg+dyfzVh7CYFCMvb81nVqEcep0Ns9/+CcpGbkYlOL2XnHcfVMDAD78ZhurNh3HoBR+3i689UR7gv3cLitfY9eRGGu1RBflU7RsCjr5P9sAByccbnoW5RMMZjPm/7ZgWvs1ACq8IQ5dR6ACoyla9D7mA9XzPVVWiw/GEtKnM6bcPDaNfIGM85xzWnz0MkHxlnPOzlc+IGH+8iuST1UZpfwnHVBx+cL6xuMZF8Mvcb3wb9uM1tPGsbzd7eXiGo19kLzkNBbV6wNK4eznA0DOsVNsGD6GBs+MsFvOs9YvZsrquXw1/BW7tWkwDCZYAAAgAElEQVRQihfbjmTUitdJyknlu35vsfr4Fv47k1Ac8+6WL4sfD6nfh/p+lk5Op/AWNPCrxW2LnsPJ6MjMXuNYl7CN7MLcKuVkNmlWfLaf219tgae/M7Of20yd1gEERNp2QOp3CD5v53Ldd4eIbORTpTxKM5nMjH9nCV9MGUZwkBeD7vmMbp3qEVs7qDgmNMSbt14ZwMyv/7LZ1sXZkYnjbiEmyp+k05kMvHsGHdvVwcvTtUo5mU2axZ/u4+4JLfHyd2HG6I3UaxtIUJTtcWrcKYQbHqxfbvsOt0ZTmG9my9ITVcqjImsOZHA0NZdlTzRnx4mzvPbLf8x5oEm5uK71fBnaNoQ+H24rty4738TXGxJpGlH1jieAW/tOOEZGc3RQX1waNyXouVc4PnJIhbHuXXqgc3NslmV8PZO06R8D4H37UPxHPkTyxPGVzsds0vzw8XYem9gRn0BX3nnkd5q0DyU02ssmLi+nkNXzDxJT39dmeUCYBy9O716ptk0mM+Onb2Lmaz0I9nfjtmeX0q1NBLGRJf9m5q44iJeHE8s/HcDitYd576t/+ODZeA4ez2DJuqMs+vgmktNyuPeVlSyb2h+jUfH8vdfRqI4/Z3MLGfj0Yto3DyU20oeRtzTkiaHNAfhq0V6mztnBaw+1u+R8DbVaYvANpWDmI6jQujj0GEXhty+Uf11bFqKP7wKDA463jUPHtMB8ZCs66zRFyz7G2Kp/pY7XpQjpE49HbAxLG/TCr20zrpsyjlUdyp9zGox5kPzkNJY2spxznPyq73vq/zKlVB/gQ8AI/E9r/XaZ9VHAl4CPNeYFrfWSqrZr9z64UsqklNpW6k+MUmq4UmpKmbjVSqlW1sdHlFIBZdaX2+Yi7bZQSmmlVO9LiH1QKXV3BctjlFK7rI9bKaU+ush+jiil1pZZtu3cPi6wXRel1CLr4+FKqdPW7fYope6/WP5XWnj/7hz+agEAqRu34+TjhUtIYLm42iMGsvut6ZYnWpOfmg5A9tEEMnbuR5vNdst57cFtpGVn2q09gMb+sRzLSiThbDJFZhPLjvxF18jW543vG9OBpYfXAVDHJ4ItSXswaTO5RfnsTz9Kh7DmVc7p1MFMfENd8QlxxehooH7HYA5uSrnk7RMPZZKTUUBMM78q53LOjt0JREf4ERnuh5OjAzf0asyqNfttYiLCfKkfF4KhzJWjtaIDiInyByA40As/X3fS0m07N5WRcOAMfqFu+IW44eBooHF8CPs2XnpFrnYzf5xcjVXOoyK/7Uunf/NAlFI0i/QkK8/E6ayCcnHNIj0J9HSqcB8frTrOyI5hODtUzzw0j/huZC79GYC8XTsweHpi9A8oF6dc3fC98x7Svphus9ycnV382ODqita6Svkc2Z9GYJg7AWHuODgauK5LBDv+PFUubtGsPfQcXBdHp+p7r3YcSCUq1JPIEE+cHI306xjNqo3HbWJWbTrOgK51AOjdPpr1OxLRWrNq43H6dYzGydFIRLAnUaGe7DiQSpCfG43qWD7nHq6O1InwJinV8jn3cCt5j3PzilCXObfQUKcNpj2rAdCn/gVnd3C37ZBTVGDpfAKYizAn/weelnzIPI1OOQr6yn2fh9/cnSNfW845aRu34+hd8Tmn1vCB7J1Ycs4psJ5zxPkppYzAJ0BfoCEwRCnVsEzYS8APWusWwB3A1OpouyaKwLla6+al/hyxU7tDgHXWvy9Ia/2p1vqri8Rs0Vo/fgnteiqlIgGUUg0uKdPy5mitmwNdgDeVUsGXspFS6opUuN3Cg8k5nlj8POdEIm7htik5eluGr5pNeII+f/9Exx8+xCXI/0qkc9UKdvMjKTu1+HlSTipBbhV33ELdAwj3CGJTouVLfn/aUTqGN8fF6ISPsydtQhoR4l7143c2NQ9Pf5fi557+zpxNyy8X9+/6ZL54aiML39lBZkoeANqsWT3rAJ3viatyHqUlnc4kJLikMhUc5EXS6cv/sbBj9wkKi0xERfhePPgiMlPz8Q5wLn7u7e9MVmr547TnrySmPraeOW9t58zpvCq3eymSMwsI8S7pdAR7OZGUWb4Dej57TmWTmFlAl3pVP07nOAQGUZRU8p1QlJyEQ2D5ryn/Bx4j/ZtZmPPKV/L9H3ycmJ9X4tn7RtJmXHJtoUIZKXn4BpVUwX0CXclItW3z+IEM0pNzadIutNz2qYnZvPXAKj4YvYaDOy/9BxpAUloOoQElUyJC/N1JSrNtOzkth9AAyzC5g9GAp5sjGVn5JKXlltnWjaQ02x9UJ5LOsve/NJrVLengf/D1VrqMnMeiNYd5fEizy8oXDz90VqnXmJWK8rjAD0xnN4y1W2E+tvPy2qkC17Bgck+UfL5yExJxPc85p/FrT9Bz009c/92HOF/F5xyjUnb7cxFtgINa6/+01gXA90DZcrYGzn1JewMnq+MYXBOzEJTlJ+EgYDjQSynlUmrd3UqpHUqp7Uqp2dZl45RSz1gfX2ddtx54pNR2pauUHkqpL5RSO637Gliq+R+AwdbHQ4DvSu3DpdR2W5VSXS/0OrTWycAhIFop5a6UmqmU2mzdtr91n8OVUj8qpX4BlluXPWdtY7tS6u2y+1VKjVJKbVFKbZkxY8alHNCKcrN5bnBwwD0ylNN//sOy624lZf1WWkx6/uL7/r+kouNExZWdPjEdWHFsA2brcVx/agfrErbyVd/XmdjpCbaf/heT2XRF0z2nTutARk3vwL0ftCW6mR9LP7LMtdq67AS1WgbgFeBykT1cnoqKXYrLq+Ikp2Tx7Kvzeevl/hiq4z9ZruhtKpNSvTYBPPV5Jx7++HpqN/dj/uQLDmxUmwqP1yUeLrNZM3HpEZ7rHVW9SVWYgG2iTnH1cYyIIvuPVRXuIvXTjzhycw+yfl2E9213Vi2fCg5S6QzNZs28aTu49cHyUxe8/FyY8E0fxkzvzsAHm/DFm5vJzS68jLbLLyp7dCou8KqL5p2dW8jjE/9gzMjWNpXPp+5qwerPB3JjfC2+XrK/3D4uqKL37nwVaGXA8YbRmLYugTNJl9dOVVxCjsrBAbfIUFL++ocVbW4ldeNWmr1zjZ1zzqP0Od76Z1Sp1eFA6RL9Ceuy0sYBdymlTgBLgMeqI6+amAPqqpQ6NynpsNb6Fju02cHa1iGl1GqgH/CTUqoRMBbooLVOUUpV9LPvC+AxrfUfSql3z7P/l4EzWusmAEqp0qWFucAsYBJwEzAUGGZd9wiA1rqJUqo+sFwpVfd8L0IpVRuoDRy05v2b1nqEUsoH2KSUWmkNvR5oqrVOU0r1BQYAbbXWORW9Rq31DOBcz1N/+8B75dqOe/hOYu+3zLlJ3bwTt8iQ4nVuESHknky2ic9PTacoO4fj81cAcOzHZdQeOeh8L+3/pKTsVIJLVS2D3fw5nVPxkFCfWu15c+PnNss+2zmfz3bOB+DtTo9zNCuxok0vi4e/C1mpJZW6rNT84ouNznH1dCx+3LRHOH/MPgjAyf1nOLE3g23LTlCYZ8JUZMbRxUjnYbFVyikkyIvEpJKKZ1JyJkGBnpe8/dmzeTzw1Dc8+WA3mjeJrFIu53gFOHMmpaTieSY1H88yx8nNq6QDcF2vCFbMOlgtbVfk242J/Pi35d9Yk3APEs+UVDyTMgsIOs9Qe1nZBSYOJOdyzxeWHxUpZwt55Nv9fHJnvcu+EMl70BC8+1v+Teft2YVDcMl3gkNQMEWnbb8TXJs0w6V+Q2LmLwcHIw6+/oRP/YKEh++1icv6dTFh708j7bNPLiuf0nwCXUlPLqk6ZpzOxdu/pCKan1PEySOZTH7aMkMqMy2P6a+s54Hx1xNdz7d4SD6qri+Boe4knzhL9CVWjIP93TiVUjKlIDE1myA/1wpicggJcKfIZCYrpxAfT6cKts0hyHpBUWGRmccn/sFNnWvR6/qKf0DcGF+LB1//7aJVUEPzPhib9ARAJx5EeQaU9Js9/dHZFX9POfR6CHP6KUz/LLrg/qtD7EN3Umuk5ZyTvmUnrhElny/X8PLnnALrOSdhgeWcc3zuMmoNv3rPOfa8DVOZc3xZF//1aCmezdJav6eUuh6YrZRqrHXV5l3U9BD8uc7n+Sb8VG0iUIkhWMrKWP8+NwzfDZirtU4B0Fqnld5IKeUN+Git/7Aumn2e/ffAMocC635K/+tNA9KVUncAe4HS4ykdz+1Ta70POApU1AEdbO20fwc8YM2zF/CCdflqwAU49620otRr6QF8obXOqeg1XqoDU79laYsBLG0xgBMLVlLrbsvVq/5tm1F4Jou8xPLz4xJ++Z3gLm0BCO5+PZl7DlWm6f9v7U49RLRnKOEegTgYjPSJac/q41vKxcV4heLl5M720/8WLzMohbezpUMQ5xNFXZ8o1p/cXuWcQmM9ST+VQ0ZSLqZCM/vWJRHb2nauXukh+YObT+MfbhkSvPGpxjw4oyMPTO9Al3tiadQltMqdT4AmDcM4cjyV4wnpFBQWsXj5Lrp1urSr6wsKi3jkuTn079eMvj0aVTmXc8LivEg7mUN6Yi5FhWZ2rUmkfhvbOWdZpY7T/k2nCYwsfyV6dbmzbQjzH27K/Ieb0r2+Lwu3nUZrzfbjWXi6GM8717MsTxcH/nqhFStHt2Tl6JY0i/CoVOcT4Mzc7zg2bCDHhg3k7JpVePW9GQCXxk0xnz2LKdV26PrMT3M4fGNXjtzSixOjhlFw7Ehx59MxsqRD5d6pKwVHD192PqVF1/MlOeEsKaeyKSo08/fqEzRpXzLU7urhyDs/3ciEb/ow4Zs+1GrgV9z5zMrIx2yynHpSTmaTnHCWgNBLf2+bxPlz9FQWJ5KyKCg0sWTdUbq1sf1h1K1NJAt+t3wf/vrXUdo1CUEpRbc2kSxZd5SCQhMnkrI4eiqLpnH+aK15acp66kR4c29/2+l5R06W/Hj7bdMJaoV7XzRH87ZlFM5+msLZT2M+uAljwy4AqNC6kJ8DFXRAjR2GgJMbpt9nXvKxqIqD075lRasBrGg1gISFK4m5y3rHhLbNKMys+JxzctHvBHW2nnO6XU/m3mvrnFNJJ4DSH9AIyg+xj8QymovWej2W/kb5Sd6X6Wq5Cj4VKPvz0g+4vMk3FbBOsB0I3KyUGoult++vlPK0Pr5QJ/di6y81bg6WDurwCra7FHO01o9WsO1ArbXNeItSqi2QXSauujryAJxc8gdh/Tpz08EVmHJy2XDvi8Xr+m5dwNIWli+Krc9Pov3sd2g5+UXyT6ex4d4xAPi1akL8/Ck4+XoRflNXmrz2GEsa31idKZbz7YjxdKnbkgAPH46/+TOvLvqMmX/9ckXbNGkzb26aybQeYzEqAwsO/s6hMyd4uNnt7Ek9xOoTfwPQt1ZHlh2xvbrbQTkwq7flKuDswhzGrPsYUzVM8jcYDfS4rx5zx2/FbIYm3UMJiPJg3XeHCKnjRWybQP5ZcpyDm1MwGBQung70fazsfPTq5eBg5JVn+3Hf47MxmTUDb2pBXJ0gPpz+G40bhNE9vj479iTw6HPfk5mZx+9r/+XjGatZPOcRlq7czZatR8k4k8P8RZaBlbdfHUCDuuXn9V0Oo9FAvwfrMfvVfzCbNS16hBEU7cFvXx8kLM6L+m2D2PDLMfZvPI3BqHD1dGTAEyUd4M+f30zKiWwK8ky8N3wN/R9vSGzLKn9fAxBf14c1BzLoM3kbLo4G3rilTvG6W6buYP7DTQGY9OtRFu9MJa/QTNdJ/zCwZSCPdqueCnFZOX+uwb19PNHzlqLz8kia8FLxuqjZ8zg2bOAFtoaAR0bjGBUDZjOFiadInvhalfIxGg3c/lhzPnnhT8xmzfV9ogmL8WLRrD1E1fWhafuw8257cEcKi77cg9FowGCAIU+2wN3r0jr4YJnT+fL9bRj52irMJs3AHrHERfnw0bfbaBzrT7c2kQzqEctzk9fR68EFeHs68f7TltuOxUX50LdDNDc8+jNGo4FXRrXBaDTw955kFq7+j7rRPgx40lJ9fOquFnRuFc57X23lyMkzKKUIC3S/rCvgAcyH/8ZQuyVOI6eiC/Mp+rVk/q3jsPconP00ePjj0O42zKkncBw2CQDTtqWYd65EBcfi2P95cHHHUKc1uv1gCr988rJyuJhTS/8gtG9n+u1bQVFuLpvvKznn9NyygBWtLOecHS9Oou2sd2j+vuWcs/m+MdWaR3W6iv4rzs1AnFKqFpCA5SKjsnNgjgHdgVnWa1lcgKrdJw1QVb3a8LIbVOqs1tqjzLJgYCPQTmudaL36/RuggdbarJQ6ArQ6V6m0bjPcuqxsx6xse72B0Vrr3qWWfQmsBP4B5gPXa61TlVJ+1mHrccBZrfUkpdQO4GGt9Tql1ETgBq11Y6VUF+AZrfWN1nmVLlrrJ63799Vap5/LG8gHHgY+AMKARdZ9jAYaaa1HWofeV2CpgF5fat8Vvk6l1JtYJgU/prXWSqkWWuutZeOV5fYKrwA9zg3BX6QKqr9V1XNvx+pyp96Puswv1StNT9tA06/K3wakJu24+wcA/rf74RrOpMR9jawXS5757sKB9uRtGQD5/t8LfnXY3R11p2Cac09Np2HDOPhLDrStvspydYjbuBuAlcevns5Fj8i3ANB7X6/hTEqoBpYfAfnv3VrDmdhyfvonfnC8us4xtxfuh0svCFWLFl8Ptlvna+tdcy742pRS/YDJWG6xNFNr/YZSajywRWv9s/Wq+M8ADywFree01lW+wepVUQHVWicppZ4AliilDMBZYEiZ+QU7lFLnnv8A7ACGK6UGlIppp7Uue+O9IVg6maXNAx7SWs9WSr0B/KGUMgFbKV+lvBeYqZTKAc53x/TXgU+st1cyAa8BP5V6fVnARKDsLTKmAp8qpXYCRcBwrXX+Jd5GYwKWD8wO60VWR4ByZUSt9TKlVHNgi1KqAMsE4hfLxgkhhBDCPoxXTQEUrPf0XFJm2SulHu/Bci1NtbJ7B7Rs9bPU8oXAwvOsiznP7mZdQnvDK1j2M/Cz9fGXWG6wWnr9uFKP/wZKz+geZ12+GsvcS7TWZ4FypYuK8rbedqqx9XEe5Tu8Zfc9iwpep9Y6F3igguXl4q03lS139bsQQgghRE24KiqgQgghhBDXiqtoDmiN+T/VAVVKbQScyyweprW23x1zhRBCCCHEBf2f6oBqrdvWdA5CCCGEEBdiz/uAXq2uif8JSQghhBBCXD3+T1VAhRBCCCGudjIHVCqgQgghhBDCzqQCKoQQQghhR1fTfUBrilRAhRBCCCGEXUkHVAghhBBC2JUMwQshhBBC2JFchCQVUCGEEEIIYWdSARVCCCGEsCO5Eb1UQIUQQgghhJ1JBVQIIYQQwo6kAioVUCGEEEIIYWdKa13TOYiri3wghBBCXGvsWpK8ceEwu51rF/WffVWWW6UCKoQQQggh7ErmgIpyNsfVr+kUbLQ+sI+mX91e02nY2HH3D6iH2tV0Gjb0tA0ATN/1UA1nUuKBxtMAOHDm/RrOpESc92gAlh19roYzsdUn+h2KZt5Z02nYcBjxLQevb1zTadiIXb8LgOXHnq/hTEr0ipoIgD46qYYzKaGinwHgaPfmNZyJrehV2/jJtV5Np2Hj1tz9dm9T5oBKBVQIIYQQQtiZVECFEEIIIexI/ickqYAKIYQQQgg7kwqoEEIIIYQdyRxQqYAKIYQQQgg7kwqoEEIIIYQdGaX8JxVQIYQQQghhX1IBFUIIIYSwI5kDKhVQIYQQQghhZ9IBFUIIIYQQdiVD8EIIIYQQdiQ3opcKqBBCCCGEsDOpgAohhBBC2JFchCQVUCGEEEIIYWdSARVCCCGEsCO5Eb1UQIUQQgghhJ1JBVRUStTLY/HuHI85N4/Dz48hZ8+e88bGfjoV58gIdt9wMwCu9esRM/41DG5uFCQkcOjpZzCfza5yTh3CmvF863sxKAM/HVzFzF0LbdY/2+oeWoc0AsDFwQk/F286fn8vAE+2HEp8eAsApu+cx69H1lc5n4v5fNhYbmzSgeSsdJpMGHrF2zvn8NZUVs88gNmsadI9lDa3xtis3/3bKdbMPoiHnzMAzftG0KRHGAAf3PYbAVEeAHgGuDBgTNNqyenv9ceY8d5fmM2aXv3rc9s9LWzWL5m3h8Vzd2MwKFzdHHl0TDxRtX1JOpnFQ4PnEB7lA0C9xkE8Oia+WnLauzmJn6btxGyGdn2i6HlH3Qrjtq05yRevb+bpKfFE1fUFIOG/M/zw4XbycopQCp6e0hlHJ2O15KW15q1Vx1lzKBNXRwNv9IuhYYhbubgP1yTw865UzuSZ2DK65Hi+veo4m45lAZBXaCYtp4gNTzavcl4BT43BrX0ndF4eyRPGkv/v3vPGhr7zMQ5hERy/6xYA/EY9inunbmA2Y0pPI+n1sZhSTlcpnz2bk5g3dSdms+b6vtH0Os/7t3VNAjMnbObZKZ2Jqlfy/n0/eZv1/VM8+0nl37+1m4/zxrT1mM2aQX3qMeoO22NdUGDi+XdXs/tACj6ezrw/tjsRIZ7s2JfMK5PXAqCBR+9qSc+OtcgvKOKupxdRUGjCZDLTq1NtHr/7ukrldj6+jzyHa9uO6Pw8Ut95hYID+84bGzhhMg6hEZy6b1C15gDQ9L2xhPTujCknj79HvUDGtvLnmU6/foVLSBCm3DwA/rxpBPmn03CNDKXVZxNx9PZEGY3senkSSb+uqfYcK0vmgEoHVFSCd+d4nKOj2dmjN+7NmxE9/lX2DhpcYaxvr56Yc3JsltV643WOT3yHrE2bCRh0K6H3jSRh8kdVysmgFC+2HcmoFa+TlJPKd/3eYvXxLfx3JqE45t0tXxY/HlK/D/X9agHQKbwFDfxqcdui53AyOjKz1zjWJWwjuzC3SjldzKz1i5myei5fDX/lirZTmtmk+e2z/Qx8pQWe/s588/wW6rQOxD/S3Saubvsgut9fr9z2Dk5Ghr3XplpzMpnMTHvnT16fcgP+Qe48dc9PtO0UQ1Rt3+KYLr1j6TewIQAb1xzhf5P/YvxHNwAQEu7Fx99U78nPbNL8OGUHD7/dHp8AV9577A+aXB9CSLSXTVxeTiFrFvxHdP2SXE0mM7Mn/sOw51oSXseb7MwCjNU43rb2v0yOpuWzdFQjdpzMZvzyo3x/d4NycV3qeHNnyyD6zthls/yF7pHFj7/5O5m9STllN71sbtd3wjEyimO39cO5UVMCn3uZE/fdWWGse+cemHNt20z/+gvSZkwBwPu2ofiNeIjT74yvdD5mk+bHj7fzyMQO+AS48u6jq2lyfQihFbx/fyz4j5gy799Xb//NsOevI6KK75/JZGb8lD+Z+XY/ggPcue2xBXS7PprY6JL25i7bj5eHE8tnDWbx74d47/NNfDC2O3Exfsz95BYcjAaSU3MY8OA8ul4fjZOjkVnv3IC7qyOFRWaGPvUz8a0jaN4guHIHqwyXNh1xjIji5N0349SgCX5PjCXx0WEVxrp27IbOvTLfk8G94/GoE8Pyxr3wbdOM5h+NY3X87RXGbr73GTL+sf2c13/+IU7MW8rhz77Ds34d2i+Ywa/1u1+RXEXl1OgQvFLKpJTaVupPjFJquFJqSpm41UqpVtbHR5RSAWXWl9vmIu22UEpppVTvUstilFK7ysSNU0o9Y308Syl12JrndqVU91Jxq5VS+63LNyulmpdaN1gptUMptVsp9U6p5VFKqd+VUlut6/uVWjdGKXXQus/SOc5USiVXkKefUmqFUuqA9W9f6/JnSx3bXdbj7Xepx+l8fHp0J3WBpbqYvW07Rk8vHAMDy8UZ3NwIvnc4J6dOs1nuUrsWWZs2A5C57i98e/eqako09o/lWFYiCWeTKTKbWHbkL7pGtj5vfN+YDiw9vA6AOj4RbEnag0mbyS3KZ3/6UTqEVb0idDFrD24jLTvzirdTWuLBTHxC3PAJccXoaKB+xyAOba5apamq/t2dTGiEFyHhXjg6GonvFcuGNUdsYtw8nIof5+VaqlJX0tH96QSGuRMQ6o6Do4GWncPZ+VdiubglX+6j2+2xODqVfJXu+/s0YbW8CK/jDYC7lxMGY/Xl+9uBDG5u7I9SimbhHmTlmzh9trBcXLNwDwI9HC+4ryV70ujXoMpfCbjHdyVr6c8A5O/egcHDE6N/QLk45eqKz5C7Sftius1ynVMyAmJwdQWtq5TP0f3pBIR5FL9/13WJqPD9WzxrLz1uj8Oh9Pu3JZmw2l5EVMP7t2P/aaLCvIgM9cLJ0Ui/znVY9ddRm5hV648woKelOts7vhbrtyagtcbVxQEHa8e3oKDkM6+Uwt3V8r4WFZkpMplRVN/ny61DF84uX2Rpd+9Oy3vpV8F76eKK16BhnPnms2pru7SwG7tz7NsFAKRv2o6jtxcuIeXPM+elNY5eltEaR29P8k4lX4k0K82o7PfnalXTc0BztdbNS/05Yqd2hwDrrH9fjme11s2BJ4FPy6wbqrVuBkwF3gVQSvlbH3fXWjcCgkt1XF8CftBatwDusG6HUqqh9XkjoA8wVSl1buxnlnVZWS8Aq7TWccAq63O01u+eO7bAGOAPrXXaZb7mcpyCgyk4dar4eWFiIo7B5X99hz/5OIkzv8BsHRo5J/ffA/h07waAb98+OIWEVjUlgt38SMpOLX6elJNKkFvFJ9ZQ9wDCPYLYlGjpx+9PO0rH8Oa4GJ3wcfakTUgjQtz9q5zT1ehs2v9j777Do6jaBg7/zm567z0h9BqKVOkEpNgAwYKIYgML6msHG4oCdl8rUkREQFEQREWK9C5FWugQAum9993z/TGbbDZZICQh4fM993XlYnfmzMyTncnsM885MxTh6mNf/t7Fy56ctKIq7c7sTmHhs3v47YMj5KSa919psZHFL+1lyeR9nNlTN4lrWko+vv4u5e99/JxJS6k6JOP3n4/yyMgf+Pbz3Ux4vlf59KT4HJ6+bxmTJ67i6D8JVZariazUQjx8Hcvfe1m9tmYAACAASURBVPg6kpVmeRzHnskkI6WAdj0CLKanxOYiBMyaspMPntjMhp9O10lMZZJzSwhwMyfk/q52JOUUX/V64rOKiM0qonsj11rHZOPrT2mSOcErTUnCxrfqOcF7wlNk/vAdsrCwyjyviU/TaOVfuAy+hbS51a4nWJWZWoBnxf3n40BmqmWl7uIl9l9yXC4C+HLyTt57fBN/La35/ktKzSPQ13xsB/g6k5RmeWwnp+YT6Kv1QNjodbg625GZrf1NHjqezK2P/sztE5fz5tO9yhNSg8HIiMeW0+uu7+l5QzAdWvvVOMbK9D5+GFIs96Xep+r6PR58kuyfF2K0si/rgkOQPwWx5jgK4hJxCLJe5e08ewaRu1fSavIT5dOOT/+C0HtuY9iZLfRcMYdDz71zTeJUaq6hE9B6J7TLyNHAeGCwEMKhBqvZBQRXY14T4JSUsuyb+i9glOm1BMr6g9yBeNPr4cCPUsoiKWU0cAboBiCl3ApYSyCHA2X9y98BI6y0GQP8YC1gIcQEIcQ+IcS+OXPmXOLXqriAlWmVKhaOrVvh0KgRmev/qtI0esor+N03ljYrlqN3dkaWVK3eXDUrFTGJ9SrK0PBerL+wG6Mp5l0Jh9ke9w8Lh73De32e4VDKKQxGQ+1juh5Z+UgqVxObdPXh4a97cv8n3Qlr78Waz83jrh6d3ZOx73fl5v+0ZfO3p8lMrH33rbVql7VD7NY72zFvxRjGT+rO0vkHAPDyceLbVWP5bNFoHvnPjXz4+gbyc68+GasSktUPyvzSaJSs+PooIya0q9LMaJCcO5rOuMmdeebj3hzekcDJf+quyiytfV41qHKsPp7B4JaedfQ/slhZR6U47Zq3xDYkjLwtG6yuIX32Z8SMGETuuj/wGG29+77arB7n5tdGo+SXWUcYOdH6/jsblc4DUzrz7Cd9OLQjnpMH6m7/Vd5XlzvWOrT24/e5d/LzFyOYs/QQRcWlAOj1OlZ+PYrNS+7l8MkUTkXXuq5QdeMWQVrGaNu0JTbBoRTs2FSH260UhbWD2sqxv/fBF9jQ9Xa2DhqLd6/OhN07HICQu24hZtEK/mzWj50jJ9Dlm/dr9odyjeiEqLef61VDJ6COFbqIV9TTNnsB0VLKs8Bm4ObLN7dqKLCyGvPOAK1M3fs2aIlh2QCsN4H7hBCxwGrgKdP0YOBihfXFculkt4y/lDIBwPSvxeWqEMLJFNdyawtLKedIKbtIKbtMmDDB6gb8xt5L21UraLtqBSVJydgFmquWtgEBlCRbdm+4dOqIU9u2tN+0gdY/LsYhPJyWixYCUHgumlMPPsyxkaNI+/0PCi9cuMKvd2VJeWn4V6ha+jt5k5KfYbXt0MY9+TN6h8W0uUdWcNfvLzHxr3cQQhCTU7W77t/AxduenFRzxTM3vQgXLzuLNo6uttjYaqeGiEFBJJ3LMS9vujHJI8CRkLYeJEfn1jombz9nUpLM60lNzsPL1/mS7fsObsbuLecBsLXT4+ahXUM2a+1LQIgbcReyah2Th48jmSnmillmSgHuXuZr1aKCUhLO5/DFi9t5a9w6zh/PYO4be7hwKgMPHweatffGxd0eOwcb2nT1J/Z0Zq3iWXIgmTu+PcYd3x7D18WOxGxzkp2UU4yfi91llrbuz+O16353H3UPod8tI/S7ZZSmJmPjb64k2vj6U5pqeU5waNcR+5ZtaPTLWkJmL8QuLJzgL7+tst6cdX/g3H9QjeMCrWKdUXH/pRbi7m2uiJbtv89e2M7U+9Zy/ngGs9/Yw4WTGXj4ONIswrz/2nbz5+KZmu0/fx9nElLMx3ZiSh5+Xs5W2mhV0VKDkZy8Yjxc7S3aNA3zxNHBhlPnLc9pbi72dGsfyLZ9sTWKr4zL8LsJnL2UwNlLMaSloPe13JeGNMsE3L5Ne+yatyZ48WoCPv0W25BG+H80r1YxADSZeC+Ru1cSuXslBQnJOIaY43AMDrDajV4Yr00rzc3j4tLf8eyq3RgZ/sBo4pb/CUD6noPoHeyx9/GssrzScBo6Aa3YBT/SNO1Sg39qNyjIbAzwo+n1j5i74auz3Q+EEOeARcCMSu0Wm5LJl4HPAaSUGcDjwFJgG3AeKK0QxwIpZQhaEvy9EELHJeqL1frNLu02YEdtut+TFy8h6vaRRN0+koy/NuA9QrvKdO7YAUNODiUplieolCU/cqh3Xw4PGMjxe8ZSeP48J++7HwAbL9OXnhAEPfEYKT/+SG1FpZ2lkWsgwS6+2Oj0DA3vyeaL+6q0C3cLxM3OmUMpp8qn6YTA3V7rJmvuEUYLjzB2xR+qdUzXo4BmrmQm5JOVVIChxMiJ7ck06WI5vis3w5ygnt2Xilew9oVZmFtCaYkRgILsYuJPZOEdculEsbpatPEj/mIWiXHZlJQY2LruDN37NLJoUzGp3LsjhqBQrfMgK6MAg0GLKTEum/iLWQQE175LOaylBylxeaQl5FFaYuTAljja3Vjhy9DZlhnLhjH1+8FM/X4w4a09eXRad8JaeNKqix/x0dkUF5ZiMBg5cySVgFp2c997gx+/PNiGXx5sw8AWHqw6moaUkkNxubjY66841rOy6LRCsgsNdAyu+f7LWv4jFx8YzcUHRpO3dSOuw7SnXNi3bY8xLxdDWqpF++wVSzl/eyQxdwwhduL9FF84T9yT2lMobEPCyts59x5ASUx0jeOCsv2XS6pp/+3fHEtEpf337vKbeWvREN5aNITw1p5MnNadsJaetK60/04fTqvx/oto6UtMXDaxCdkUlxhYveUskTeGWbSJvLERK9dr56O1W6Pp0TEIIQSxCdmUmo7tuKQcoi9mEeLvSnpmAdm52t9oYVEpu/6Jo0moe43iK5P761ISJt5NwsS7KdixCZfBtwJg1zpC25fplvsy97efibt7MHFjbybxmQcpiY0h6flHahUDwLnZS9jYYwQbe4wg4be/CLtX68zz7NaBkuwcChMtv2eEXo+dt5ZUChsbAm/uT3aUNmQi/2ICvv1vBMC1ZRN0DvYUpdRlpbh21BjQ6/Mu+DSg8mWKF5Bqpe1VMY2lHAXcLoR4FS3Z8xZCuF5muxXPhC8CvwBPo3V1V3z2xVjgEPAu8CVwB4CU8jfgN9P2JwBlfbsPYxrPKaXcZRoK4INW8QytsN4QzN3zl5IkhAiUUiYIIQKBypeJ93CJ7veayNq8Bfd+fYnYsE57DNPkV8rntV21gqjbR15mafC+7Rb8xmqPHcpYt47UZb/UOiaDNDLj7/nMGvQqeqFj5ZlNnM2K5YkOd3Es7SybY/cDMKxxb9ac32mxrI2wYcEQ7Y7bvJJ8pmz/HIM01jqmK1ny0DT6t7gBHxcPLs5YxdTf5zJ/52/XdJs6vY4Bj7Rg+dsHkUZJu8ggfMJc2PHDOQKaudK0qy///BHLub2pCL3AwcWGoZO0O6zTY/NZP/sEQgiklHQd2ajK3fM1obfR8diLvXnj6dUYjZKbbmtJo6ZeLJq9l+atfeneN5zffz7Kob/j0NvocHGz59mpAwA4+k8Ci2fvQ6cX6PU6npzcB1f3moyqqRSTXseoSe2Z9Yr2+JweQ8IIDHdj9XfHCW3hQcSNlx637ORqR/87mvLRU9ojX9p086dt94BLtr9afZu4sfVsFsPmHMXBRsc7N4eXz7vj22P88qD2tIAPN8Wy+lg6hSVGIr88zKgOPjzZW3uc1urj6Qxr7VlnN3Pl79yKU88+NPr5T4xFBSS/83r5vNDvlnHxgcs/pcD7iWexDQsHKSlNjCe5FnfAg7b/7pzUnq+m7EQaJT2GNCIw3I0/FhwnrIUHET0vv/8iRzXjg0lbEELbf+1quP9s9Dpen9STh1/5E6NRMmpIS5qHe/HZd/to18KXyBsbMXpoS156bzODxy/F3dWej1/Rxsfvj0pi7htrsdHr0OkEU5/qhae7AyfPpTH5gy0YjBJplAzt14QBPRpdIZLqK9izDcfuvQn6/jdkYSFpH0wtnxc4W0tU60Pimi34D+nH4Kj1GPIL2D/R/D0TuXslG3uMQGdvR69V89DZ2iL0OpI37SJ6/k8AHJn8Ljd89Q7NnhoPUrL/0cn1ErdSfcLaeKJ627gQuVJKl0rT/IE9QA8pZaLp7vfFQGsppVEIcR7oIqVMrbDMeNO0SVfY3hDgOSllxTvLvwP+klJ+L4TYB7wspdxgult8NzBMSnlWCLEA+F1Kucw0jvQAMFlKuVYIsRl4QUq5TwjhCJxFu/HouBDCT0qZbLozfRNwl5TylBDiT2CplHKBEKI12s1DwUAbYAnauM8g0/TmUkqDKd5wUxzlg5eEEB8AaVLKd4UQkwEvKeVLpnnuaEl0qJSyOg/blHubt6pGs/rT9fQJ2i+0/viNhnL4/p8Qj/do6DAsyFm7AZh99PEGjsRsYjvtCQinsz5u4EjMmrs/B8CamJcaOBJLQxu9T+n8Wo59rGM2Dy3hzI1Vx0k2pGa7tJsH1114uYEjMRsc9h4AMubDBo7ETDR6AYCYgdf+iR5Xo9GGg/ziWPURbw3pjoKTYL338Zp5duuj9ZZ8fdJ37nVZB23oLvgqpJRJwDPAaiHEQeC/wBgpLUpSh4UQsaafsm+28RWmxQohQqysfgxQeazpcqDsrH8/8JppuxuBt0xjRSvHKIF3gCrfYFLKAuAj4AXTpE+FEMeAHcC7Usqyvt/ngUeFEIfQqpPjpSYK+Ak4BqwBnqyQfP6AdpNTS9Pv+LBpXe8CNwkhTgM3md6XGQmsq2byqSiKoiiKcs01aBd85epnhem/Ar9eYl74JVa3oBrbG29l2ipglen1MWBAdZaVUi7HdFOPlLJ/pXkfVXht9VFPpm31usS86cB0K9Mvta40wOoTdqWUC6jGZ6MoiqIoilJfrscxoIqiKIqiKP9a1/PNQfXlX5uACiH2APaVJo+TUh5piHgURVEURVEUzb82AZVSdm/oGBRFURRFUSrT1cl/APH/23V3E5KiKIqiKIry7/avrYAqiqIoiqJcj9QYUFUBVRRFURRFUeqZqoAqiqIoiqLUIzUEVFVAFUVRFEVRlHqmKqCKoiiKoij1SI0BVRVQRVEURVEUpZ6pCqiiKIqiKEo90glVAlUVUEVRFEVRFKVeqQRUURRFURSlHulF/f1ciRBiqBDipBDijBBi8iXa3CWEOCaEiBJCLKmLz0B1wSuKoiiKovwPEkLogS+Bm4BYYK8QYpWU8liFNs2BKUAvKWWGEMKvLratElBFURRFUZR6dB09B7QbcEZKeQ5ACPEjMBw4VqHNo8CXUsoMACllcl1sWEgp62I9yr+HOiAURVGU/zX1mhJO3zux3r5rX+s2ZyIwocKkOVLKOQBCiNHAUCnlI6b344DuUspJZY2FECuBU0AvQA+8KaVcU9u4VAVUURRFURTlX8qUbM65xGxriXfl5NgGaA70B0KAbUKIdlLKzNrEpRJQpYrT3ds2dAgWmu+JYl7UEw0dhoVH2n7F7KOPN3QYFia2mwWAeLxHA0diJmft1v49Ob2BIzETLV8F4Fz2Zw0ciaUmbk9jXPdkQ4dhQTf4S6IHdGjoMCw03nQIAM+ZQxs4ErOMKVoxSMZ/0cCRmIkgrYCVPr5PA0diyWvBNtZ4tWzoMCwMTT9Z79vUXz+PYYoFQiu8DwHirbTZLaUsAaKFECfREtK9tdmwugteURRFURTlf9NeoLkQorEQwg64B1hVqc1KYACAEMIHaAGcq+2GVQVUURRFURSlHl0vNyFJKUuFEJOAtWjjO+dLKaOEENOAfVLKVaZ5g4UQxwAD8KKUMq2221YJqKIoiqIoyv8oKeVqYHWlaW9UeC2B50w/dUYloIqiKIqiKPWoOg+I/7dTY0AVRVEURVGUeqUqoIqiKIqiKPVIp8p/qgKqKIqiKIqi1C9VAVUURVEURalH19FzQBuMqoAqiqIoiqIo9UpVQBVFURRFUerR9fIc0IakKqCKoiiKoihKvVIVUEVRFEVRlHqkngOqKqCKoiiKoihKPVMVUEVRFEVRlHqkxoCqCqiiKIqiKIpSz1QCqiiKoiiKotQr1QWvKIqiKIpSj9SD6FUCqtSQ73NTcOrZF1lYQNLbr1J08vgl2wZ+8AW2wSFcuHcEAF4Tn8KlzwCQktKMNJKmvYohNaXWMUUfSGPD/FNIo6T9oCC63xFuMf/oxng2LzyDi5c9ADcMC6H9TcHl84vyS5n/9G6ad/dl0KMtax0PQPQ/aWyefxqjURIxMJBulWKK2pjA1u/NMXUcFkLEoCAAPrlzIz5hLgC4+jgwYkr7Oonpcr4Z9yq3RvQiOSeDiLfHXvPtldm2P47p8/ZiNEhGD27GhNERFvOLSwy8/Ml2os6k4+Fmz8cv9iXE34WM7EKeeW8LR0+nMSKyKW881r3OYtq3M4avP9qO0Whk6PA23DW+s8X8P5Yf5fefj6DTCRyc7Hj6lf40auLFyagkPpu+CQAJjH20G70GNKmzuKSUzFh+iq1RqTjY6ZlxXxvahrpVaRd1IZspi6IoKjHSt60Pr4xqgRCCE7E5vLn0BPlFpQR7O/LB/e1wcaz9V4HXUy/j1L03srCQlPdep/j0iUu29XvnU2yDQoh7aBQATv1uwnP849iGNSb+8bEUnzpW63gGNunMzEGPo9fp+P7gGv67+yeL+SFuvnx16wu42zuj1+l5a/N81p/di63Ohk+GPU2ngOYYpWTyX1+z48LhGsex7e8Ypn+xVTu2b2nDhHu7WMwvLjbw8sx1RJ1KwcPNgY+nDiUkwI3YxGxueWARjUM9AejQJoC3nhugLVNi4O1Pt/D3oTh0Av7z8I0M6desxjE6jX0G2/Y9kMVF5M2bgSHmVJU2Ls9/iM7dG/R6Sk8dIn/hJyCNON7xMLad+oA0IrMzyJ03A5mZVuNYKmo981V8buqHsaCQI09OJvtw1eOi26qF2Pv7YSgsBGDfqIcoTk0neMxIWr71EoUJSQBcmLeI2O+X1UlcSt1QCahy1Zx69sE2tBExo4fh0K49fi+9wcWHx1ht69x/ELIg32Ja5qL5pM/+HAD3u8bi/fDjJL83rVYxGQ2S9XNPctfUTrh62/P9S3tp2tUHn1AXi3atevlfMrnc/sNZQtt61CqOyjFtnHuSUW9oMS1+eR9Nu/riHeps0a5FTz8GWonJxk7PuI+61Vk81bFg1x98sXkZC8e/UW/bNBiMTJu9h/nTbsLf24k7n19NZLdQmoWZ98Wy9adxc7Fn3ZyR/LE1mo++288nL/XD3k7PM2M7cjomk1MxmXUa05fvb2XGF7fj4+/CMw/8TPe+jWnUxKu8Tf8hLbhlVDsAdm+JZu4nO3jn89to1NSLzxbehd5GR3pqHk/cu5QefcLR29TNiKetx9KISc5nzRs9OXQ+m2lLT7D0harHyVtLT/DWmNZ0DHdn4qyDbDuWRt+2Prz+w3FeHNGcbs09Wb4rjm82xPDMrU1rFZNj997YBocRe99t2LeOwPvZ10h44j6rbZ36DEQWWp4TSqLPkPzGs3g/93qt4iijEzo+GPwkI398hfjsVDaO/4w/T+/mZNqF8jbP9xzDyuNbmf/PH7T0DuOnu96mw6wHeKDjMAB6ffM4Pk7u/HzXO0QueBqJvOo4DAYj0z7dzPwPRuDv68Kdjy0lsmcTmoWbj6Nlq6Nwc3Vg3eL7+WPjKT6avYNPpmoxhAW5s3Je1XPr14v24u3pyNrvx2E0SrJyCq86tjK27Xug8w8h6+Ux6Ju2wfn+58l+e2KVdrlfvgGm/eYy6W3sug2geM8GClb/QMEv3wBgP2gUjsPHk//dRzWOp4zPoL44NQ1nW5fBuHfpQJuP3mT3TXdZbXto4gtkHzxaZXrCitUcf/ntWsdyLaibkBpwDKgQwiCEOFjhJ1wIMV4I8UWldpuFEF1Mr88LIXwqza+yzGW2eV4IccT0c0wI8Y4Qwr5Sm2eFEIVCCHfTez8hRLQQIqBCm6+EEJOFEP2FEFII8XCFeZ1M014wvV9a4Xc8L4Q4aJpuK4T4zhTLcSHElArrGCqEOCmEOCOEmFxh+mLT9KNCiPlCCFvTdCGE+MzU/rAQ4oYKy6wRQmQKIX6vzmdUHS59I8n+cxUAhUcPo3N1Re/tU6WdcHTC894HSP92tsV0Y15e+WudoyNSXv2JvbKEM9l4BjriEeCI3lZHq97+nPk7tdrLJ57NJj+zmPAOXlduXN11nsnGI8CpQkx+nN1b+0rvtbTtzEHS87LrdZuHT6cRFuhKaIArdrZ6bu4TzoY9Fy3abNhzkRGRWpI0pFcjdh1KREqJk4Mtndv4Y2enr9OYTkUlExTqTmCIO7a2evrd1JzdW6It2ji72JW/LiwsoaxHzcHBtjzZLC4yUNc9bRuPpDC8WyBCCDo2die7oJTkrCKLNslZReQWltKpsQdCCIZ3C2TDEe3Yi07Oo2szLbnv2cqb9YeSax2TU68B5K77DYCi40fQObui97JyTnBwxP3OcWR+P9diesmFaEouxtQ6jjKdg1pyLiOBmMxESoyl/HJ8Cze3uLFKO1d7JwDcHJxJzNWqdi19wth6/iAAqflZZBXl0imweY3iOHwiibAgD0KD3LVjO7IFG3acs2izYUc0I4a0AmBIv2bsOhB7xXPiL38eL6+k6nQCT3fHGsUHYNupN8U71gBgOHsM4eSCcPeu2rDsokGvBxtbKIuxwsWEsHekBnm6Vf43DyT+x5UAZO07hK2bG/b+vnWzcuW60JAV0AIpZceKE0T9jIkYIKVMFUK4AHNMPw9UmD8G2AuMBBZIKZOFEO8BHwL3mZK73kBnoBdwBLgb+Ma0/D3AobKVSSnvLnsthPgIyDK9vROwl1JGCCGcgGNCiB+Ai8CXwE1ALLBXCLFKSnkMWAyUlRWWAI8As4BhQHPTT3fTtLK+yA8AJ6DqJW0N2fj6UZqUWP6+NDkJG19/DGmWCZ/3xKfIWLwAY2FBlXV4P/Y0rjffjjE3l7gnHqx1TLlphbh6O5S/d/W2J+F01UTq1K5kLh7LxCvQkQEPtcDNxwFplGxecJqbn2nLhcPptY6lPKb0Ilx9zNc3Ll7WYzqzO4W4Y5l4BjnR/8HmuPpov0dpsZHFL+1F6ATdRjaiWfd/58k3KS2fQB9zVTjAx4lDJy2PpeS0AgJ9tGTBRq/D1dmWzJwiPN0cuBZSU3Lx9TdXz338XTh5NKlKu99+OsIvSw5SWmLk3VnDy6efOJrIJ9M2kpyYwwtv3VRn1U+ApMwiAjzNv3eAhz3JWUX4uZuPteSsIvw9zG38PexJytSS1OaBLmw8ksLA9n6s/SeJhIyaV8/K2Pj4UZps/nwMqUnoffwwpFvuR8+HniTrp4XIwtpv83ICXbyJyzZf7MXnpNI5yLKX4d1ti/jlnuk82vl2nG0dGPGjVgM4mnyOYc1vZPmxzQS7+dIxoDnBbr4cSKjaLX0lSal5BPqZj6MAXxcOHU+0aJOcmkugnytgOrZd7MjM1j6f2MRsRj76A85Odvzn4R50aR9Mdq62Hz+dv5u9h+IIDXLn9af74ePldNXxAeg8fTGmmy9CjBkp6Dx9MGRV7UZ3ff4j9E1aU3J4N8V7N5dPdxz1KHY9hyAL8sh575kaxVGZfaA/BXHmz6owPhH7QH+KkqpexEd8MQNpMJL02zrOfvhV+XT/2wbj1bMreWejOfHqTArjEqss21DUg+j/h++Cl1LmAo8BI4QQXgBCiKaAC/AaWiJaZg7QVAgxAPgCmCSlLDHNuwA4CCH8hZZBDwX+rLw907y7gB/KQgCchRA2gCNQDGQD3YAzUspzUspi4EdguCnm1dIE+BsIMa1rOLDQNGs34CGECDQtswHIudxnIYSYIITYJ4TYN2fOnCt/eFYvFCwve+2at8I2JIy8LRusriLt6884f/sgctb+jvud9155m3WgaVdfJszuxYOfdKdRBy/+/EwbT/TPmlga3+CDm08dJzNWKgGVL7KadPXh4a97cv8n3Qlr78Waz81jnB6d3ZOx73fl5v+0ZfO3p8lMzK+8un8HK9WeyodYXVTJr4q1zVk57G+7K4JvV47joadu5If5+8qnt2oXwOyf7uXT7+7kpwX7KS4qrbvQrH1e1WljajT93jYs2RbLqPf3kFdowFZfB18DVr9MK50TmrbENjiM/O0ba7+9K4Vj5RxV+TMZ1aY/S46sp92X47jr5zf4+rYXEQgWHVpLfE4Kmx78nJmDHuPvuGOUGg01C8TqfhBXagKAn5czG38cz4q5Y5j8RB9eeGcduXnFGAxGElNyuaFdIL/MuYeObQJ4/+vtNYtPC6hacQPkfPQ8mf8ZgbC1xaZNeUcbBcvnkvX8aIp3rcdh4B01j6UGcR2a+AI7et/OnlvG4nljZ4Lu1i4Ek9dsYkvHSHb0uZ20zbuI+PK9uolLqTMNWQF1LOuOBqKllCPrOwApZbYQIhqtcrgHLen8AdgGtBRC+Ekpk6WURiHE48BGYJWUcmulVS1Dq2j+AxwAiqiqD5AkpTxdYZnhQAJahfJZKWW6ECIYrQpaJhZzNRPQuu+BcUDZpaa1ZYJN667O51BWCQaQp7/5tEob99FjcB8+GoDCY0ex8S8fkYCNnz+lKZbdeI4RHXBo1YbwFevARo+NpzfBX31bpdqZs/YPgj6eRfrcL6sT6iW5eDuQk2auquSkFZXf2FMek6tt+ev2g4LZ8v0ZAOJPZhF7PJODa2IpKTRgKDVi66Cn37iaD+rXYrInJ9V8KOSmF+HiZWfRpmJMEYOC2LbojHl5U/weAY6EtPUgOToXj4CaVTmuZ/4+ziSkmodlJKbm41epmuPv40RCaj4BPs6UGozk5JXg4WpfeVV1xsfPhZSk3PL3qUm5ePs4X7J9v8HN+eLdLVWmhzX2wsHRlvNn02nRxq/G8SzeepFlO+MAaBfmRmKFqmViZhG+7pafhb+HA0mZ5jZJmeYKaZMAZ755UkseopPz2BJV/aEqFbmOeKVVGgAAIABJREFUuBvXW7Rko/hEFDZ+/uUnPr2Pf5UbC+3btseuRWtCfliN0Nug9/Ai4JN5JD77SI22fznxOakEu5l7DIJcfUjMtezduK/DEO5c+ioAe+OO46C3w9vJjdT8LF7dYL4QXzvuY86lx9coDn9fFxKSzcdRYkouft7OVtrkEODroh3bucV4uDkghMDOTutab9fSj9Agd6JjM2jXwg9HBxtu6qMNSRnavxnLV1/dTVv2A0di3+82AEqjT6DzMh+bOk9fjJe7iaikmOJ/dmDXqTelUfssZhXvXo/Ls+9TsHL+VcVTJuzhewm5XxvnmfXPERyDAygb2e0QFEBRYtXhIkUJ2jRDbh4Jy37H/Yb2xC/9lZIM85jwiwt/osWbL9QopmtFp+6Cb9AKaIGUsqPppyz5vFSZ41qWPyoeBfcAP0opjcAvaEmlFoCUB4GjwFdU9ZOpbVkCa03led0AAxAENAaeF0I0wXotofLv/xWwVUq5zcrvcKllaiVr2Q9cGDeKC+NGkbt1A27DbgfAoV17jLm5Vbrfs35ZSvStAzg/cjCxE8ZRfOF8efJpGxpW3s65zwCKYyzH1tVEYDNXMhLyyUwqwFBi5MT2JJp1tRyDlptuTgbP7E3BO1j7Irj12XY8Nqc3E2f3ov8DzWjbP7DWySdAQDNXMhPyySqPKZkmXSrFlGGO6ey+VLxMMRXmllBaYgSgILuY+BNZeIdcOgH6/yyiuTcx8TnEJuZQXGJg9bbzRHYPtWgT2S2UlRvPArB2Rww92gdc0yE7Ldr4EX8hi8S4bEpKDGxZf5oefcMt2sRdMH/B/b39PMFh7gAkxmVjKNX2XVJCNrExGfgHudYqnrF9Q1kxuQcrJvdgYHs/fv07ASklB6OzcHWwseh+B/Bzt8fZwYaD0VlIKfn17wQiI7SELC2nGACjUfL1mmju7h1cZXvVkbNyKfGP3k38o3eTt2MTLoO1hMa+dQQyL7dK93vOqp+5eOdNxI65mYSnxlMSG3NNkk+AA/EnaeoZRJi7P7Y6G+5o3Y8/T++2aBOXnUzf8E4AtPAOxd7GjtT8LBxt7HGy1T7P/uGdKDUaLG5euhoRrfyJicskNiFLO7Y3niKyZ2OLNpE9G7NyrfbEgLVbztCjUwhCCNIzCzAYtOPoYnwWMXGZhAa6I4RgwI2N+ftgLAC7DsTSNPzqxq4XbVhB9hsPkf3GQ5Qc2IZdr6EA6Ju2QRbkIit3v9s7mseF6vTYtu+BIUH7THT+IeXNbDv1xphQs88K4MI3S9jZbwQ7+40g+Y+/CLpHe3KKe5cOlGTnVOl+F3o9tl7aUwKEjQ2+Q/qTe1yr8VQcL+o3LJK8U2drHJdybVxvd8GnAZ6VpnkBNbtEvwIhhCsQDpwSQrRHq4SuN32x2QHn0MZjljGafixIKROFECVo4zafAXpW2o4NcAfauNEy9wJrTF35yUKIHUAXtEpmxW/fECC+wrqmAr5YjumMvdwydS1/x1ace/al0fI/kYWFJL39Wvm8sO+Xc2HcqMsu7/Pkc9iGhYPRSEliAsnvvVXrmHR6HYMeacmyaf9gNELEwEB8wlzY/sNZApq60aybLwdWX+TM3lTtsTmuNgx7qk2tt3ulmAY80oLlbx9EGiXtIoPwCXNhxw/nCGjmStOuvvzzRyzn9qYi9AIHFxuGTmoNQHpsPutnn0AIgZSSriMbVbl7/lpY8tA0+re4AR8XDy7OWMXU3+cyf+dv13SbNnodr0/sxsNv/oXRKBk1qBnNwzz4bPFB2jXzJrJ7KKNvas5LH29n8IQVuLva8fGLfcuXj3xkOXn5JZSUGtmw5yLfvDXI4g76mtDb6Hj8pT689vQqDAbJ4Ntb06ipNwu/3kOL1n706NeY3346wj9/X8TGRoeLmwPPTx0IQNShBH5asB8bGx1CJ3jy5X64e9T8JpHK+rX1ZuuxVIZM24mDrY4Z97Utnzfy3d2smNwDgKl3typ/DFOf1t70baMlEH/sT2TJVi15uamDL3f0CKp1TAW7t+HUvTchi35HFhWS8p75KQpBc7VE9XKcekfi/fRk9O6eBMz8gqKzJ0l66fEax2OQRl5a/xXL75mOXuhYfHgdJ1JjmNJnHAcTTvPnmd28tmEun978DE90HYlE8uQf2p3bPs4eLL97OkZpJCEnjcd++6DGcdjodbz+dD8efmkVRqORUcPa0LyxN5/N3027ln5E9mrC6Fva8NKM9QweuxB3N3s+fl1LBvceiuPzb/eg1wv0eh1vPjsAD9OY5+cn9OTlmeuZ8eU2vNwdmfHyoBrHWHJoF7bte+D+/o/IokLyvplZPs9t2nyy33gIYe+A6zMzwdYOdDpKjx+gaNOvADjdORFdQBhIiTEtkbwFH9Y4lopS1m/B56Z+9N2/HkNBAUcmvVI+r+eWlezsNwKdvR1dls1DZ2sLeh1pW3ZxcaH2uK1GE8bhOywSWWqgJCOLI09OudSmGoQaAwqi3sdWlW1YiFwppUulaf5oXeE9TEldF7Qbb1qbusHPA12klKkVlhlvmjapGtssX950E9IswCilfEAIMRPIllLOrNA+GugvpYwxvd8MvCCl3Gd639/0/lYhRE/AT0q5UgjxJpArpfzQ1G4oMEVK2a/Cul8GWgEPoXXB70WrwB4DTgEDgTjT9HullFFCiEdM7QdKKQsqrOsWYBJwM1p3/WdSym4V5pfHeaXPCJCnu7e9cqt61HxPFPOinmjoMCw80vYrZh+t+RfktTCx3SwAxOM9GjgSMzlLqzrJk9MbOBIz0VLrdj2X/VkDR2KpidvTGNc92dBhWNAN/pLoAR0aOgwLjTdp93h6zhzawJGYZUzR7iKX8dV6IEu9EEHaV2L6+D4NHIklrwXbWONVN89ZritD00/CJUYyXytLTj5Zb8nXvS2/vC7T3euqAiqlTBJCPAOsFkLogFxgjKlLvMxhIUTZ+5+Aw8B4IcSICm16SCljL7GZTaYbgnTACqDsIWH3oN1NXtEK0/Qrjl6WUu68zOx7qNo1/yXwLVq3vgC+lVIeBhBCTALWAnpgvpQyyrTM10AMsMtUpf1FSjkNWI2WfJ4B8oHygZZCiG1oia6LECIWeFhKufZKv4+iKIqiKNeGGgPagAlo5epnhem/Ar9eYl74JVa3oJrbvNTySCkbW5n2XKX3/Su93wxstrLcm5Xej7fSJpcKY0wrzVuNllRWnm51f5nuirdaOpFSXl+Xv4qiKIqi/M+7riqgiqIoiqIo/3aqAvovTUCFEHuAys9oGSelPNIQ8SiKoiiKoihm/8oEVErZ/cqtFEVRFEVR6p+qgP4P/09IiqIoiqIoSsNQCaiiKIqiKIpSr/6VXfCKoiiKoijXK51Q9T/1CSiKoiiKoij1SlVAFUVRFEVR6pG6CUlVQBVFURRFUZR6piqgiqIoiqIo9UhVQFUFVFEURVEURalnqgKqKIqiKIpSj1QFVFVAFUVRFEVRlHqmKqCKoiiKoij1SKfqf+oTUBRFURRFUeqXkFI2dAzK9UUdEIqiKMr/mnodlLkm5qV6+64d2uj963LAqeqCV6owbn++oUOwoOv9EWT90NBhWHIfw+msjxs6CgvN3Z8DQJ6c3sCRmImWr2r/Pt6jgSMxk7N2A3Ax9+sGjsRSqMtjFE4f3tBhWHB49VdKv7uvocOwYPPAIgDk/tcaOBIz0fkdAOSF6+ecIMK084Fhyf0NHIkl/b0LyZ88rKHDsOD07p8NHcL/JJWAKoqiKIqi1CN1F7waA6ooiqIoiqLUM1UBVRRFURRFqUc6oep/6hNQFEVRFEVR6pVKQBVFURRFUZR6pbrgFUVRFEVR6pG6CUlVQBVFURRFUZR6piqgiqIoiqIo9UhVQFUFVFEURVEURalnqgKqKIqiKIpSj1QFVFVAFUVRFEVRlHqmKqCKoiiKoij1SD2IXlVAFUVRFEVRlHqmKqCKoiiKoij1SIcaA6oqoIqiKIqiKEq9UhVQRVEURVGUeqTuglcVUEVRFEVRlP9ZQoihQoiTQogzQojJl2k3WgghhRBd6mK7qgKqVIuUkhk/RLH1SDIOdnpmPNSRto3cq7SLOp/JlPmHKCox0DfCj1fGtEUIwRe/nuTnrRfwcrUH4D93tKRfe3+KS428ufAwR89noRPwypi2dGvlU6MYt+46zfSP1mA0Grlz+A1MeKCPxfy9B84z45M1nDyTxMfvjGbowLYAHD+VwJvv/kFuXhE6veDxB/ty803tahRDZft3XWDORzsxGiWDh7fizgc6WcxfvfwYfyyLQqcTODrZMmlKX8KaeJIUn8Pjdy8lOMwDgJbt/Jg0pW+dxLRtfxzT5+3FaJCMHtyMCaMjLOYXlxh4+ZPtRJ1Jx8PNno9f7EuIvwsZ2YU8894Wjp5OY0RkU954rHudxFMd34x7lVsjepGck0HE22PrZZt/7zzPVx9uxmgwMmxEO8Y82M1i/m/LDvHrT4fQ63U4ONry3GuDaNTEm/27Y5j3+XZKSgzY2uqZ8EwfOnULq9PYbAY/iq5pZygpouT3T5GJ5yo1sMN21MsIjwCQRoyn91K6aSEA+huGou88DKQRigspWf0VMvVireKRUjJz/QW2ns3C0UbH9Nsa0ybAuUq7TzfHsupIKlmFBva92Nli3ppj6Xy5LQ4hoKWfEx+MaFqjOKYvPMjWgwk42Nkw87GutG3sWaXd0XMZTJn9N0XFBvp2DOTV+zsihOD9xYfYdCABWxsdYf7OzJjYFTdnO4pLjUydt5+j0enohOCV+zvSvY3fVce3be8Fpn+lnQ9GD2vFhHsszwfFxQZefn8jUadT8XBz4ONXBxES4Fo+Pz45h1sf/okn7+/Cw3d2uOrtWyOlZMaaGLaezsTRVseMEU1pE1h13/13w0VWHU4lq6CU/a90LZ++4mAKH66/gJ+rHQBju/kz+oar/2yssb3tMfQtu0JJEUU/f4SMP1upgT32Y19BeAWCNGI4voeSNd+Wz9ZH9MF20H2AxJhwjuIf36+TuOrC9XIXvBBCD3wJ3ATEAnuFEKuklMcqtXMFngb21NW2VQKqVMvWI8nEJOWxZsYADp3LZNr3R1j6Wu8q7d5adIS37m9Px6YeTPzv32w7mkLfCO1k9MBNTXhoqOWXys9bLwCwalo/0rKLmPDfv/n5td7odFfXPWEwGJn2/mq+/WIc/n5ujH5gLpF9WtKsiflEGBjgzsw3RjB/0U6LZR3sbXnvzZGEh3mTlJLNqPvn0LtHU9xcHa8qBmsxzXp/B+98cQvefs48+8AvdO8TTlgT8xdi/yHNuHlUGwD2bD3PvP/uZNpntwAQEOzG54tH1yoGazFNm72H+dNuwt/biTufX01kt1CamRJdgGXrT+PmYs+6OSP5Y2s0H323n09e6oe9nZ5nxnbkdEwmp2Iy6zSuK1mw6w++2LyMhePfqJftGQxGPn93I+99dQe+/q48OW4JPfs1pVET7/I2kUNbcdtoLQnYueUssz7ewrtf3IGbhyNv/3c4Pr4uRJ9JZfKkX1i6ZkKdxaZr2hnhFUjxrMcQQS2wHfo4xQterPo77F6JMeYI6GywGzsNXdMbMJ49gOHoFgwH1mjrat4Nm0EPUfLjW7WKadvZLGLSi/jzsQgOx+cxbU0MP45vU6Vd/+Ye3NvFj2GzjlhMj0kvZO6uBBbd3xp3RxvS8kpqFMfWg4nEJOay9uNhHDqTzlvzD/DT2wOrtHtr/n6mPdyFjs29mPD+drYdSqRvx0B6Rvjz3D0R2Oh1fPjDYeasOsELY9rz80Ytwf/tvSGkZRXy6HvbWPbOoKs6TxkMRqZ9voP5792Cv48zd076hcgbw2nWyHw+WLbmhPa3990Y/th0ho/m7eaT124qnz9z1i76dK3bi5mtZ7KISS9kzVMdOByXy1t/RLP0kaoX4ANaejC2mz9DPz9UZd6wtt68dnN4ncala9kVnU8QhR8+jC60FXYjJlH01bNV2pVsXY7x3GHQ22D/yEx0LbpgPLUP4R2E7YC7Kfz6eSjIBeeqBRMFgG7AGSnlOQAhxI/AcOBYpXZvA+8DL9TVhhs0BRdCGIQQByv8hAshxgshvqjUbnNZyVcIcV4I4VNpfpVlLrNNFyHELCHEWSHEP0KI/UKIR03zwoUQBZViut80z10IsdC03FnTa3cryx0zzbOtsM0pptL2SSHEENM0ByHE30KIQ0KIKCHEWxXaNxZC7BFCnBZCLBVC2Jmm9xVCHBBClAohRlf6vR4wtT8thHigwvTpQoiLQojc6nw+l7LxYBLDe4YghKBjU0+y80tIziy0aJOcWUhuQSmdmnkihGB4zxA2/JN42fWejc+hR2ttd3q72ePmaMPR81ef3ByOiqNRiBehwV7Y2dpwy+B2bNh60qJNSJAnrZoHVPnSaNzIh/AwLbHw93XDy9OZ9Iz8q46hslNRyQSGuBEQ7IatrZ6+g5uxe+t5izZOLnblrwsLShHXeFzQ4dNphAW6Ehrgip2tnpv7hLNhj2X1a8Oei4yI1C4UhvRqxK5DiUgpcXKwpXMbf+zs9Nc0Rmu2nTlIel52vW3vZFQiQaEeBIV4YGurp//gluzYbFl9cXaxL39dWFBSvu+at/LDx9cFgPCm3hQXGyguLq2z2HQtumE4vAkAGX8KHJzBpVKVr7RYSz4BjKUYE88hXE3Jc3GBuZ2tPSBrHdPGU5ncHuGNEIIOwS7kFBpIyS2u0q5DsAu+FY75Mj8fTGFMZz/cHbWaiLezbZU21bFhfzzD+zTSzlPNvcnOLyY5o8CiTXJGgXaeaqHFO7xPI/7aFw9A7/YB2Oi1r8UOzbxJTNOWPRuXzY3ttItZb3cH3JztOHou46piO3wymbAgN0ID3bS/vf7N2LDzvGX8O88zYnALAIb0bcKuf+KRUts/f+2IJjTQlWbhVSu6tbHxRAbD2/to+y7EVdt3OVb2XYgrvq5V9921om/Tg9IDGwAwXjyBcHQB10q/e0mRlnwCGEoxxp9BuGvfJzbdhlKy6zct+QTIy6qv0KtFJ0S9/QghJggh9lX4qXhFHAxU/BKINU0rJ4ToBIRKKX+v08+gLldWAwVSyo4Vfs7XwzbnARlAcyllJ2Ao4FVh/tlKMS00Tf8GOCelbCqlbApEm9ZlsRwQAYQAdwEIIdoA9wBtTdv6ylTyLgIipZQdgI7AUCFED9O63gM+kVI2N8X6sGn6BWA8sKTiLySE8AKmAt3RrmamCiHK/lJ/M02rlaSMQgK8zBXBAE8Hqwmov6e5jb+nA0kZ5jaLN55n+NQtvDr/EFl52gmuVagbG/9JotRgJDYln6iYLBLTLddbrfhSsgnwdzNv28+NpJSrT1gOR8VSUmogLKT2J/m0lHx8/V3K3/v4OZOWklel3e8/H+WRkT/w7ee7mfB8r/LpSfE5PH3fMiZPXMXRfxJqHQ9AUlo+gT7m7rUAHyeS0iyT7eS0AgJ9nACw0etwdbYlM6eoTrb//0Vqci5+/uauT19/F9JSql7D/frTQcbdPp+5n23jyRf7V5m/bcNpmrX0xc6u7jqbhKs3Mju1/L3MTjUnl9bYO6Nr3hXj+cPlk/Sdb8buia+xHTie0rVzax1Tcm4xAW7m5MTf1ZaknOpXMWPSCzmfXsjYhccZs+AY287WLFlIyigg0Mup/H2AlxNJlRLQpIwCy3OZl2OVNgDLN0fTt2MAAC3DPNiwL147TyXnERWdQUL61V2kJqXmE+hrPh8E+DiTlGp5PkhOyytvo/3t2ZGZXUh+QQlzlx7kyXF1MvTOcps5xQS4my+m/N3sSLKSgF7OuuPpjJh1mP/8dIqErLo5V+jcvJGZFY7zrFR0bpcZnuXgjL5Vd4xnDwIgfILR+QRj/9iH2D/xCboWnS+97L+clHKOlLJLhZ85FWZbq3qUX5UKIXTAJ8DzdR1XQyeg9UoI0RQtGXtNSmkEkFKmSCnfu8JyzYDOaCXoMtOALqZ1lpNSGoC/MV9BDAd+lFIWSSmjgTNAN6kp+0azNf1IoZVRIoFlpnnfASNM6z4vpTwMGCuFOARYL6VMl1JmAOvRkl2klLullJfNXipeHc2ZM8dqG2mlSFL5qLXaxtTonv7hrHs3khVT++LrYc/7S48DcEfvUPy9HLjz7e3M/DGKjs080euvvgpoPb6rW09yag4vTl3BzNeHo9PVwZ+GlaCsRXTrne2Yt2IM4yd1Z+n8AwB4+Tjx7aqxfLZoNI/850Y+fH0D+VYqSnUSk6jcpPYVsf/vrH4EVqrTw+/qyPerHuKRp/qweJ7l0KjzZ1OZ+9l2nn1lUN0GZ61Kfql9JnTYjnwew97fkZlJ5ZMN+1dT/NVjlGz8Dpved9U6pOqcHy7HYJRcSC9kwdiWfDCiKVNXR5NdWIOqsdW/ucoHeNXFKsf69crj2OgFt/XSurtH9Q8nwNuR0a/9xYzvD9KpuTc2VzlMqHp/e1aWE4LPF+5j/Kj2ODvWrDJ82bCsbfIq9t6AFh789UxHVj7enh5N3Hll5bkrL1QdVo5zealqvU6H/ZiXKd25Cpmu9boJnR7hE0zRnJcp/uFd7Eb9R+stUCqLBUIrvA8B4iu8dwXaAZuFEOeBHsCqurgRqaHHgDoKIQ6aXkdLKUde4+21BQ6VJZ+X0LRCTABPAZ7AQVNyCWiJpqldW6C8tCCEcECrRD5jmhQM7K6wvvLytqkSuh9oBnwppdxjGl6QKaUsrdz+Mq5YQr8c09VQWeYpjdu1C53FG8+zzDRGs124O4np5ipBYkYhvh4OFuvRKp7mNkkZhfiZ2vhUuMK+s28Yj326F9Cu8qfc07Z83pgZO2jkf/UniQA/NxKTzBXPpORs/HxdL7OEpdzcQiY+u5j/PBZJx4jQKy9QDd5+zqQkmatmqcl5ePle+nfrO7gZX723HQBbOz22pq7uZq19CQhxI+5CFs3b+NYqJn8fZxIqVF0SU/Pxq1Ax0to4kZCaT4CPM6UGIzl5JXi42lde1b+ar78LyUk55e9TknLx9rn0vhswpCWfztxQoX0OU1/4jZenDSEo1OOSy1WXvvPN6DtpYwGN8WcQbj7lX8XCzQeZm251OZtbnkSmJ2DY+5vV+caobdgOfaxGMS3Zl8SygykAtAtyJjHbfIGUlFOCn2v1kyV/VzvaB7tgq9cR4mFPuJcDMemFRAS5XHHZxevO8PMmLemJaOJlUZlMTM/Hz7PSecrL0fJcll6AX4WemxVbz7PpQDwLXu1XPqzCRq9jyriO5W3umbqRRgHVP78A+Ps6k1Chip6Ymoeft+Ux5e+jtQnwdTH97RXj4WrP4RPJrN12jg/m7iYntxidTmBvq+e+ETW7WXLJ34n8fEDbdxFBziRmFaHlGZCUXXxV+87Dydz2zhv8+Pivmt/QZtPjVmy6DQXAGHsK4eEDMdo84e6DzE6zupzdHc9gTI2ndMfK8mnGrFSMF0+A0YDMSEKmxKLzCcYYe6rG8dWl6+gxTHuB5kKIxkAcWo/tvWUzpZRZQHnpWQixGXhBSrmvthtu6ApoxS74suTzUuWXOi/LCCFeNY3brJjtV+6C34Z2gWz9QtE8vSxxTQMumCqVZW0qk6AlsaZu+xCgmxCi3eXaX+5XqcEyVzQ2MpwVb/ZlxZt9GdgpgF93xiKl5ODZDFydbMqTyzJ+Hg44O9hw8GwGUkp+3RlLZEd/AIvu+vUHEmkerJ3sCooM5BdpufaOqBT0OkGzoKs7sQNEtAni/MU0LsZlUFxSyh/rjhLZp2W1li0uKeXJl5Yy/OYODBvU9soLVFOLNn7EX8wiMS6bkhIDW9edoXufRhZt4i6Yuxr37oghKFQbRpCVUYDBoF0nJcZlE38xi4Dgq/9cKoto7k1MfA6xiTkUlxhYve08kd0tE+7IbqGs3KiNd1y7I4Ye7QOu+djU603LNgHEXcwgIS6LkhIDm9edpGe/JhZtYi+YxwDu2X6OENONXLk5hbz6zEoentSbdh2rfR14WYb9qyme9yzF857FeGo3+vYDABBBLaAoD3Krjke06TcWYe9E6bp5FtOFZ2D5a13zLsiMmg3vuLeLP7880o5fHmnHwBaerDqShpSSQ3G5uNjrrY71vJTIFp78HaNdQGbklxCTXkhopfPLpYwd3IyVMwezcuZgBnYJ5tdtMdp56nQaro62FsklgJ+nI86ONhw8rcX767YYBnYOAmDboUTm/XaCWS/0xtHeXJ8pKCol31SR3XEkCRu9oFmIG1cjoqUfMXFZxCZka397m88QeaPl+SDyxkasXKclSGu3nqNHxyCEECz+ZDgbF41l46Kx3H9HBBPGdKpx8glwb7cAVjwWwYrHIhjYypNfD6dq+y42B1d7/VWN9aw4XnTTyQya+FRvv1lTuvt3Cj+bROFnkyiN2oXNDdoNZLrQVsjCPMipepzbDr4fHJwo+X22xXTDsV3om5ieFODkhvAJxpheN0OZ/k1Mxa5JwFrgOPCTlDJKCDFNCHH7tdx2Q1dArUlDqzhW5AWkWml7tY4BHYQQOimlUUo5HZhejRt0ooBOZctB+biIDmg7DEyJqxAiEK1UfbuUchVXLm8jpcw0XVUMBT4CPIQQNqYDo0p7K2KB/pW2sfkKy1yVfu392HokmSFTNpkew2R+BMjIN7ey4k3tEUFTx0Uw5RvtMUx9InzL74D/8OfjnLiYjRAQ7P1/7N13eBTV+sDx79ndNNJJSKFD6F06SpcmFkBQriCKggiKBfSigoqigA1QEUGpNhTkSkcRUIpSpBh6CyUQII2EhJZky/n9sUuym0JL7iY/7/t5njzszDkz82Z2c/bMe84MpXjrMfujf1IuZjJ48jYMBkVYkDfvD26U9+A3wWQy8ua/uzP4+W+w2jS977+D6lFhfPLFb9SrXZa729Ziz4EzDB/1A+npGfy+6QhTv1zPygXP8vPa/ez4O5YLaVdYvMKe/H5vbE9q14i8wVGvz2gyMPTfrXnz+VXYbJrO99ekUlRp74oUAAAgAElEQVRpvv1iO9Vrl6FF28qs+HEfu/86g9FkwC/AixFj7R2LfX+f47svdmAwKoxGA8++2gb/wNtv2LPPk9HAG083Z9Bba7HZNL07VaN6xSA+/S6aetVC6NiiAn06V2fU5D/oMmQxgf6eTP53zuOfOg7+D5evmDFbbKzbdprZb3dyuYP+v2X+k+NoX6MxoX5BnJ6wjLErZjJnc/5ZvaJgNBl4blRHXh3+EzarpluPulSOCmXe9M3UqBPOne2iWLogml1/ncJkMuLn78Wot7sCsGTBbs6evsB3s7ZlD8u/N+1BgnNlmm+XLWYnhqimeD4zw/EYpqnZZZ6Dp5A1awT4h2Bq/TC25NN4Dp4MgHXHKqzRazA2vRdDlYZgs6CvXsa87ONCx9Q2KpCNMWncM30v3h4G3r2vSnbZg7P28ZPjruqPfjvNqv3nyTDb6Dg1mt4Ny/Bs23K0rhrA5hNp3P/FXowGxUsdKxBU6ta/nto1imBj9Dm6jPgZby8jE57OeVxQz9d+ZcnELgCMfbIxo2dsJyPLSpuGEdlzPd+Zt4sss40nJ24A7DcivT2oCefTMxn83kYMShEe7MP7w259Wr3JaOCN4a0Z9Jq9PejdtSbVK5fm03nbqVejDB3vrEyfe2ox6r3f6fL49wT6ezF5TBFP38hH2+pBbDx6gW5Td+PtYWB8j5wLrV4z9rJ4qL2t/mjNKVbuTSbDbKPD5F30bhzG8Pbl+WZbPL8fuYDJoAj0MTLhNh6flR/b4e3YajXD+99zwJxB1o9Tssu8n/+MjE+HowJC8ej4CLbEU3g/Z/87MG9ZjnX7amxHdqKrN8Z7xBegrZhXzYYrFws6nNuVlMcwAWitVwGrcq3L95EjWuv2RXVcVZzzvZRSl7TWfrnWhWN/zlRLrXW8Y57Bd0BtrbXNMQehqdY62WmbgY51w2/imAuxz8N8wzGM7g2c11r7KqUqAyu01nkuLZVSP2Efhh/nWH4TaKi17p17O6VUL2CU1rqVUqou9puGmgNlgXVAdeydarOj8+kD/Aq8r7VeoZT6EfiP1voHpdQMYI/W+nOnWOY5jrfIsVwa+1B+Y0eVXUATrXWK0zZ5znUBsofgSwpD60mQ9n1xh+Eq8BGOpk0u7ihcVA8cCYA+PL6YI8mhao6x/zus5Q1quo+ebp8Rc/rSjGKOxFUFv6FkjO9R3GG48B6zFMtXjxZ3GC5Mj38LgN75ejFHkkM1eRcAfarktAmqor09sM5/rJgjcWXs9zVXXr2nuMNwUeq9n+HWpi0XWnTyBLd1vhqFji6RQ1glpwvuoLVOwD5/cpVjSPtj4JFc8zb3KKXiHD/X/uIHOq2LU0qVL+AQg4EQIEYptRNYC7ziVB6V6zFMzzvWDwJqOB6ndAyoQc7d6bktAUoppdporfcDC7FnX38BnnXMJY0EfldK7cE+B2ON0yMOXgFGKqViHLHOBlBKNVNKxQEPAV8opfY7zlkK9huktjt+xl3rfCqlPnBsU8pxXt4qIGYhhBBCuIE7H8NUUhXrEHxBGTmt9VJgaQFllQvY3bybPGY68HQBZSeBfJ8+7ri7PN9UgGO7ek7LGvvw/LXl8cD4XNvsAVz/G4ycsuPk8+gkrfV27MPr+W0zB5iTz/pRwKj8thFCCCGEKA4lcQ6oEEIIIcQ/lsG9I/4l0j+2A6qU2gbkfnbMAK313vzqCyGEEEII9/jHdkC11i2KOwYhhBBCiNxK8txMdylxNyEJIYQQQoh/tn9sBlQIIYQQoiQqSc8BLS5yBoQQQgghhFtJBlQIIYQQwo1kDqhkQIUQQgghhJtJBlQIIYQQwo2UzAGVDKgQQgghhHAv6YAKIYQQQgi3kiF4IYQQQgg3Mkj+T86AEEIIIYRwL8mACiGEEEK4kdyEJBlQIYQQQgjhZkprXdwxiJJFPhBCCCH+17j1yfAnL37mtu/ayv7DS+RT72UIXuRxeUTn4g7Bhe+UNfxwZHhxh+HiXzU+45fYUcUdhotulT4A4Hj6p8UcSY6qAc8DcPrSjGKOJEcFv6EAqGEtizkSV3r6VjLG9yjuMFx4j1lKcv+7ijsMF6Hf/QlAcsa3xRxJjlDvRwE42KhWMUeSo3b0IfuLjOXFG0hu3veXqPMETudKuJV0QIUQQggh3EjJDEg5A0IIIYQQwr0kAyqEEEII4UYGuQteMqBCCCGEEMK9JAMqhBBCCOFGMgdUMqBCCCGEEMLNJAMqhBBCCOFGMgdUMqBCCCGEEMLNJAMqhBBCCOFG8n/BSwZUCCGEEEK4mXRAhRBCCCGEW8kQvBBCCCGEGxkk/ydnQAghhBBCuJdkQIUQQggh3EhuQpIMqBBCCCGEcDPJgAohhBBCuJE8iF4yoEIIIYQQws0kAypui2evZzDWbg7mTDK//xBbXIxrBQ8vvAa+gSEkErQNy/6tmFfMBkAFlcGr3yjw8UMZDGStmI314F+FjunozmR+nnkYbdM07lyONg9VcSn/e+1Zfp17hIAQLwCa31uBJl3LA/DN2F3EHU6jYu0g+o+9o9CxXHNwewI/Td+LzQYtu1Wk879q5FsveuNZ5r67nZc+a0vFGsEAnDmexsJPdpNxxYJS8NJn7fDwNBY6ph2bY5kx6Q9sNhvdetTh4YFNXMpX/mcfK37ci8Gg8C7lyfOj21OpamkO70/g0/G/A6CB/k81564OVQsdD8Bfm0/y+UfrsVlt3NOzHo880dylfPmi3SxduBuj0YC3jwcjX+9Epaoh7Nway6ypf2A2W/HwMDLkhTbc0bxikcR0I7MHjOG++neReDGV+u/0d8sxrzF1eQpDVBMwZ2Je8Qk6/niuCp549H4FFRQB2obt6HYsv38NgLFxN4xN7gFtg6wMzKs+RyefLnRMvo+9iGfDVuisDC5+MR7rySN56gSMmoQhKASMJsyHd3N57iR7HA4+3R/Bt/9wzj/dHX0prVDxbP0zho/fX43Nprm/1x0MGHSXS/kPX29l+eK/MRoNBAWXYvTb9xNRNgiAaVPWsnnjUbTWNGtZlRdf6YpSqlDxXBM+agx+rdtiy8jg3JuvkXHoQIF1y3/8OR7ly3OizwMAhA4dTtCDD2FNTQEgceoULv+xsVDxbPzzEOPfX4rNZuOhXi0YMqijS/n2nceY8MEyDh89x+T3+9Otc8PssrPnUnn9rR85l3ABpeDLzwZTvlzpQsXjrKSdq6KkKHxb/v+ddEDFLTPWbo4qU46rEwZiqFQbzz7Pk/Hx83nqmX//EVvMbjCa8H7mA2y1mmE9tB2PLv2xRG/AsnkFKrwi3kPGc/WdAYWKyWbVrJxxiMfeaUxAiDdfjtxGzRZlCKvo51KvXpsI7h1aK8/2dz1YCXOmjR0/xxUqjtwx/fjZHp55706CQn2Y9NwG6reKIKJSgEu9jCtmNi45TqVawdnrrFYb37y/iwGjGlMuKpDL6VkYjYUfsLBabUz7YCMTPnuA0HA/Xnj8R1q0rUKlqjlfGu271uDe3vUA2LrhBDOn/Mm7U++nUlRpPv36YYwmAynJl3mm3wJatqmM0VS4uKxWG1Pf+433P3+QMuH+PDtgPne2i6JS1ZDsOh271eL+PvYvvs0bjjF98gbe++xBAoJ8eOfjHoSW8eNETDKvDv+JBb8MKVQ8N2velpV8tn4RXw980y3Hu8YQ1QRVOpKs6UNRZWvg0W0YWfP+naeedesSbLF7wWDCs/84DFGNsR3bhXXfBqy7frHvq3pzTJ2exPzD24WKyaNhK4wR5Ul9qS+manXxe+Jl0sbmfR8uTn0DffUKAP4vjMezRQeytq6zx1I6DI/6zbAmxxcqFrB/piZN+IWPv+hPWHgAg/vNonX7GlSJKpNdp3qtCGbPH4y3jweLF+5g2pR1vPNhb/ZGn2Zv9Gm+XvQ0AMMGzuPvHbE0bla50HH5tm6LZ8VKHHugK971GxIxZiwnB/TNt65/x87YHOfKWcq3X5Hy9ZxCxwL28zRuwmLmfjGE8PBA+vT7hI7t61AtKiK7TmREMBPf6cucrzbk2f6V179n6OBO3NWqBpevZGIook46lLxzJYqe24fglVJWpVS0009lpdRApdRnueqtV0o1dbw+qZQKzVWeZ5vrHDNQKfW1UuqY4+drpVSgU3ldpdRvSqkjSqmjSqk3lONy13GcJEes+5VSi5RSpRxlbymltFKqmtO+RjjWXYv9EaXUXqXUHqXUL9d+D8e2Z5zOQ3enfbymlIpRSh1WSnV1Wj9HKZWolNqX6/crrZRa44h9jVIqOFd5M8d573Mz5+tGjPVaYdm+FgBb7EGUjx8qINdVrznT3vkEsFqwxcWgghxvodYob197bN6+6LTzhY7pzNE0SkeWonREKUweBuq1jeDQtqSb3r5qwxA8fYr2ijT2cCplyvoSGumLycNA43bl2Ls575frqq8O0fHhanh45vw5HtqZRNkqAZSLsn9MfQM8MRgL37gf2Z9I2QqBRJYPxMPDSLvO1dm64YRLHV8/z+zXGRlmrn2neHt7ZHc2szKtFNV3zeH98ZStEETZ8kF4eBhp36Umf64/lismr5yYrpqzs1HVa4URWsZ+kVE5KoSsLCtZWZaiCewGNsVEk3I53S3Hcmao0RzrHkcm+uwR8PYFv2DXSpYse+cTwGbBFn8c5e/o0Gddzann4YU9n104nk1ak7HJ3qm1xOxHlfJHBYXkqXet84nRiDK55j98BzzP5e8/B134eA7uO0v5CsGUKx+Mh4eRu7vVZdP6wy51mjSvjLePBwB165cjKdH+XiqlyMq0YDFbMWdZsVhslA7xLXRMAP7t7yZtxVIAMvbuxuAfgCm0TJ56yqcUpQcMJHnm9CI5bkH27DtFpQohVCgfgqeHiXu7NWLd+v0udcqXK02tGmUxGFz/4GOOxWOx2LirlX1Ux7eUFz4+nhSVknauippBGdz2U1IVR2RXtdaNnH5OuuGYs4HjWusorXUUcAKYBaCU8gGWAe9prWsADYE7gWectl/giLUukAU4X4btBf7ltNwHOODYtwn4BOigtW4A7AGGO9Wd4nQeVjm2qePYX12gG/C5Uupaz2ieY11urwLrtNbVgXWOZRz7MwLvA6tveJZukgoMRV9IzF7WF5JRgaEFb+Dti7FuS6xH/wbAvPobTE3uxmfsfLyHjCfrp2mFjin9fCaBoTmdlMAQLy6ez8xT78DmBD5/bgsLJu4mLSmj0Me9nrTkDILK+GQvB5XxIe286zHjYi6QmnSVei0jXNYnxV1CKZj+2mY+fGY96xYeLZKYkpMuUSY8JyscGu7H+aTLeeotX7iXJ3p+w+xPtzD05TbZ6w/ti+fph+cz7JHvGf5q+0JnPwGSEy8RFu6fvVwm3I/zSZfy1Fu6MJoBD8xh5qebePbf7fOUb1p3lGo1y+Dp+c8e2FH+Iej05OxlnZ6c07nMj5cvhurNsJ3ck73K2KQ7ns/MwOPugVhWzyx0TMbSZbCdz2kTbCmJGIPzdhYAAl6ZTOnpK7BlXCFrm70j7dm4NbaUJKynYvLd5lYlJaYTFpEz0hAWFkBSwsUC6y9fHE3Lu+x5hHoNy9O4WWUe6DSFBzpNocWdValcNf/f5VaZwsIxx5/LXrYkxGMKC89Tr8yzz5Py9Vx0Rt42Kvhf/amycCmRb43H4B+Qp/xWJCSmERERlL0cHhZEQsLNTX04GZtMgL8Pw0fMo+fDk3l/8nKsVtuNN7xJJe1ciaJXcrvGRcSRnWwCvOO0ehzQVCkVBfQD/tRa/wqgtb6CvZP4aj77MgG+QKrT6iVAD0d5VSANuJZ6U44fX0dGNQA4e4OQewA/aK0ztdYngBiguSO2jUBKAdt85Xj9FdDTqew54D9AYu6NnH6vIUqpHUqpHV9++eUNwoN8U18FZS0MBrweG41542L0eXv2z3hHB8zbf+Xq2/3I+HIMXv1fyX+ftyK/w+faZc3moYyY3YZnpraiaqPSLP54Xz4bFR2dX1BOMdlsmsUz9tFzSL081WxWzfF9KQx4tQkvTG7Nnj/Pcfjvm8/oXieo68Z0zf0P12fukgE8+Vwrvp+zI3t9rXoRfLGwH5989RAL5+0kK7Pw2cZ8Pzr5fB56PNyIb5Y9yeDn2vDdrG0uZSePJTPz0z8YMbpToeMp8W7l708Z8Oj1EtbtK9AXErJXW3euIuvzoZh/+wpT64eLIqh8Qso/pvT3R5LybA+UyROPuk3A0wufHo9xZdGsIojj2rHzibCANmb1ij0cOnCOfgNbARB3KoWTJ5JZ/OuLLFnzIjv/Okn0ztgiiSvfEHIF61WzFp4VKnHx97V5qqYu/J5j93XmRN+eWJKTCH/plULFcyvnKTeL1cqOv0/wykv3s2j+C8TFpfDT0u2Fisc1jnxWFuO5KmoKg9t+SqriiMzHadh5sRuOVweI1lpbr61wvI7GnmWsC+x03kBrfQzwU0pdu2Tqq5SKBs4ApYHlTtXTgdNKqXrAI8ACp/2YgWHYs6RnHbHMdtp2uGNofo7TsHk5wPmOgDjHuusJ11qfcxzzHBAGoJQqB/QCZlxvY631l1rrplrrpkOG5D9/znTXA3i/PAPvl2eg086jgsKyy1RQKDo9/2F0z4dHoJPOYNmY81Z7tOyGNdo+n8gWexA8PME3MN/tb1ZAqBdpyTkZz7TzmfiX9nKpUyrAE5OH/SPfpEt5zsYUnBEpCkGhPlxIyhnuvJB0lcDS3tnLmVctnDt5kc/+/QdvD/iVkwdTmfnmNk4dSSUo1JtqDULwC/TC09tEnWbhxB29UOiYQsP8SErIyS4mJ1wiJLTg4cV2XaqzZf2JPOsrVimNt48HJ4/ldz10a8qE+5HolJ1KukFMHbq6DtEnJVxk7MvLeWVcV8pWCCpwu//PjE264zl4Cp6Dp6AvpqACckYcVEAo+lL+74Pp3mfRKeewbl+eb7lt/yYMNVrcVkzenR8kaMI8gibMw3YhGUNITptgKB2G7UJywRubs8ja9QeeTdpgDC+HsUxZgiZ+RfDHizCULkPQ+DmowNu/mSUsPIDE+JzpEYmJ6YSG+eWpt33rcb6a9QcffNI3O3O+4bdD1K1fjlKlPClVypOWd1Vj/54ztx1LcN9+VFmwmCoLFmNOSsQjIjK7zBQegSXJNTfg06AR3rXrErVqHZXmfodXpcpUnGW/gcyach5sNtCaCz/9iHe9+rcdF0BEeCDx8TntSkLiBcLCbi5TGBEeRJ1aZalQPgSTycjdHepx4NDtnyco2edKFL3iHoLv5VhX0KSfwk8Gsl+aF5T30dcpdz7+Aq11IyACe2cy94z/H7APm/cEsntaSikP7B3QO4Cy2IfgX3MUTweigEbAOWCSU1wFxXGrPgZece583y7Ln8vI+GgoGR8NxbrvT0zN7JkmQ6Xa6KuX0el5vwA97hmI8vYla4nr3BxbaiLG6vY7zVVYRTB5wqXCda7KVg8g5ewVUuOvYjHb2LcxnlrNXYfNLqbkdFAP/5VEmQpFM6+rIBVrBpF05jLnz13GYraxa8MZ6rXKGWr38fVgwqJ7GPtNF8Z+04XKtYN5alwLKtYIplbTMM6eSCcrw4LVaiNmbzIRlfyvc7SbU6NOGGdPpRF/Jh2z2cqGNUdp2bayS50zp3Lei7/+OEm5ivaLg/gz6Vgt9iG2hHPpxMWmEl628DHVrBPBmdOpnDuThtlsZf2vh7mznevd9XGncgYdtv1xnPIV7R3NSxczGPPCEgYNb029Rje6Tvv/y7pzFVmzRpA1awS2I1sxNugAgCpbAzIvw6XUPNuY2vVHeZXC8qtrZlEF53ypG6o3Raeey73pTclY8xMXRg/kwuiBZO7YiHcb++wgU7W66KuX0BdyXZR6+eTMCzUY8WzUCuvZWKynj5PyzH2kvtiH1Bf7YEtJ4sKYJ9Fpt39xU6tuWeJOpXA2LhWz2cq6X/bTup3rEyiOHDzHB++s4v1P+hLsNMczPCKQ6J2nsFhsWMxWonfGUqnKdaYY3UDqgvmc6NuLE317cen3dQTe1wMA7/oNsV26iCXZdWTjwo8/ENOlLce6303sE/3JjD3JqcGPAbjMgfTv2InMmMJNzalftwInTyVzOu48WWYLK3+JpmO7uje9bVr6VVJS7Be02/46SrWqeYfIb0VJPldFTeaAlpy74M8DuWbRUxq4ziX0TdsP3KGUMmhtf96Hsv8fWA2Bg0A40NZ5A8dQ+iWt9UXn4QittVZKLcc+rP2e0ybLgQ+BHVrrdKdtGjm2O+bY70IcQ/ta6+zxMKXUTGCFYzEOqOC07/LceNg+QSkVqbU+p5SKJGe4vSnwgyOeUKC7UsqitV5yg/1dl/XAXxhrt8BnzFeQlUnmDx9ll3m/PIOMj4aiAkPx7NIfW8IpvF+yd0Atm5Zi2fYzWUu/wKvvSEztHgQg6/sPCxMOAEajge5Da/LN2F3YbJo7OpUlrJIfv30bQ9nqAdRqEcbW5ac4vC0Jg1Hh4+9BzxdyGtrZr2wnOe4yWRlWJg3cSI/n61Ct8e1/6VyLqffwBkwfvQWbTdOya0UiKwew6quDVKgRRP1WkQVuW8rfk/YPRjHpOftjQ+o0D6dui4gC6990TCYDw0a14fXnl2G1aro8UJtKUSF8PWMbNWqH0bJdFZYv3Mvff53GZDLgF+DNS2PvBmD/7nMsnLcTk8mAMiiefaUdgUE+NzjizcX03KiOvDr8J2xWTbcedakcFcq86ZupUSecO9tFsXRBNLv+OoXJZMTP34tRb9vvzVuyYDdnT1/gu1nbsofl35v2IMGlSxU6rhuZ/+Q42tdoTKhfEKcnLGPsipnM2Zx/prEo2WJ2YohqiuczMxyPYZqaXeY5eApZs0aAfwim1g9jSz6N5+DJAFh3rMIavQZj03sxVGkINgv66mXMyz4udEzm6C14NmpF8OSF6KwMLn0xIbssaMI8LoweiPLyJmDk+ygPDzAYMe/fSca6QjVFBTKZDIx4rRsjh83HatPc17MhVauFMXPaemrVjaRN+5pMm7KOq1eyeP3f/wEgPCKADz79Fx0612bXXyd5rM8MlFK0uDOK1u3zf3zarbq0aQO+rdsStfxX+6OFxo7OLquyYDEn+va6ztYQ9uLLeNWsDVpjPnuG+HfHFioek8nIm6/1YvCwmVhtmt49m1G9WgSfTPuFenUrcHf7uuzZd4rhI74iPf0Kv284wNTPf2Xl4n9jNBp4ZeT9PD7kC9CaunXK81Dv28um56eknStR9FRB83T+awdU6pLW2i/XunBgG9BSax3vuIP8O6C21tqmlDoJNNVaJzttM9CxzvmmnoKO+RP2YfhxjuU3gYZa696Om5D2A0O01msdyz8Cq7XWU3MfRyk1HgjQWj+nlHoLe0f1I6XUv4AjWutdSqn1wMvYO447gQZa6ySl1DtAKa31S9c6jI59jgBaaK3/pZSqC8zHPu+zLPabiqpfy2IqpSoDK7TW2RMHlVIfAue11u8ppV4FSmutR+U6B/Mc2y26wenSl0d0vtEpdSvfKWv44cgN32a3+leNz/gldtSNK7pRt0ofAHA8/dNijiRH1QD747lOX7ruLBC3quA3FAA1rGUxR+JKT99KxvgexR2GC+8xS0nuf9eNK7pR6Hd/ApCc8W0xR5Ij1PtRAA42yvuIt+JSO/qQ/UXGf/+i6JZ431+izhNkn6uie4bUTbhoXuy2zpe/Ry+3/m43q0RkQLXWCUqpF4BVjuzkJeCRaxlLhz1KqWvLC7EPZw9USjnfcNNSa53fgxwHAVOVUjHYP2RbHOvQWl9VSvVwlE8DjMA3gPMjnvoqpVpjn7IQBwzM53f4IZ91Z5VSbwMblVJmINZp2w+UUo2wD6+fBJ52bLPfkSk9AFiAZ506n98D7YFQpVQcMFZrPRt7NnahUmoQcAp4KJ9zIIQQQghRIri9A5o7++m0fimwtICyygXsbt5NHjMVePQ65Xuxd+zyK5tX0HG01m8VsL690+sZ5HMTkNa6wCeva63HA+PzWf9IAfXPA3cXtD9HnYHXKxdCCCGEcJcSkQEVQgghhPhfYSjBj0dyl39UB1QptQ3wyrV6gCPDKYQQQgghSoB/VAdUa110t+AJIYQQQvwXqBL8eCR3kTMghBBCCCHc6h+VARVCCCGEKOlK8gPi3UXOgBBCCCGEcCvJgAohhBBCuJGS/J+cASGEEEII4V6SARVCCCGEcCOZAyoZUCGEEEII4WaSARVCCCGEcCOZAyoZUCGEEEKI/1lKqW5KqcNKqRil1Kv5lI9USh1QSu1RSq1TSlUqiuNKBlQIIYQQwo1KyhxQpZQRmAZ0BuKA7UqpZVrrA07V/gaaaq2vKKWGAR8AfQt77JJxBoQQQgghhLs1B2K01se11lnAD0AP5wpa69+11lcci1uB8kVxYMmACiGEEEK4kTv/L3il1BBgiNOqL7XWXzpelwNOO5XFAS2us7tBwM9FEpfWuij2I/455AMhhBDif41y58E0v7vtu1bRocDfTSn1ENBVaz3YsTwAaK61fi6fuo8Cw4F2WuvMwsYlGVAhhBBCCDdS7kz1XL9rHQdUcFouD5zNswulOgFjKKLOJ0gHVORDJ88p7hBcqNAnsS54vLjDcGHs+xWWOf2KOwwXpifnA2D79dlijiSHocs0ADLG97hBTffxHrMUKFkxgT0uNaxlcYfhQk/fCtY1xR2GK2NnAPT20cUcSA7VbIL9xdWlxRuIMx/751snzSrmQFypMoO5kPljcYfhIsjroeIOoThtB6orpaoAZ4B/AS5fbkqpO4AvgG5a68SiOrDchCSEEEII8T9Ia23BPqy+GjgILNRa71dKjVNKPeCo9iHgB/yolIpWSi0rimNLBlQIIYQQwp20zX3HusHsVq31KmBVrnVvOr3u9N8ISzKgQgghhBDCrSQDKoQQQgjhTu7MgJZQkgEVQgghhBBuJRlQIYQQQgh3kgyoZECFEEIIIYR7SQZUCCGEEMKdJAMqGVAhhBBCCH7G73wAACAASURBVOFekgEVQgghhHAnm2RAJQMqhBBCCCHcSjKgQgghhBDuJHNAJQMqhBBCCCHcSzKgQgghhBDuJBlQyYAKIYQQQgj3kgyoEEIIIYQ7SQZUMqBCCCGEEMK9JAMqbsqmrccZ//E6bDYbfe5vyJABLV3Ks7IsvPLOSvYfjico0IfJ43pQPjIQs8XK6xN/4cCReKxWGz261ePpx1oBMO+H7SxavhulFNWjyjBxdHe8vG7/I6m1ZsKqWDYeTcXHw8iEXlHUKeubp97Ha0+xLDqZtAwLO19vnqd89f7zjFhwlIVP16NeOb/bjudaTBPXnWbjsXR8PAyM716ZOhGl8tT7ZOMZlu07T1qGlR0j78he/9660/x16iIAGWYbKVcsbH2xUaFjmvCfI2zcn4y3p5EJj9ahboWAPPX2n0rntW/3k2m20bZuKKN710ApxaG4i7y14BBXMi2UC/Hhw8fq4edT+KbE1OUpDFFNwJyJecUn6PjjuSp44tH7FVRQBGgbtqPbsfz+NQDGxt0wNrnHnlXIysC86nN08ul/ZEzXM3vAGO6rfxeJF1Op/07//+qxnG3cdIDxExdhs9p4qM+dDHmqi0v59h0xTJi4iMNHzjL5oyfo1jXnM/7BR0vYsGEfNq25q1Utxozug1LqtuLQWjP+m91sjI7H28vIxCFNqVslOE+9fSdSee2LHWRmWWnbKIIxAxqilOKTH/ezbtdZDEpROsCLiU83JTzYh9krDrN8s/29s9o0x86ks3n6/QT5ed5SfBv/PMz4D5Zis2ke6tWcIU92cCnfvvM4Ez5cxuGj8Ux+rx/dOjfILqvd+BVqVIsAIDIyiBmfPHGrpyfbpq0nGP/JOmw2TZ/7GjBkQAuX8qwsC6+8u4r9hxMICvBh8rj7c9rz91Zz4EiCoz2vy9OO74LRE35m/ebjhASXYvk3tx/bNVv+OMLk91dhs9l44MEmPD6onUv5/K//ZOlPOzAZDQQF+/L6uF5ElrW/1/HnLjD+rcUkxqeDginTHqNsubyfA1G8JAMqbshqtTFu0hpmTnqIFd8NZuXaA8ScSHaps2jFHgL8vfl14dM83rcpkz5fD8Avvx3GbLaw/JtB/GfOQBYsjSbuXBoJSRf5ZtFOFs15nOXfDsJms7Fy7cFCxbnx6AViz1/llxca8fYDVXh7+fF863WoGcyCp+vlW3Y508q3W+NpUL5wHc9rNh1PJzYlk5+H1OWtrhUZ92tsvvXaRwXyw2O186x/9e4K/PREHX56og79m4TRqUZQoWPaeOA8sYlX+OXNO3n7X7UZt+BQvvXeXnCItx+pzS9v3kls4hU2HTgPwBvfH2TkA9VYNroVnRqUYfa6/H+nW2GIaoIqHUnW9KGYV03Do9uwfOtZty4h64tnyZo1AkP5WhiiGtvX79tA1swXyJo1AsuWxZg6PfmPjOlG5m1ZSbepI/7rx3FmtdoY9+5CZn3xDCuXv86KVTuJiTnnUicyMpiJEwZw371NXdbv+vs4u/4+zrIlo1mxdAx798Xy1/ajtx3Lxt3xxMZfYvWkrowb1Ji35/2db7235/7NuEGNWT2pK7Hxl9i0JwGAQffWYNnEziyZ0In2d0Ty+WJ7mzTovposmdCJJRM6MeLhujSrXeaWO59Wq41xExcza9ogVv70Eit+iSbmWIJLnciIICaO68t99+S9yPT28mDpwhEsXTiiUJ1Pq9XGuMlrmPlRH1Z8+yQr1x7Mpz3fa2/PFzzF432bMGn6BuBae25l+ddP8J/Zj7Fg6W7izqUB0Kt7PWZO6nPbceWO8cMJy/l4+mP8sOR5fv15L8ePJbrUqVErkq++H8Z3/3mOjp3r8tmU1dllb49ZxKMD27Bg6QvMnT+U0qXzJiKKnc3mvp8SSjqgBVBKaaXUJKfll5VSbymlxiiloh0/VqfXzxewn7eUUmec6kUrpYKUUu2VUmlO69Y6bfOYUmqfUmq/UuqAUuplx/qHHOtsSqmmTvU9lVJzlVJ7lVK7lVLtncrWK6UOOx0n7FbPxZ6D56hYPogK5YLw9DDS/e7arNvk+iWxbtNRena3d+q6tq/Flp2xaK1RCq5kmLFYbGRkWvDwMOLna2+4rVb7OovFxtUMC2Ghhev0/XYolR6NyqCUomEFfy5mWEm6mJWnXsMK/pTxz//L49N1pxnUuixeptvLwOSJ6egFHqgXYo+pnB8XM60kXTLnjamcH2X8PK67r1UHUuheu3ThY9qbRI/mkSilaFQlkPSrFhLTMl3qJKZlcinDwh1VglBK0aN5JOv2JgFwIvEyzarZO8J31gphze7EPMe4VYYazbHu+R0AffYIePuCX66MhSULW+xe+2ubBVv8cZR/iH0562pOPQ8vQP8jY7qRTTHRpFxO/68fx9mevSepVDGUChVC8fQ0ce89jVn32x6XOuXLhVCrZjkMBte/K6UgK9OM2WwhK8uC2WIlNCRvNv5mrdt5jh6tK9k/29VCSL9sJjH1qkudxNSrXLpq5o7q9r/LHq0rsXbHWQD8SuX8DV7NtJBfInblljjubVXhlmPbs+80lSqEUqF8CJ4eJu7t2pB16/e71ClfrjS1akRiuM0M8E3FcfAcFcsH57TnnWqx7o8Ylzrr/oih5z11AejaviZbdp5ytOeKK1ed2nNTTnverFEFAgO8iyTGA/viKF8xhHLlS+PhYaJzt/ps/N01QdG0eVW8fezHrtegAokJ9s/98WOJWKw2WrSqBkCpUl7Z9UTJIkPwBcsEHlRKTdRaZ18eaq3HA+MBlFKXtNY3Mx46RWv9kfMKxxDTJq31fbnW3wO8CHTRWp9VSnkDAxzF+4AHgS9y7f8pR2z1HR3Mn5VSzbTOnuXcX2u94ybizFdC0kUiw3K+FCLC/Nm93zXDkZh0icgwfwBMJgP+vl5cSLtK1w41+W3TUdr0+IyMDAuvPt+RoAAfAJ58pDkdH5yOl5eJu5pVoXWLKrcboj2G9CwiAnMamvAATxLSswrsbOZ24Nxl4tOzaF8zmLl/ni1ULNkxXTITEeAUk78nCRezbtjZzO1sWiZxaZm0qORf6JgSLmQSEZzzRRER5EViWiZhgV45cadlEh6UUyc8yIuEC/ZOavVIP37bm8TdDcJY/XcC51IzCh2T8g9Bp+dkYXR6sn3dpdT8N/DyxVC9Gebty7NXGZt0x9jiAZTRg6xvX/9HxlQSJSSkERGR0zEPjwhmz56TN7XtHY2q0qJ5dVq3G4PWmkf7tSUqKuL2Y0m9SmSIT/ZyRGkfElIzCAv2caqTQUTp3HVyOqlTFu5j6R+n8C/lwVej27rs/2qmhT/2xPPG47c+DSYhMY2IiMDs5fDwQPbsvfkpGZlZFh7s9wkmo5EhT7SnU8f8R3FuGIdTWw0QUcaf3Qfya8/tbb69Pfd0tOc1+O2PGNr0/Nzenj/XIbs9L0qJCemEh+ecq7DwAPbvjSuw/rLFO2nVujoAp2OT8ff34ZUR8zl7JpVmLaJ49sUuGI0lLN8mNyFJBvQ6LMCXgHvHs+A14GWt9VkArXWG1nqm4/VBrfXhfLapA6xz1EkELgBN86mXL6XUEKXUDqXUji+//DJvhXwSN7kv0LXOv9LeA+cwGAxsXPosaxc9zdzvt3P6zAXS0jNYt+koa38cysalz3I1w8yy1fvz7uMWFBDCTbHZNO//fJJRXSsWKoa8MeUN6naSG6sOptKlZjBGQ+EzI/nGdDN1HJXG96vD/E1x9P5gG5czrHgURcOe30nJ7w0FUAY8er2EdfsK9IWcIUzrzlVkfT4U829fYWr98D8zphLoZj5PBYmNTeLY8QQ2/PYuG38fz9ZtR9i+I+bGGxYYTN5Ved7GG/xNjni4Hus/7c59d1bg2zXHXOr9/vc57qgRcsvD7wUc9pbagt9/fo2f5r/ApImPMOHD5Zw6ff6WY7AHcuM4rt+eKzYuGcbaH59i7g/29twdCpoX/POKaA7uP8OjA9sAYLHYiN51kudf6sbc+UM5E5fCyqW73BKjuDWSAb2+acAepdQHhdzPCKXUo47XqVrrazPP2yiloh2vf3RkV+sBO29x/7uBHkqpH4AKQBPHv385yucqpazAf4B3da7WRWv9JfbOtn0xeY7LzsPD/DmXmDOsF594Mc9wub3ORSLCArBYbFy8nElQgDcr1hygTcsqeJiMhAT70rhBOfYdOodSivJlAykdbL8hp3O7Gvy99wwPdK17S7/4/G3x/LjTPgRcv5wf8Wk5Q+4J6VmE3WT283KWlaOJV3l87gEAki+ZeXb+Yab1q3nLNyLN35XIot32zFm9CF/i051iuphF2G18ef18MIXXO99+5/i7jadZtPmMPaaKAcQ7ZS3jL2RSxin7CRAe5E3ChZw6CRdyMqRVI3yZ/ax9nuOJxMts2O86f+xmGZt0x3hHZwBsZ2NQAaHZ340qIBR9KSXf7Uz3PotOOYfVKdPozLZ/Ex7dhv5jYirpIiKCiI/PyQonxKcSFhZ4nS1yrFm7m4YNK+Pra/9stWlTl+jdJ2jWtNpNH/+7Ncf48fcTANSvGsy58znZzPiUq4QFuQ4Lh5f2IT4ld528Wbz77qzA0I8283zvOtnrVt3m8DtARHgg8fFp2csJCWmElbn56QbhjnNaoXwIzZtW5cChM1SsEHLLcYSH+XEu8WL2cnxSQe15OhFh/o72PMvRnh+kTQun9rx+OfYdiqdCucLPTXcWFh5AQkLOuUpMSCe0TN7Rn7+2xjBv5gamzxmEp6fJsW0gNWtFUq68fbpSu4612bcnjgeKNMIiIBlQyYBej9Y6HfgayHd+5y2YorVu5Phxvu1xk9P68YXY/xwgDtgBfAxsxp7BBfvwe32gjeNnQL57uI76tSKJjUsl7uwFssxWVq07SMfWrl8QHVtXZ8mqfQCsXn+Ilk0qopQiMjyArY75oFeuZrF7/1mqVgohMjyA3fvOcjXDjNaaLTtiqVrp1hvTfi0iWPxMAxY/04C7awWzNDoJrTW7T1/E39t408Pv/t4mNr/alLUjG7N2ZGMalve7rc4nQL/GYdk3Dt1dI4hl+87bYzpzCT8v4y0Pv584n0F6hpVG5W5/In3/thVY/GpLFr/akrsbhLH0r3NorYk+kYa/t8ll+B0gLNALX28T0SfS0Fqz9K9zdKxfBoDzjnm1Nptmxi8n6Nu63G3FZN25iqxZI8iaNQLbka0YG9j/NFTZGpB5GfIZ6ja164/yKoXl11ku61VwZPZrQ/Wm6NRzuTf9fxtTSVe/XiVOxiZxOi6ZrCwLK3/eRccODW68IVC2bDDbt8dgsVgxm61s336UqKq3NgTfv3NU9g1Cdzcpy9I/7O1NdMx5/Et5uAy/A4QF++Dr7UF0jP3vcukfsdzdxP5enYzP6Zj9tuscVSJzOj0Xr5jZfiiJuxuXvaX4rqlftzwnTyVz+kwKWWYLK1fvpmO7OjfeEEhLv0JWlr1JT0m9zK7ok1SrGn57cdSKJPa0U3u+9hAd78rVnt8VxZKf7SNSq9cfpmVjp/Z816mc9vzAOapWKvy89Nxq1y3H6djznI1LwWy2sOaXvbRtX8ulzuGDZ3lv3FI+/LQ/pUNy2uk69cqRnp5BasplAHb8dZwqUWWKPEZReJIBvbGPgV3AXDcdbz/2DOZvN7uB1tqC01QBpdRm4Kij7Izj34tKqflAc+yd6ptmMhl4Y0RnBo1ciM2q6X1ffapXLcOnMzdRr1YEHdtUp899DRj1zgq6PPwFgQE+TH7bfr3Z78HGjJ6wivsfnY0GHuxen5rV7PdBdelQkwefmIfJaKB2jXD69mh4K2Hl0bZGEBuPXqDbx9F4exgY3ysqu6zX53tY/Iz9S/Gj1bGs3HueDLONDh/tonfjMgzveHtZjRvGVDWAjcfSuOfLfXibDLzbvXJ22YNzD/DTE/YvoI9+j2PVgRQyzDY6TttD74ahPNva/kW36mAK99QOvu1H0+TWrm4IGw8k03XcZrw9DEx4NCfr3Ou9rSx+1f5YlbF9a2U/hqlN7RDa1rFfIKzcGc/8jfb5WJ0bluHBlrf3hezMFrMTQ1RTPJ+Z4Xjk0dTsMs/BU8iaNQL8QzC1fhhb8mk8B08GwLpjFdboNRib3ouhSkOwWdBXL2Ne9vE/MqYbmf/kONrXaEyoXxCnJyxj7IqZzNmcf1a2qJhMRt4c8zCDn5qG1abp3asl1atH8snUFdSrW5G7OzZgz95Yhj8/k/T0K/z++16mfraSlctfp2uXO9i69Qj395yAQtGmTW06dqh/27G0axTBxt3xdHlptf0RY0NyZiL1HL2WJRM6ATD2iTsY/eUOMrKstGkYTtuG9k7vpAX7OHnuEkpB2dBSvP1E4+zt1+w4w131wynlfXtfmyaTkTdf7cHgYbOw2mz07tGM6tUi+OTz1dSrU56729dlz77TDB/5tf08bTzI1OlrWPnTSxw7nsjYd39CGRTapnnqyQ5Ui7q9DqjJZOCNkZ0YNHIRNpuN3vfWp3rVUD6d9Ye9PW9dzdGer6RL35kEBngz+a37Aej34B2MnvAz9w+Y62jP62W35yPHLmd79GlSL1ylXa/pPDfoLvrcd3MXIvmdq5dH38fzw77CZrVxf88mVK0WzhfT1lK7TjnadqjN1Mm/cOVKFqNf/gGwZ+I/mvooRqOB51/qxvCn5qA11KpTlp69b3pGmvtIBhSV71wPce0GIz/H6w+AfwFztNZv5VfnOvt5C7iUz01I7bHP9cx9E1J3YBxwn9Y6XinlBTyttf7Uqc56x7Y7HMulsL+Xl5VSnYE3tNZtlVImIEhrnayU8gC+B9ZqrWdcJ+Q8Q/DFTYU+iXXB48Udhgtj36+wzOlX3GG4MD05HwDbr88WcyQ5DF2mAZAxvkcxR5LDe8xSoGTFBPa41LCWN67oRnr6VrCuKe4wXBntUyT09tHFHEgO1WyC/cXVpcUbiDMf++dbJ826QUX3UmUGcyHzx+IOw0WQ10Nw81OXi0ba9+7rfAU+4t7f7SZJBvTmTAKGF2J75zmgAD0Lqqi1XqWUCgfWKnvKS2MfYkcp1QuYCpQBViqlorXWXYEwYLVSygacIWeY3cux3gMwAmuBmYX4PYQQQghRSFpb3XasEtn7RDqgBXLObGqtE4A8/33NjbKfjjpvAW/lU3QSWF/ANnPJZ8hfa70YWJzP+pNAzXzWX8Y+nC+EEEIIUWJIB1QIIYQQwp1K8P9Q5C7SAS0iSqkxwEO5Vl97tJIQQgghhHCQDmgRcf4fkoQQQgghCiR3wctzQIUQQgghhHtJBlQIIYQQwp0kAyoZUCGEEEII4V7SARVCCCGEEG4lQ/BCCCGEEO4kQ/CSARVCCCGEEO4lGVAhhBBCCHeSDKhkQIUQQgghhHtJBlQIIYQQwp3kv+KUDKgQQgghhHAvyYAKIYQQQriTzAGVDKgQQgghhHAvpbUu7hhEySIfCCGEEP9rlDsPps9+5rbvWlV2uFt/t5slQ/AijyMXPiruEFzUCHqZoy3qFncYLqpv209Mq3rFHYaLalv2AXCiQ8NijiRHld93A2D56tFijiSH6fFvAUjuf1cxR+Iq9Ls/wbqmuMNwZeyMGtayuKNwoadvBSD1yXbFHEmO4DkbANAnPijmSHKoKqPsLyyrizeQ3ExdeezXJ4s7Chdfd5lT3CH8T5IOqBBCCCGEO8kcUJkDKoQQQggh3EsyoEIIIYQQ7iTPAZUMqBBCCCGEcC/JgAohhBBCuJPMAZUMqBBCCCGEcC/pgAohhBBCCLeSIXghhBBCCHeSIXjJgAohhBBCCPeSDKgQQgghhDvJY5gkAyqEEEIIIdxLMqBCCCGEEO5k08UdQbGTDKgQQgghhHAryYAKIYQQQriTzAGVDKgQQgghhHAv6YAKIYQQQriTzea+nxtQSnVTSh1WSsUopV7Np9xLKbXAUb5NKVW5KE6BdECFEEIIIf4HKaWMwDTgHqAO8IhSqk6uaoOAVK11NWAK8H5RHFvmgIpbtnPLaWZO3oLNpun8QE0eeryRS/nPPx1g5aIDGAwKbx8Phr/WhopVgwE4cfQ80977gyuXszAYFJPn9sTTq+g+hmVGvkapO9uiM66S8M4YMg8fLLBu5Ief4VGuPKf69QSg9NPP4demA2iNJfU8CePGYE1OKnRMoSNeo9SdbdAZGSS+M4bMI9eJ6YOpmMqW5/SjvewxDRmOb5uOYLNhTU0h4d2iian0c69QqkVrdEYGSe+/QdbRQwXWDXv3EzzKlufMk70BKNWuM8EDh+FRsQpnh/Un68iBQsejtWbimlNsPJaGj8nA+PurUCfCN0+9T9bHsWxvMmkZVnb8u4lL2S8HUpi26QxKQc2wUnzYM6rQcfk+9iKeDVuhszK4+MV4rCeP5KkTMGoShqAQMJowH97N5bmTXP6XE5/uj+Dbfzjnn+6OvpRW6Jg2bjrA+ImLsFltPNTnToY81cWlfPuOGCZMXMThI2eZ/NETdOt6R3bZBx8tYcOGfdi05q5WtRgzug9KqULHdD2zB4zhvvp3kXgxlfrv9P+vHis3n37P41G/BTorkyuzJ2I9dTRPHb8RH6CCQlAGI5Yje7jy7cegbfg8NBSPRneiLRZsSWe5Mvs99NVLtxzDph1xjJ++FZvNRp9uNRnSt6FLeVaWlVc+2sD+o8kEBXgz+bUOlI/w589dZ5g0Zztmiw0Pk4FRg5vTslFZ+zZmK+98voW/9pzDoBQvDmxC19ZVbuscbdx0gPHv/WT/PPVuxZCnOruUb98Rw4T3frJ/nj58PPvztHXbESa+vzi73vETCUz5aCCd7m5wW3HkVj+kHo/W6odBKTbEbWLFyVV56jQPb0avqB5oNKcvnmb63i8BeLh6HxqVscex9NhytiVsL5KYilTJuQu+ORCjtT4OoJT6AegBODfsPYC3HK8XAZ8ppZTWulC/hHRAxS2xWm3M+PBP3pnanZAwX0YOXEKLNpWyO5gA7bpU454H7RdQ2zbGMvuTrbz9yT1YLTYmv7WekWPbU6VGCOlpGRhNRZeEL3VnGzwqVCK2zz1412tA2Kg3OT3okXzr+rbvhL56xWXdhW/nkPLFVAACH+5PyKBhJL4/rnAxtWqDR4WKnHqoO151G1Bm1BvEDe6Xf0ztOmHLFVPqt3NJ+fIze0wP9af0k8NI+qBwMfm0aI1HuYrEPXo/XrXrEzLidc4982j+8be5G53hGpP5RAyJb44gZOQbhYrD2aZjacSmZPLz0PrsOXuZcb/E8sPA3Bfh0L56EP2ahnHP9L0u62NTMpi55RzfPlabQB8T5y+bCx2TR8NWGCPKk/pSX0zV6uL3xMukjR2Sp97FqW9kf5b8XxiPZ4sOZG1dB4ChdBge9ZthTY4vdDxg//sb9+5C5s4aTnh4EH36fkjHDvWpVi0yu05kZDATJwxgztx1Ltvu+vs4u/4+zrIlowHo9+hk/tp+lBbNaxRJbAWZt2Uln61fxNcD3/yvHic3U/0WGMPLk/5af4xV61DqsZFcfHdYnnqXpr8Fjs+47zPj8GjWHvNfv2E+sIOr/5kJNis+fZ7G+97+XF30xS3FYLXaGDdtM3MmdCM81JeHnl9Gx5YVqVYpp71ctPowAX5e/Dr3YVauP8akOduZMrojwQFeTH+7M+Ehvhw5mcLgMavZ+J29PZvxw25CAr1ZPfshbDZN2sXM2zpHVquNceN/ZO7MZx2fp4/o2KFe3s/T+P7Mmfeby7YtW9Rg6U+vAHDhwmW63PMOd91Z67biyE2heKz2o3ywcxIpGSm83fJNdiVFc/by2ew64aXCuL9Kd975awJXLFfw9/QHoGFoAyoHVOL1LW9hMpgY0/RVdifvJcOaUSSx/X+klBoCODdeX2qtv3S8LgecdiqLA1rk2kV2Ha21RSmVBoQAyYWJ66a+/ZVSvZRSWilVy7FsUEp9qpTap5Taq5TarpQq8PJLKXVSKbUp17popdS+wgR/neOZlFLJSqmJRbzffC9/lVJDlVKPOV7PU0pdUUr5O5V/4jh/oUUZz81SSo0uqn0dPZBEZPkAIsoF4OFhpG3nKLZtjHWpU8rPM/t1xlUzOBIsf2+Lo3K10lSpEQJAQKA3RmPRdUD92nYk/edl9uPu24PB3x9jSN5TrnxKEdzvcVLmun6Z2C5fzn5t8PGhkBd3APi27cBFR0yZ+/dg8CsoJh+CHnksT0z6imtMFEFMpe7qwKVfl9tjOrgXg68/xtL5xOTtQ+BDA7jwzUyX9eZTJzCfjs1TvzB+O3KBB+qHoJSiYTk/LmZYSbqUladew3J+lHH6fF3zY3QSjzQJI9DHfk0d4utR6Jg8m7QmY9MvAFhi9qNK+aOCQvLUy76QMRpRJtdret8Bz3P5+8+L5H0D2LP3JJUqhlKhQiienibuvacx637b41KnfLkQatUsh8HgmtlUCrIyzZjNFrKyLJgtVkJDAookruvZFBNNyuX0//pxcvO8ozWZm1cDYD1+AFXKDxVYOm/FjJz3D5NH9ntl2b8DbFb76+MHUMFlbjmGPYeTqBgZQIXIADw9jHRvV5V1W0651Fm35RQ9O1UDoGubKmyJPovWmjrVQgkPsY8CVK8UTOb/sXff4VFU6wPHv2c3vfcGCR2khCpIb0pR8UoXRBRFQcWLigUBrx0QkKKIICgXUVSQYgGREkB6E0LvEGp6Ib3tnt8fs0l2s5sKBry/83mePGRn3pl59+xk9ux7ziy5BnJztXxWbzjLqCFaJVWnE3h7OlU4N4Cjxy5TI9S/6Hx6qCURWy0/3BWeT6VUyjdsjKRTp4Y4O1v/bVZGHc/axGXGEZ8Vj0Ea2Buzj5YBliNtXat1YfPVLWTma69fWm4aANXcQjiddAajNJJryOVK2lWa+oXflrxuqyqcAyqlXCilvNfsZ6FZJrZe2OIXrPLEVFh53/2HAjuBIabHjwEhQFMpZTjQD0gpYx/uQohQACFEw0rkWhE9gTPAYPF3jy8BegM6kgAAIABJREFUUsoFUsqlZovOo5WsEULogG7A9b87j1Lctg5oYlwGfoFuhY99A1xJjM+wilv30wme6/8jSz7fz+hx7QG4fkUbfnxn7O+8/ORqVn175HalBYCdfwD5sUWVpvy4WOz8A63ifEf/m+RlSzBmZ1mve34sNX/djHuvPoWVx1vLKdAyp/gSchr1b1J++AaZbf0p3Wf0WGr8vBm3ng+TuOg25OQXQH5cbOFjQ0Iser8AqzjvZ8Zwc8VSmzndbnHpuQR5FL15BbrbE5tW/irm5aRsopKyGbb0FEOXnGTHhVsf6tb7+GNMjCt8bEyKQ19CJ8Rj/Cx85q/FmJ1J7r6tADi07IgxKR7DlfO3nEuB2NibBAUVVc8Cg7yJjSvfc23RvDb3talHxy6T6NhlIp06NKROnaDbltvdRnj7YUwyf/3i0ZXw+rmNm4HnnF8gO5O8g39arXfo+BD5x/ZVOIfYxEyC/YumkgT5uRCbaHm9jEvMINhfu6ba6XW4uzqQkmpZ0dywM4pGdXxxcNCTmq6t+/Sbv+g/5mde/iiChGTra1m58otNISjYq/BxYKAXsbEV/9tZt/4QfR5qVXZgOXk7eZGYnVT4OCk7GW9Hb4uYINdAglyCeLv1BN5pM4lw3yYAhR1OB50DbvZuNPS5Bx8nGx88lALXgFCzx9WBGyXFCCHsAE8giVtUZgdUCOEGdECbhFrQAQ0GoqXUJjpJKa9JKZPL2NUKtI4raB3aH8yOoRdCzDBVUo8KIUYXHFsIESGEOGSqtBZ06moKIU4JIRYJIU4IITYKIZzNjjUU+BS4ArQ1O06UEGKKEGKPEOKgEKKlEGKDEOKCEOJ5U0xXIcR2IcQaIcRJIcQCUyeyYB+ThRBHhBB7hRCBpmXvCSFeNzv+D2bPtSuwC8g328c4U/X4uBDiFbPndFoI8ZVp+TIhxANCiF1CiHNCiDamOFchxGJTWx02a5MRQojVQog/TPHTTcs/BpxNFedltl4YIcQoU3scXLhwoa2QQrY+8tjq4j88qDGLVg/hqZfasPy/hwEwGCQnj8Tw2gfdmbbwX+zZFsWRA7exX27zs4Zlxg717sG+ehgZf0bYiIXEBZ8R9a8HSNuwFs9BtofKK5iUjZSK59Sg1JySvvyMy30fIH3jOrwG3oacbH4kK5ZTnQbYVwsjc+cWW8G3na0CYUU+ORqMkitJ2SwZ1oAZfevw7u+XSM3OL3vDUllnUFJVPHXaOJLGPIqwc8C+cStwcMT50SfJXPnVLeZQ9vHL206XL8dz4WIsf275iO1bJ7N331kOHLx9neO7jSjH316B9FlvcPPV/mBnj13DlhbrnPo8AUYDuXs3VTwJW+d1seuUzZTMQs5FJTNz8QHeH9sB0K6jMQkZtGwcyOp5fWneMIDpiyreOS4hvQrPCY6Lv8nZczfo2OF21pXKvpbrhZ5Al0CmHpzOF8e+ZGTjEbjYOXM88QRHEo7xnzYTebHpaM7fPI9RGm5jbrfJ3XMX/AGgnhCilhDCAa2f92uxmF+Bp0y/DwS23Or8TyhfBbQv8IeU8iyQJIRoidaZfMTUqZkphGhR+i4AbeJqf9PvjwC/ma0bCdyUUrYGWgPPmYb0s4F+UsqWaFXEmWYVzXrAPCllY7Tq6wAAU0f0fmAtWkew+CTAq1LKdsAOYAlaY7YFzCfWtQFeA8KBOmZ5uwJ7pZTNgO3AcyU813OAvxDC23T8HwtWCCFaAU+jzbFoa3quBe1XF63j3BS4B3gc6Ai8TlEVcxLai9/a1CYzhBAFH7Gbo3V8w4HHhBChUsq3gCwpZXMppc07AMzL86NGWc9xM+cX4EpCbNFMhMS4DHz8rG8WKdC5Rx32/hlVuG2TlsF4ejnh5GTHve1DuXD6lqaQ4DlwKGHfriLs21Xkx8djF1hU0bELCCQ/Ps4i3jm8GU73NKLmmo1UX/gtDmE1qfbFf632m7ZhHW7delgtL1dOA4YQ+s1KQr9ZSX5CnGVO/oHkJ1jm5NSkOY4NGlFj9Qaqf7lUy2mejZw2rsO16wOVysm972OELFpOyKLlGBLisQsoqsLq/QKtbmxybNwUh/oNqf7D7wTPXYJ99RoEzb69nanvD8bS/6vj9P/qOP7u9sSkFg25x6blEeBe/mH0QHcHutX3xl6vo7qXIzV9nLicVPHKrVOP/nhNWYLXlCUYUxLQ+RZVhnU+ARhTSjlf83LJPbQTh1ad0AdWQ+8fgtfUb/CesxKdjz9ekxfbHgKugKAgL2Jiij7rx8YkExDgWa5tN20+QrNmNXF1dcTV1ZFOnRoTeeTSLeVzt3Hs3hf3977C/b2vMKYkovMxf/38S3/98nPJi9yFfYsOhYsc2vfCvml7MhZ+WKl8Av1ciDYbIYpJyCTAx6VYjCvR8do1Nd9gJC0jFy93Ry0+PoOXPtzMtNe7EBaiTZfw8nDE2dGOHu1rAtC7cy1Onk+sVH5BgV7ERBcNXsbGphAQULFpGev/OEyP+5thb6+vVA62JGcn42tWtfRx8iY5x3KQNSk7iUPxhzFIAwlZCURnxBDool3Xfru0lv/sfY/pf81EIIjJjEWxTUqZD7wEbABOASuklCeEEB8IIf5lCvsa8BVCnAfGAVZf1VQZ5emAmnegfgSGSimvAQ2ACYARiBBC3F/GfpKAZCHEELQnaX5nQ0/gSSFEJLAPbXJrPbSPQVOEEEeBzWgTYQveOS9JKSNNv/8F1DT93gfYKqXMBFYB/YT2NQMFCnr2x4B9Uso0KWU8kC2EKBiL2C+lvCilNKB1YjualueidWyLH9OW1WifJO5D6+wW6AiskVJmSCnTTXGdzJ7TMVNl+QQQYfqUcczsWD2Bt0xttQ1wAsJM6yKklDellNlod7DVKCW/SqnX0J8bV1OJuZFKXp6B7Zsu0KZzmEXMjStFQzgHd10hJFR7g2zZtjpR55PIzs7HkG/k+OFoQmtZDqtU1M2VP3Bl+ACuDB9A+vYIPB7U/l6cmjTFmJ6OIdHyDefm6uVc6tONqH49uTZqOLlXorj+4tMA2IcWPQ/XTt3IvVy5N+ebq37k6lMDufrUQDK2b8HdlJNj46YYM6xzSl2znKh/dedy/15cG/2kltMYU07VzXLq2I28SuaU9vNybjz3GDeee4yMXVtx6/mIllPDcGRGOoYky5zSfv2Jq4N6cG3oQ0T/ewR51y4T8+qzlTp2SR6/N5DVzzZh9bNNuL++N78eS0RKyZHr6bg56m3O9SxJ9/re7L+szTNMzszjclI2oV4VnxeXvWk1KRNHkDJxBDkHt+PUqTcAdnUbI7PSkSnF3ugdnYvmher0ODRvh+HGZQxXL5L0Yh+SXxlI8isDMSbFkzLpGeTNWxu1Cm9Sg6jL8Vy9lkBubj7r1h+ie7fy3XUcEuLNgQPnyc83kJdn4MCBc9Sp/b81BJ+z5WfS3nuWtPeeJffwDhzb9wJAX7sRMjPDuv0dnYs+FOj02DdtizFam6Np16QNTg89TvrcCZBbuZt8whv4c/lGKtdi0sjNM/D7nxfp3tbyetm9bRg/b9Yq0Rt2XKJtsxCEEKSm5zD6nY2Me/peWjYu+sAohKBb21D2H40GYM/hG9QJ86IywpuEEXUlnqvXErXz6fdDdO9WsfmS637/i4cfall2YAVcTL1EoEsgfs5+6IWetkH3cTgu0iLmr7jDNPLRbnpys3cjyDWI+Kx4BAI3e60oEupWnVD36hxPPHFb8/tfI6X8XUpZX0pZR0o52bTsHSnlr6bfs6WUg6SUdaWUbQrumL9Vpd4FL4TwBboDTYQQEtADUgjxppQyB1gPrBdCxKJVSm2PIRZZjvZ9UyOKHwr4t5RyQ7HjjwD8gVZSyjwhRBRahwvA/IpgAAqG4IcCHUyxoHVmu6F1YM23Mxbbh5Gi9iheWi54nGdWdjZQevv9CBwCvpFSGs2GNUob3yiej3muBccSwAAp5RnzDYUQ92HdJrf9Ww70djqef709745dj9EoeeCRBtSo7cN3Xx6kXkN/7utcg7U/nSDywHXs7HS4uTvyyrtdAHDzcKTv0HDGjViDEIJ724fSumNYGUcsv8xd23Ft35kaq9Yjs7OJ/fDtwnVh367iyvABpW7vN2Yc9mE1wWgkLyaauGnv33pOu7fj0r4TNX5ajzEni7iPiu4cD/1mJVefGljq9r4vvqrlJCX5MTeIu8U74AGy9u7A5b6OVP9uLTInm/hpRXcnhyzSOqqlcenYHd+xb6H39CZo6ufkXDhD7JvWdxdXROc6nmw/f5MH5x/DyV7HR32K7mns/9VxVj+rze/6ZMtVfj+RSHaeke5zIxnQzJ8xnavRsbYHuy/d5JEvj6HXCV7rHoqXy62d/nmRe3Bo3g7vWSuQudmkfzmlcJ3XlCWkTByBcHTCY9w0hL096PTknfiL7Iifb+m4pbGz0/POpME8+9w8DEbJgH5tqVcvmE/nrqVJ4zDu796Uo8cu89LYRaSmZrJ16zHmfr6Odb+9Ta+eLdi79yyP9J2CQNCpU8MKdzYq4/tnPqBr/Zb4uXlxdcqvvLt2EYt3/1b2hrco/+heDE3b4vHx95CbQ8bijwvXub/3FWnvPYtwdMJt7FSws0fodOSdOkzONq1G4TLsZYS9A26vzQTAcOEkmd/OqlAOdnod/3mxHSMn/YHRKBnQsz71anrz2dK/aFLPj+7tajCwd33enP4nPZ9egae7I7MmdANg2a8nuXIjlfnfRzL/e63z9fWU3vh6OfPaM60ZP+NPpizYi4+XE1PGda5UG2nn00CeHfUFBqNRO5/qBvPp3HWm8ylcO59e/orU1Cy2bjvO3HnrWferNiB37Xoi0TEptGldt1LHL4lRGll6+jvebDkOIXRsv76T6xk36F+nL5dSozgcH8mxxOOE+zZmavuPMEojP55dQXpeBvY6Oya1ngBAVn4WC44twijLHIauenfP1zDdMaK0YXzTXMyWUsrRZsv+BN4Bzkkpb5jmRy4BjkopPylhP1HAvWgdpBfRvsg0BFgrpWwitK8IeAgYZOpo1ke7aedZoK6U8t9CiG7AFqDgnWmtlLKJaf+vA27ALLQbgEJNHWSEEE8DHaWUIwvykFImmDq390opXyqWYxO0jnUj4LLp94VSylVCiHQppZspfiDQR0o5QgjxHpAupfxECLHElNtKU/ttllJeMNt/mKm92qJ1JvcBw4HkYs/JfD81zdpqCuCB1mGXQogWUsrDNp7PWuATKeU2IUQyECClLM9dHfJsis2X8Y6p7/U65+5rfKfTsFBv3wnOt2typ9OwUHeP9qUSl7o1KyOy6tTaqt1olv+N7a95uhPsnvoOgIRhHcqIrFp+y3aBoRLzDP9O+h6IF9qWHVeF5Py9ACQ/0+UOZ1LEe7F245K8NP0OZ1JE1HpT+yV/Q+mBVc2uF09ufOZOZ2Fhac/FULFp57dMHnm3ynqgotn7VfrcyqusEsFQ4ONiy1ahdaCShBCOpmX7gTJvz5VSpmH6Bv1iE52/QhtiPmSa4xmPVlFdBvwmhDgIRAIlf1u2pj/a/EjzSuAvwHSzXMtjD9rzDkeb67mm9HDbpJRWXxonpTxk6lzuNy36ytSBrFnO3X4IzAGOmtoqCm3aQWkWmuIPlTQPVFEURVGUKlKO/yLzf12pHVApZVcbyz4DPqvIQaSUNW0si0KrNmKa8zgR218X1K6E3RaWn4pVXpcUO04S2jA+mM3ZlFIuMY8tyNHUMc6UUlqNQxZUP02/r0S7sQop5Xtmy0fYSta8DaSUs9Cqtebro4o9pxG21kkps4DRFGPj+fQx+308MN5WXoqiKIqiKFVN/U9IiqIoiqIoVUnNAb29HVAhxD6g+FD3cCnlMVvxdyMp5Ta0u8sVRVEURVGUv8Ft7YBKKYv//6GKoiiKoiiKOTUHtNz/FaeiKIqiKIqi3BZqDqiiKIqiKEpVUhVQVQFVFEVRFEVRqpaqgCqKoiiKolSh0v4ToNvtrvwWelQFVFEURVEURaliqgKqKIqiKIpSldQcUFUBVRRFURRFUaqWqoAqiqIoiqJUJVUBVRVQRVEURVEUpWqpDqiiKIqiKIpSpdQQvKIoiqIoSlUyVt3XMN2tVAVUURRFURRFqVKiKr8MVflHUCeEoiiK8v9NlX5fu3HbK1X2XqvrOueu/C56NQSvWInud9+dTsFC8Jp9bL464U6nYeGB0KlsvDL+TqdhoWfYNAC8p/a+w5kUSZ7wBwDyr7fvcCZFRKuPAEjI/u4OZ2LJz+kJ5IGJdzoNC6L1FJKf6XKn07DgvfhPAMQLbe9wJkXk/L3av1fn3OFMiojQVwDImvTQHc7EkvPk3znbutGdTsNC/QMn73QK/y+pDqiiKIqiKEpVUl/DpOaAKoqiKIqiKFVLVUAVRVEURVGqkroLXlVAFUVRFEVRlKqlKqCKoiiKoihVSc0BVRVQRVEURVEUpWqpCqiiKIqiKEpVUhVQVQFVFEVRFEVRqpaqgCqKoiiKolQldRe8qoAqiqIoiqIoVUtVQBVFURRFUaqSmgOqKqCKoiiKoihK1VIdUEVRFEVRFKVKqSF4RVEURVGUqqSG4FUFVFEURVEURalaqgKqKIqiKIpSldTXMKkKqKIoiqIoilK1VAVUqRSPkeNwbNUemZNNytwPyb94xirG+z9z0Hv7gV5P7qlIUhfOAKMRr9c+wq5aDQCEqxsyI52EccNvOacT+2NY+cVRjEZJhwdr0nNoA5txh7Zf5+sP9vHmvG7UaOBNYkwGHz6ziYBQdwBqNfRh6CstbjkfgJMHYln1xTGMRkm7B2vQc0h9m3GHt19n8YcHeOPzLoQ18Abg+sWb/DgnkuzMfIQQvDGvC/YO+lvO6f7arZj6wAvodTq+jfyDOXtXWKyv7uHPF31ex9PRFb1Oz/vbFrPpwgHsdXbMfnAsLYLqYZSStzYvYNeVo5XOQ0rJ5KWRbI+MxsnBjqnPt6ZxLW+ruOMXk5nw5X5ycg10bh7MpCebI4Rg+rIjbD0Ujb2djrBAV6aMbo2HqwO5+Ube/eovjl9KQicEE59szn2NAiqc395d55kzbQNGo+SRfi0YPrKDxfofl+7ltzWH0et1eHm7MPH9RwgK8QJg3uzN7N5+DiklrdvW5pXxvRBCVL6dvj3C9sgYnBz1TB11r+12upTMhC8PmtopiEnDmyGE4NOfThBx6AY6IfDxcGTq6HsJ9Hbm67Vn+G33VQAMRsmF66nsnv8IXm4OlcrT+fGx2Iffh8zNIfPrqRiunLOKcXt1OsLLF6HTk3/2KJnfzQFpxHnQ89g3b4/Mz8cYf4PMrz9GZqVXKo/y+Hr4JPqEdyAuLZnwD4f9bccB2LH/CpO/2InRKBn4YENGDW1psT4318D4aRGcOBePl4cTs97uQfUgj8L1N2LT6DPyR8Y82ZqRg5sDkJqew9szt3EuKgkhYPLr3WjRKKjSOdo/PBpdg9aQl0PuqlnIGxeKBTjiMHQCwicYjEYMp/eRv3FJ4Wp9k07Y3T8MpMQYc4m8FdMrnYs5/9cm4tqhMzI7i5j3J5Jz5lSJsSEzP8e+WiiXhzxqsdz7iafxf/kNzj/QHuPNlNuS122h5oCqDqhScY4t26MPCSX+xYHY12+C5+g3SRw/0iou5ZNJyKwMALze/Bin9veTvXMTKTPfLoxxHzEWmZlxyzkZDZIVc4/w72kd8fJ3ZvqYrYS3Dya4hodFXHZmHtvWnKfmPZZv4H4hbkz88v5bzqN4Tj/NPcKYaR3w8nNmxkvbCG8XZDOnP3++aJGTwWBk6cd/MXx8K6rX8SQjNRe9/tYHLHRCx4yeY+j340RupCawZcRnrD+3lzOJVwpjXms/lJ9PbWfx4XU08A1jxeAPaTb/KZ5q/iAAHb5+AT8XT34a/BHdl4xFUrmhpO2RMVyOSWfDrAc5cj6J9xcfYsWH1q/B+4v/4oOR99K8ng+jpu9kx5EYOjcPpn14IOOGhGOn1/HJD0dZ+OtpXh/alJ+2XATgt2m9SLyZzXPTdrDyowfQ6crfATQYjMyc8gdzvhxGQKAHzz7+FR271qdWHf/CmHr3BPH198/i5GzPmhUHmTc7gg9nDOBY5FWORV5l6crRALwwYgmHD16mZeualWunI6Z2mtmLIxeSeH/JYVa83926nf57mA9GtqR5XR9GzdjFjqOxdG4WxMiH6/PyoMYALN1wni/WnOL9Z1oysk8DRvbRPqRtOXSDb/44X+nOp134fegDq5M6YRj62o1weXIcaR+9YBWXPv89yM4EwPXFD7Bv3ZW8/VvIO3mQrFWLwGjAeeBonB4eRtbKLyuVS3ks2bOOz7etZOmId/62Y4B2Hn0wdweLpz1CoL8rg8asonv7mtSt4VMYs3L9KTzcHdm4dBjrtp5j5qK9zP5Pz8L1U+fvolObMIv9Tp63k06tQ/ns3V7k5hnIzsmvdI66+vci/KqRM+tZRGgDHP71EjkLXrWKy9+xGuOlo6C3w+GZKejq34vx7EGEbwh2XQaT8+XrkJ0Orp6VzsWca/vOOITVIKp/b5yaNCXgrXe5+vQQm7Fu3R7AmJlptdwuMAiXNu3Ii75xW3JSbq9/zBC8EKKfEEIKIe4xPdYJIT4TQhwXQhwTQhwQQtQqZfsoU9wxIcRJIcRHQgjHYjGvCiGyhRCepscBQohLQoggs5gvhBBvCSG6mvIZabauhWnZ66bHy4UQkaafKCFEpGm5vRDiG1Mup4QQE8z20VsIcUYIcV4I8ZbZ8mWm5ceFEIuFEPam5cLUDueFEEeFEC3NtvlDCJEihFhb+Za35timM1lb1wOQd/Y4Old3dN6+VnEFnU/0eoSdHUjrjopzhwfI2rHxlnOKOpOEf4grfiGu2NnraNW1Okd3RVvFrV1ykh6P1b8tlcSyXD6TjF+IG37BRTkd2x1jFbduySkeGFwPO4eiP8fTB+MIqe1B9TraxdzVwwGdvnIVNHOtQhpwMTmayykx5BnzWX3qTx6q384qzt3RBQAPJ1di0hMBaOAXxvaoSAASMm9yMyedFsH1Kp1LxF83eLRTDYQQNK/nS2pmLnHJWRYxcclZpGfl06K+L0IIHu1Ug80HtTeTjk2DsDN1ypvV9SUmUdv2wvVU2jXRKp6+nk54uDpw/GJyhXI7dfwG1UO9qVbdG3t7Pff3bsyObZZV/lZtauLkbA9A4/BqxMelAiCEIDcnn/w8A3m5BvLzjfj4ulawdYpE/BXNox1N7VTXl9SMvBLaKY8W9Uzt1LGondxc7AvjsnLysVWIXbfnGg+3C610jg4tOpKzewMAhosnES5uCE8f60BT5xO9HuzsC68J+ScOgtGg/X7xJMLb33rb22jH+UiSMlL/1mMAHD0TR1iIJ6EhHjjY63moa10idkVZxETsjqJvT+2DQK/Oddhz+DrS1C6bd10iNNjDosOanpHLwWPRDHywIQAO9no83CzeyipE37AthsMRAMirZ8DJFdyLVdjzcrTOJ4AhH3njAsJDu+br7+1N/r61WucTIONmpXMx59qlO6nrfgEg+/hR9O7u6H39rOKEswvejz9F0mLrDyz+r44nfu5Mm+89d5o0yCr7uVv9YzqgwFBgJ1DwEegxIARoKqUMB/oBZdXXu5li2wC1gYU2jnHAtC+klHHANOATAFPnriMw0xR/zJRHgSHAkYIHUsrHpJTNpZTNgVXAatOqQYCjKZdWwGghRE0hhB6YBzwINAKGCiEambZZBtwDhAPOwLOm5Q8C9Uw/o4D5ZvnMAG59bLsYva8/hsTYwseGxDj0PrbfMHze+ZTAJX8gszLJ3rPFYp1Do+YYUpIwRF+95ZxSErLxDnAufOzl70xKouWb9NVzKSTHZRHeNthq+8SYDKaOjmD2uO2cP5Zwy/loOWXh7W+Wk58TKQnFcjqfQnJ8Fk3aWg6fxV1PRwDz3trNtBe2snm59XBmZQS7+XI9Nb7w8Y20BILdLT88fLzjOwY37s7xMd+yYtAHvLnpCwCOx13kwXrt0AsdYZ6BNA+qRzWPyncUYpOzCPZxKXwc5ONCbLGOVWxyFkE+zmYxzlYxAKu2XaJzc60NG4R5EXHwBvkGI9fiMjhxKZnoJOvqSGni41IJMBsGDQjwID42rcT439ZE0rZDXQCaNKtOy9Y1+dcDs/nXA7O5r31tata+xXbyLd4G2cViskttp9krjtN17O+s3X2VsQMaW2yblZPPzqMx9GxdrdI5Cm8/jElxhY+NSfHoSuhEuo2bgeecXyA7k7yDf1qtd+j4EPnH9lU6l7tJbEIGwQFFHz6C/F2JTbQc8YlLTCfY3w0AO70Od1cHUlKzyczKY9GPhxnzZGuL+KvRqfh4OjNhxlb6jf6Jt2duJTMrr9I5Cg8/5M2ia4JMTUB4WHf0Cjm5orunDcYL2ludzq8awrcaDqM+wXH0LHT1WlU6F3N2/gHkxRZ9YM+Pi8UuINAqzu/5f5O8bAnGbMvrgmvnbuTHx5F7znp6mHJ3+Ed0QIUQbkAHYCRFHdBgIFpKaQSQUl6TUparzCGlTAeeB/oKIXxMx6gDuAFvo3VECywE6gghugGfAy9JKQv+2q8ATkKIQKFN8OoNrLeRvwAGAz8UpAC4CiHs0DqTuUAqWsf4vJTyopQyF/gReNSU8+/SBNgPVDft61FgqWnVXsBLCBFs2iYCKPldsyi/UUKIg0KIgwsXFu+T29zCaklJHzCTPniZ2GceBnsHHMLvtVjn1Kkn2beh+llSAuZZGo2SVfOP0v/5cKs4Dx8nPlzWmwlf3s+A58P575QDZGVU/oJelJP1IvPqk9EoWT3/GP1GN7GKMxokF04k8dSEVrw6uxNHdt3gzKF4q7iKsjUPURZruwGNuvL9sU00mTecwT+9w4JH3kAg+O7IBm6kxbP16blMfeB59l8/Sb6palUpNl+zYvnZasNijxf8fAo7veCRDtow5YCuNQnydWbg25uZ8m0kLer5YleB4fcSUitxDufHeY5nAAAgAElEQVSGtUc5fTKax0doleRrV5KIupTAmo2v8POmV/hrfxSRf12u0PEtk7GVS9kJm8e8OrgJ2z57iD7tQ/luk+X8vq2Ho2lR37fSw+9g43UrISeA9FlvcPPV/mBnj11Dy/mQTn2eAKOB3L2bKp3LXaUc56/tZhLMXXqAEQOa4upsb7Em32Dk5Ll4hj7SmDVfDsLZyZ5FPx6ufI62TuuSLug6HQ6PjSd/z6/IZFPnUKdH5xdC7lfjyV0xDYd+L2tV1Ftl6++tWF6O9e/BPjSM9G0Rlps6OuHz9GgSF8y99Tz+LkZZdT93qX/KHNC+wB9SyrNCiCRTJXIFsFMI0QmIAL6TUpb7r1BKmSqEuIRWOdyH1un8AdgBNBBCBEgp46SURiHEC8AW4Fcp5fZiu1qJVtE8DBwCcmwcrhMQK6U8Z7bNo0A04AK8KqVMEkJUA8zLgdeA+8x3ZBp6Hw68bFpka5tqpn2Xi5RyIUXVYBm9/murGJcHB+LSQ5vcnXf+JHrfQAq6aHrfAIzJpXSO8nLJObAdpzadyT2yX1um0+PUthsJrz9V3jRL5eXvTHJc0SfglPgsPM2qRjmZ+dyISmXOazsASE3K5st39jD6g3bUaOBdOCQfVt8b/2BX4q6lU6OB9Y0eFc4p3iynhGzLnLLyiY5K47PXd5pyyuHLd/Yx+oP78PJzpm64L26e2tBa4zaBXD2fQoOWtzY0eSMtwaJqGeLuR0x6kkXME816MWj5JAAOXD+Fk94BXxcPEjJvMimi6APKhuGzuJhUsblVyzae56et2hzN8No+FpXJmKRMArydLOIDfZyJScoyi8kiwLuoDddsj2LroRssmdSlsINop9cxYXjzwpgh726hRpB7hfIMCPQgLqZoiDYuLhW/ADeruAN7L/LNVzuZ9/VTODhol9M/t5ymcXg1XFy0Dl3bDnU5cfQ6zVvVKPfxl226wE9bLwEQXtub6MRibeBVjnbycqa4Pu1Def6T3Ywd0Khw2e+VHH537N4Xh859ADBcOoPOJ4CCjyM6H3+MKaWMJOTnkhe5C/sWHcg/eRAAh/a9sG/anrRPrOcf/lMF+rsSHVdU8YyJzyCg2HSMQD83ouPTCfJ3I99gJC0jFy8PR46eimXD9ovMWLSXtPQcdDqBo4OeXp3rEOjvRrOGWjWwV+faLPqhYh1Q/X19sGvdCwDjtXMIz6JrgvDwQ6Yl2tzOvu9YZMJ1DLt/KVwmUxMwXjkNRgMyORZjwjWEbwjyesVHbTwHDcWz7yAAck4ewz4wiIJav11AIPnxcRbxTuHNcLqnMbV+2QR6PXY+vlRfsIS4GZOxD6lGje/XFG5b47tVXBnxGIbE2zPCpdy6f0oHdCgwx/T7j8BQKeUbQogGQHfTT4QQYpCp6lde5h+xhgD9TB3O1WidynkAUspIIcRx4Asb+1gBLEcbHv8BaF9C/j+YPW4DGNCmEHgDO4QQmynhs2ixx18A26WUO2w8h5K2uWWZ61eSuX4lAI6tOuDy0ECyd27Evn4TjJnpGJMtL1jCyRnh7KIt1+lxbNme3JORhesdm7Um/3oUxkTLC0pl1WjgTdz1dBKiM/Dyc+avbdcYMbFo6MrZzZ7pq/sUPp4zbjv9RodTo4E3aSk5uLprcywTbmQQdz0dv+Bb/wQf1sCL+OI5TSiqAju72vPxqocKH3/62g76jWpCWANv/EJc2bziHLnZ+ejtdZw7mki3AXVuOadDN85QxzuEMM9AotMS6d+wC8/9Os0i5npqHJ1rtuCHY5uo7xuKo50DCZk3cbZzRAjIzMuha80W5BsNFjcvlcewnnUZ1lMbqt52OJplG8/zcLtQjpxPwt3Z3qJzCRDg7Yyrsx2R5xJpVteHX3Zc5gnT9juOxPDVb6f59j/dcHYsupRl5eQjJbg42bHrWCx2ekHd6pY3fpXlnsYhXLuSxI1ryfgHehDxxwnendrPIubsqWimf/g7s74YirdZpyIwyJPfVh8mP98IUhL512UGD7uv+CFKb6cedRjWo05RO226wMPtqnPkQhLuLiW0k5M9kecTaVbHh192XuaJntr2UTFp1DR1wLcciqZWcFFnPC0zjwOn45n+guUwb3nkbPmZnC0/A2DXtC1O9/cnb18E+tqNkJkZyJuWH2xwdEY4OWvLdXrsm7Yl/6w2r9CuSRucHnqctGljIdfWZ/h/pvAGAVy+nsK16FQC/Fz5fdt5Ppn4gEVM9/Y1+XnjGVo0CmLD9gu0bV4NIQTL5hSdb3O/OYCLsz1P9NVGcIL9Xbl4NZnaod7sOXSdOjUq9mHZsG8thn3arQG6Bq2xa/sIhqN/IkIbQE4GpFkPJto98CTC0ZXcNZ9a7uvkHvRNu2A4vBlcPBC+1ZBJ1nPdy+PmTz9w8yftrdK1Q2e8Bg8jbePvODVpijE9zarzeHPVcm6uWq7lFxxCtdnzufb8CAAu9upUGFfrl01cfnLQ3XUX/F08N7Oq3PUdUCGEL1oHs4kQQgJ6QAoh3pRS5qANea8XQsSiVUrL1QEVQrgDNYGzQoimaJXQTaYqigNwEVMH1MRo+rEgpYwRQuQBPdCqkhYdUNMwe3+0uZ4FHker6OYBcUKIXcC9aJVM81JEdeCG2b7eBfyB0WYx10rb5u+Q89cuHFu1x3/+KmRONjfnfli4zm/WtySMG45wdMZ7wicIe3vQ6ck9dpDMDWsK45w69rh9w++AXq9j8L+bM++tXdpXHvWuQUhND9YuOUlYfS+atg8pcdvzRxNY+81J9HodOh0MfaUFrh6VH440z2nQS035YsJupFHStlcNgmt6sG7JKcLqexHe3nouagEXdwe6D6jLjJf+RAho1CaQJvdV/mtWChikkTc3fcGqIZPRCx3Ljm7kdMJlJnQaTmT0Odaf38vbEYv49KGXebF1PySSMeu0Kc9+rl6semwyRmkkOi2R53+bcUu5dGkexPbIaHq+uh4nRz1TRhd1gvpO2MjPU7U7gd99piUTFxwgO9dAp2ZBhXM9P1xyiNw8I89M1eYRNqvry/sjW5GYmsOzH29HJwSB3s5Me6FNhXOzs9Px6oTejHvhewxGSZ++zahdN4BF87ZxT+NgOnVtwLzZEWRl5vL2G6sACAzyYPpnQ+jWoyGH9kfx5MAFCCG4r30dOna1/fVb5W6nIzH0fG0DTg56powq+hDTd+Jmfp6idWjefboFExceNLVTIJ2bae00c/lxoqLTEQJC/Fx4/+miYe9NB6/TITwQF6dbeyvIP7oXQ9O2eHz8PeTmkLH448J17u99Rdp7zyIcnXAbOxXs7BE6HXmnDpOz7VcAXIa9jLB3wO017VwzXDhJ5rezbimn0nz/zAd0rd8SPzcvrk75lXfXLmLx7t9u+3Hs9Dr+8+9OjHxrLUajZEDve6hX04fPluynSX1/urevxcAH7+HNjyPo+eQyPN2dmDWpR5n7ffulTrwxNYK8PAOhwR5MecP6WxHKy3jmALJ+axzHfa19DdPq2YXrHF+aS87n/wYPX+y7DcEYdwXHMZ8BkL93LYaDGzCe+wt93ZY4vrwAjEby//gassqc+VWmjF3bce3QmZpr/kBmZxPzwaTCdWHLVnNlWP9bPoZyZ4ni87/uNkKI0UBLKeVos2V/Au8A56SUN4QQOmAJcFRK+UkJ+4kC7pVSJpjmlM4HjFLKp4QQU4FUKeVUs/hLQFcp5WXT423A61LKg6bHXU2P+wgh2gMBUsqfhRDvAekFeQghegMTpJRdzPY9Hq1i+gzaEPwBtArsSeAscD9w3bT8cSnlCSHEs6b4+6WUWWb7ehh4CXgIbbj+MyllG7P1hXmWq8FBRverWLXm7xa8Zh+br04oO7AKPRA6lY1Xxt/pNCz0DNMqmd5Te9/hTIokT/gDAPnX22VEVh3R6iMAErK/u8OZWPJzegJ5YOKdTsOCaD2F5Ge6lB1YhbwXax84xAtt73AmReT8vdq/V+eUEVl1ROgrAGRNeqiMyKrlPPl3zrZuVHZgFap/4CTYHk382+R/N7zKOl92T3xbpc+tvO76Cija8PXHxZatQutwJpl9ldJ+tJuESrPVdEOQDlgDFJTuhqDdTW5ujWn5NMogpdxdyuohWA6/g1ZZ/S9wHO2k/6+U8iiAEOIlYANapXexlPKEaZsFwGVgj6lKu1pK+QHwO1rn8zyQCTxdcBAhxA60jq6bEOIaMFJKuaGs56MoiqIoivJ3uus7oFLKrjaWfQZ8VsH91CxlndX3h0opx5WWh5RyG7DNxnbvFXs8wkZMOtocU1u5/I7WqSy+3OZrZborfkwJ6zrZWq4oiqIoyh2k5oD+M76GSVEURVEURfnfcddXQCtKCLEPKP7fQgyXUh67E/koiqIoiqIolv7nOqBSyrvrDhpFURRFURRzBqsv1fl/Rw3BK4qiKIqiKFXqf64CqiiKoiiKcjeTd/F/kVlVVAVUURRFURRFqVKqAqooiqIoilKV1NcwqQqooiiKoiiKUrVUBVRRFEVRFKUqqTmgqgKqKIqiKIqiVC3VAVUURVEURalC0iCr7OdWCCF8hBCbhBDnTP9624hpLoTYI4Q4IYQ4KoR4rDz7Vh1QRVEURVEUxZa3gAgpZT0gwvS4uEzgSSllY6A3MEcI4VXWjtUcUEVRFEVRlKpk/Mf8T0iPAl1Nv38DbAPGmwdIKc+a/X5DCBEH+AMppe1YVUAVRVEURVH+RwkhRgkhDpr9jKrA5oFSymgA078BZRyrDeAAXChrx6oCqiiKoiiKUpWq8HtApZQLgYUlrRdCbAaCbKyaVJHjCCGCgW+Bp6SUZZZ4hZTqqwAUC+qEUBRFUf6/EVV5sNzZA6rsvdbh1VWVfm5CiDNAVylltKmDuU1K2cBGnAfa8PxUKeVP5dm3GoJXFEVRFEWpQtIoq+znFv0KPGX6/Sngl+IBQggHYA2wtLydT1BD8IoNxo1j7nQKFnQ95yFPfXSn07AgGr6NvPzJnU7DgqjxOgDyxud3OJMiIuQlAOSVWXc4kyIibBwAp5rfc4czsdQw8jRkWV3b7yznR5GXpt/pLCyIWm8CIK/OucOZFBGhr2j/vtD2DmdSRM7fC8CZlg3vcCaWGhw6dddeOxWbPgZWCCFGAleAQQBCiHuB56WUzwKDgc6ArxBihGm7EVLKyNJ2rDqgiqIoiqIoihUpZSJwv43lB4FnTb9/B3xX0X2rDqiiKIqiKEpVqsKbkO5Wag6ooiiKoiiKUqVUBVRRFEVRFKUqqQqoqoAqiqIoiqIoVUtVQBVFURRFUarQbfh6pH88VQFVFEVRFEVRqpSqgCqKoiiKolQlQ5n/U+X/PFUBVRRFURRFUaqUqoAqiqIoiqJUITUHVFVAFUVRFEVRlCqmKqCKoiiKoihVSX0PqKqAKoqiKIqiKFVLVUAVRVEURVGqkpoDqiqgiqIoiqIoStVSFVBFURRFUZQqJNUcUFUBVRRFURRFUaqWqoAq5SKlZMqqs2w/kYCTg54pTzSicaiHVdyJK6lM+O4EOXlGOjf2Y+KA+gghOH0tjfeWnyYzJ59qvs7MeLIJbs52JGfk8srXxzh+OZW+9wXzn8H3lJrHjkPXmfzVQYxGycAedRk1oInF+tw8A+Pn7OLEhSS83B2Y9Xpnqge6AfDlymOs2nwBnU4w6bnWdGoRQnR8BuM/3UVCShY6IRjcsx5PPtIQgE+XRRKx/yo6IfDxdGLqy+0J9HEps612HLjK5Pl7tBx7N2DUkOaWOeYaGD9jGyfOJeDl7sisSfdTPcido6fjeGfODq29gZeeaEmPjrXIyc3nidfWkptnwGAw0rNTbcY+2arMPCxy2n+ZyZ9vx2iQDHy4EaMev9c6p6kbOXE2Hi8PJ2a925vqQR5ci0nl4ae+o1aoNwDNGgXx/rhuhW394ad/sv/IdXQCXhnZjl5d6lYor6I2u8LkL3ZrbfbgPYwa0sI6v+lbtDbzcGLWpAeoHuReuP5GXBp9Rq5gzJP3MnJQs0rlYEvgm5Nw69gZY3Y20e9MIPv0yRJjq8/5Avvq1bk08F8A+D3/El79B2FITgIgbu5sMnZuv+Wctu86w+Tpv2A0Sgb1a8OoZ7pZrD/w10WmzPiVM+dimPXx4/Tu0bRwXcOW46lfNwiA4GAvFnz6dKXz2HHwGpPn78VoNGrn+WOW7Z6ba2D8J38WvWYTulE9yJ1dh64zc/EB8vKN2NvpePPZNrRtHqJtk2fgwy/2sP9oNDoheGVEK3p1rFX+nPZfYfIXO03nUUNGDW1pndO0CE6cM53nb/egelDRdexGbBp9Rv7ImCdbM3Kw9nebmp7D2zO3cS4qCSFg8uvdaNEoqLLNVqqvh0+iT3gH4tKSCf9w2N9yDFsC3piIa8fOyOxsot+dSE4p53m12fOwrxZK1GDtPPcdPQbPfkXnecLnc8jYVbnz/G68dip/n39kB1QI0Q9YDTSUUp4WQuiAOUB3tPMvGxgspbxUwvZRQBpgMC16EXAAXpdS9jGLWwKslVKuFEJsM60/KISoCWwCXgJ2AIuApoAAUoDeUsp0IURv4FNAD3wlpfzYtN+XgFeAOoC/lDLBtFyY4h8CMoERUspDpnV/AG2BncVyLGlfw4DxprB04AUp5ZFyNrGV7ScTuRyXyR/vtOdIVCofLD/N8tfbWMW9v/w07w9tSPOanoyeH8mOk4l0buzHf344xRt969Gmnjer9lzn64jLvNynDo52esY+XIdz0emcu5Feag4Gg5EPvtzP4vcfINDXhUFvrKd7m+rUDfUqjFm56Twebg5sXNCXdTsuMXPpIWa/0ZnzV1P4fedl1s59hLikTJ5+ZzN/fPEoer1g/NOtaFzHl/SsPAa8to72zYOpG+rFyH6NeHmYdgFcuvYUXyw/yvsvtC07x893sfjjhwj0c2XQv3+me7sa1K3hXZTjH2e0HJc8xrqtF5j59X5mT7qfejV9WDmvH3Z6HXGJmfR9fhXd2tXAwV7PkukP4+psT16+kWGv/krn1tVp3jCwXK+dwWDkg0+3sXhGXwL93Rj0/HK6t69N3Zo+RTn9fgIPdyc2LnuSdVvOMvPLXcx+90EAwkI8+fmroVb7XfDdAXy9ndnw7XCMRsnNtOxy5WMzv7m7WDztYa3NXlpN93Y1i7XZaTzcHNn4zVDWbT3PzK/2MvvtHoXrp87fQ6fWYZU6fklcO3bGIawGF/7VC6fwZgRNepeo4Y/ZjHXv3gNjVqbV8qTvviFp6eLblpPBYOSDqWv474LnCAz0ZOCwuXTv0oi6dYrOheAgL6Z+8BiLl/5ptb2Toz2/rHj19uQxbzeLp/TWXrOxv9K9bZjla7bhjPaa/Xcw67ZdYObiA8ye2B1vD0fmv9+DQF9XzkYl8eykDWxfpp1fC348gq+nExu+HmQ6p3IqltPcHSye9giB/q4MGrOK7u1rUreG2Xm+/hQe7o5sXDqMdVvPMXPRXmb/p2fh+qnzd9GpjeV5NHneTjq1DuWzd3uRm2cgOye/ss1WpiV71vH5tpUsHfHO33aM4lw7dMY+rAaXHu2NU3gzAie8w5WnhtiMdeveA2Om9XmevOwbkr/97y3lcTdeO/9W6iakf+wQ/FBgJ1DwV/IYEAI0lVKGA/3QOoKl6SalbG762V3eAwshqgMbgNeklBuAl4FYKWW4lLIJMBLIE0LogXnAg0AjYKgQopFpN7uAB4DLxXb/IFDP9DMKmG+2bgYw3EZKJe3rEtBFStkU+BBYWN7naMuWY/E82iYYIQTNa3mSmpVP3E3LN4e4mzmkZ+fTopYXQggebRNMxLF4LZm4DFrX1TqK7e/xZdOROABcHPW0quOFo13Zp+LRc4mEBbsTGuSOg72ehzrWIGLfVYuYiP1X6dutDgC92tdgz9EYpJRE7LvKQx21C1L1QHfCgt05ei6RAB8XGtfxBcDN2Z461T2JTdQusG4uDoX7zcrOR/t8UEaOZ+IJC/EgNNhDy7FLHSJ2W740EXui6NujvpZj51rsOXwdKSXOTnbY6bV2yM0tOp4QAldnewDy843kG4wIys6lMKfTsYSFeBEa4qnl1L0+EbsuWua06xJ9e2nV515d6rLn0DWkLP0CuXr9qcJKqk4n8PZ0LndOFvmdibNss651idgdZZnf7ij69ixos9rsOXyjML/Nuy4RGuxO3ZrexXd9S9y73s/Ntb8AkH3sCDp3D+z8/K3ihLMLPsNHkLBovtW62+3o8avUCPUjtLovDvZ2PNyrGRHbTljEVK/mwz31g9GV43ytdB5n4gkLNj/PaxOx54pFTMSeK/R9QKuI9+pUiz2R2mvWqK4fgb6uANSr4U1OroHcXK0WsHrDWUYN0Sqp2jnlVIGc4ggL8SQ0xOw82hVlmdPuKPr2bKDl1LlO4d8eFJxHHhYd1vSMXA4ei2bgg9qoiIO9Hg83x3LnVFE7zkeSlJH6t+3fFreu3Uk1O8/17h7oSzjPvYc9ReJXC/6WPO7Ga6fy9/rHdUCFEG5AB7SOXkEHNBiIllIaAaSU16SUyX/D4YOAjcDbUspfzY59vSBASnlGSpkDtAHOSykvSilzgR+BR00xh6WUUTb2/yiwVGr2Al5CiGDTNhFoVVsLJe1LSrnbrA32AtUr84QLxKbkEORd9GYQ5OVoswMa6FUUE+jlSGyKFlMv2I0tps7ohsOxRCdXvFoWm5RJsJ9rUQ6+rsQmZVnmkJRJsJ82TG6n1+HuYk9KWg6xSVnFtnUhNsnyk/y12HROXUyiWX2/wmWzvztM15GrWLv9EmOHlj20G5uQQbC/W9Fx/F2JTcywzDEhk2B/16IcXR1ISdXa6cipOPo89xP/Gr2K98Z2KLyoGgxG+j6/ig6Dv6V9y2o0axhQZi4WOQWY5+RGbIJltTkuIZ3gAPeinNwcSEnVXqNrMan0e+4Hnnh5FQePaqd6arqW76eL99J/1I+8/N56EpKsKyPlyy/Tss38XIlNKNZmiUXtWtRm2WRm5bFoeSRjhltOKbgd7AICyYuJLnycHxuDXYB15cR/zFiSlv4XmW19TnsPGUatFb8Q/N5kdO7WU1YqKjbuJkFBnoWPAwM9iY0rf4clJzef/o9/yuDhn7N5y/HK55FYdA4DBPm5WJ/nNl8zy2vGhp1RNKrji4ODvuic+uYv+o/5mZc/iiAh2fLvu9ScEjIIDjDLydbfXmJ6yefRj4cZ82Rri/ir0an4eDozYcZW+o3+ibdnbiUzK6/cOf0T2AUEkh8bU/g4Ly4GO3/r64vfi2NJ/m4Jxmzr18T7sWHUXP4zQe9+VOnz/G68dv6tDMaq+7lL/eM6oEBf4A8p5VkgSQjRElgBPCKEiBRCzBRCtCh9FwBsNcXvq8CxlwKfSyl/Mlu2GBgvhNgjhPhICFHPtLwaYF6eu2ZaVprKbFMeI4H1Ja0UQowSQhwUQhxcuNB2odRWNaz450ibMaagyY834vsd1xgwfR8Z2Qbs9ZU49WwU5KxzsLWhsLnCfNuMrDzGTvuTCSNbW1Q+X32iBdu+HkCfzrX47vczFc+ZojYozLGUJ9KsYQBrFw3ip8/7snD5EXJyteE+vV7HzwsGsO37xzl6Jp6zl5LKn4DN10WUFQJAgI8rW34cwZpFQ3nrxU68/tFG0jNyMRiMxMSn07JJMKsXDqF5oyCmL9hZ/pzKzK/MEBCCuUsPMmJA08Iqx+1ks4BYLBHHBvfgEFqDtK2brUKTV/zAhT49uPRYX/IT4gl8bbxVTEXZaoeKFDq3rp/A6u9fZubUoUyZ8RtXriZWMhFbeZTjnDILOReVzMzFB3h/bAcADAZJTEIGLRsHsnpeX5o3DGD6ogpcnm/h+jB36QGb51G+wcjJc/EMfaQxa74chLOTPYt+PFz+nP4RbJ7oFo8c69+DQ2gY6TbO85SffuTiv3oSNaQf+QnxBIx78/Zldqevncrf6p84B3Qo2nxP0KqKQ6WUbwghGqDNAe0ORAghBpmqhiXpVjBf0qSk8Ubz5ZuB4UKIJVLKTAApZaQQojbQE20o/IAQoh3l+au2VpltSt+hEN3QOqAdS4qRUi6kaIheGjeOAWDZ9qus3K1VvJqEeRBjVrWMScnB39NyKCrQy4nYlKKY2JQcAkwxtYNc+XqMdkPApbgM/jyRQEUF+roQbVYZi0nMIMDH2UZMJkF+ruQbjKRl5uHl7mBj20wCTDcU5eUbGTvtTx7pUoue7WzPI+zTuRbPf7SlzCpooJ8r0fFF1cWY+AwCfFxtxGQQ5O+m5ZiRi5e7ZVvWCfPG2cmOs1HJhNcvGg7zcHOkTdNgdhy8Rv1aPpRHoL8b0XHmOaUT4OtqIyatKKf0XLw8nBBC4OCgtXGTBgGEhnhy6VoyTeoH4OxkR49O2nSH3l3rsur3km9cKD2/Ym2WkGGdn6ldi7fZ0dNxbNhxkRmL9pKWnotOJ3C01/NE3ybFD1Mu3o89jlf/QQBknTiGfVAwBfUeu8Ag8uPjLOKdmzbn/9i77/Aoqv2P4++zu2mEFNJDEnpJ6CK9E4qIhaYigg0Ry8UK1wJe9VqwURSxgSKioojSpIoIiEpvgvQWWiohCZC+e35/zJJkSYCQhF343e/reXjIzpyZ+WQzTM5+z5nBM6YhtRevQJnNWAICqPb5DI4Ouw9ramHnLm3ObCInlX+IPizUj4SE9ILXiYnphASXvuIUGmJUT6MiA2nVoha79pygWlTgFecIDapEfHKRf08phf+eCtuc/5l5FzvPE5LPMeL1X3lnVGeqVTXy+/t64OVhoUe7GgD06lSTn5btK32mYG/ik4pkSi7pPKpc/Dzy9eDv3Yks+/38eZRjnEfuZm7qVJvQ4Mo0tc8ZvKlTLaZ+d/13QP3vuge/fncAkP3PTiyhhTdVuYWEkZ+c7ND+/Hlea+GvYD/Po6Z8xbHh9xc/zz8o2xD9tXjtvJq0zAG9viqgSqlAjA7m5/Ybif4NDFRKKa11jtZ6idb638BYjErplTgFXDiJLAAo2hfElIIAACAASURBVFN6F1gPzFZKFXTetdZntdZztNaPA99g3ER0HIgqsm0kcPIyGcqyzUUppZoAnwN9tNZXXOoY3CmKuS+0Ye4LbejWJIT5G+LRWrPtcDo+npaCzuV5IX4eeHta2HY4Ha018zfEE9vYuACcOpMLgM2m+XTpYQZ2uPLCbuO6gcTFn+F44hly86ws/iOO2FZRDm1iW0Uxb+VBAJb9FUebxmEopYhtFcXiP+LIzbNyPPEMcfFnaFI3EK01L01eS+1IPx7s08BhX0dOFg5t/rbhODUj/LicxvWDiTuRwfH4DCPj6oPEXtCpjW1bnXnLjV+sy34/TJtmVVFKcTw+g3z7cMmJxDMcPpZOZKgPqWlZBcOT2Tn5rN16glpRl89SkCk6lLgTaRyPTzcy/baP2HaOdxbHtqvJvGV7jEyrD9DmhkiUUqSmZWG1Zzp2Mp24E2lEhfuhlKJr25ps2HYcgLVbjlO7Rtku6o3rhxB3Ir3wPVt1gNi21R3zta3OvF/Ov2eHCt6zbyf24bdvBvPbN4O5r39jhg+6ocydT4DTs2ZyeGA/Dg/sx9mVK/C7tQ8Ano2bYjt7hvwUx1/MabO/50DPThzs3Y24BweTE3eEo8PuA3CYL+oT252cA/vLnOu8xg0jOXI0hWMnUsnNy2fRsu3Edm5w+Q2B9IxMcu1VodTT59iy7Qh1apXtZozG9YOJO5nB8QT7v8XVh4htc8F53qYa8349AMCyNYdp09T4mWWczeGRl3/h2Qdb0Lxh4fGVUnRtE8WGv41pD2u3nqR2NX9KyziP0hzPI3tntiBTuxrM+8UYyVj2+0HaNIswzqP3+/Hbt0P47dsh3Ne/CcMHNWdI38YEB1QiPNibQ8eM2Uxrt5ygdvWKnWvsCmk/zCRuUH/iBvXn7KoV+BY5z61nz2C98Dz/8XsO3tSZQ7d25+jQweTGxXFs+P0ADvNFfWJ7kHOwbOf5tXjtFFfX9VYBvQNjjuQj5xcopVYDnZRS+7XWJ+13xDcB/r7Cfe8HqiqlYrTWu5VS1YGmwLYL2j0DzAS+UEo9ALQDdmmtTyul3DFuOFoFbATqKqVqYswRvRu45zIZFgAjlFLfA62BdK11/GW2KZFSqhrGkwLutU9XKJfODQP5fVcKN732F55uJsYOaViwrt/b65j7gnF3+CsDowsew9QxJpBODYzqyqLNCcz83eis9GgaTP82VQu27/bKH5zLzicvX7NiRzKfP34DdcIrcyGL2cR/Hm7FQ/9dgc2qGdC9DnWr+TNp5jYa1QkktlUUd3Svw3Pv/0HPR+fh5+POhJEdAahbzZ+b21fnlhELMJtNvDy8FWazic27kpi/6hD1qvvT9+mFgDHs3rlFBONnbOXIyXSUUlQN9r7sHfAFGUe046HRS7DZNANuqk/dGgFM+moTjeoFE9u2Onf0qs9z76yi5wOz8PPxYMLoWAA2/5PI1JeXYTGbMJkUrzzRnip+nuw9dIoX3luN1abRNk2vzrXo2qb6ZZJckOnJzjz03AJsNhsDbm5A3ZqBTJq2jkb1Q4htX4s7bmnAc2OX03PwDPx8PZjwn14AbNx+gg+/XI/ZrDCbTbz6TFf8fY15viOHt+P5t5Yz9qM1BPh5Mfb57qXOVPw968BDLy52fM+mbzTes3Y1uOPmaJ57eyU97//OeM/GlO1YV+LsmtV4d+hE7Z9/MR7D9MrognU1Z83l8MB+l9w+5OlReNSPAa3JO3mChDdeKXcmi8XMyy/0Ydhjn2O12RjQpyV164TxwcfLaNQgkm5dGvL3zmOMeHYGGRmZrPx9Nx9+spxFc0Zy8FASr7wxB2VSaJvm4aFdHe6ev6IcZhP/ebwtD41ZavzMetajbo0qTJqxmUZ1g+zneT2ee3c1PR/8wfiZvWg8LurbBbs4ejKDT2Zu45OZxuX1i7G9CPT3YuTQljz/3mrGfrqOAH9Pxj7b6coyPdGRh15YaGTqFW0/jzbYz6Oa9vNoBT3v+xY/H08mjOlx2f2+NKIj/35rBXl5VqLCfRn779gyvWelMXPoa3Sp15ygyv4cG7uAVxZOZdpfP1+14wGc+8M4z2vOX2Y8hunVwvO8+ndziBvU/5LbBz81Cs960YD9PH/z1TLluBavnVeVPIgedbk7Xa8l9kchva21Xlpk2ZMYncJU4HxJbgPwuNa6xDtd7NXTFhcMwaOUag+MBzyBPGC01np5kWOffwyTO7AQ2A7sAEZhDJ+bgEXA81prrZTqjTFdwAxM01q/WSTzcxg3NSUBi7XWw+yPYZoM9MJ4DNODWutN9m3WANFAZYxq7UNa62WX2NfnwAAK747P11qX5k6NgiH4a4Wp50fo3W+4OoYDFfMSOm6cq2M4UNVHAaBPTnZxkkKq6ggA9NEJLk5SSFV7FoDdzS79zFlni9m2B7LmuzqGI68+6MPvujqFA1XTmGOoj71/mZbOo6KeNv4uxYdUZ9GfrANgb/MYFydxVH/L7mv12unU2+PPPdndaZ0v70m/XpO3/l9XFVCtdZcSlk0CJl3hfmpcZPmfGM/avOSx7Xe19yyyesZFtlkMLC5heYmZtfFpoMTen9a640WWX2xfw4BhJW0jhBBCCNeROaDX2RxQIYQQQghx/buuKqBXyv6IpQufGnyv1nqHK/IIIYQQQmiZA/r/uwOqtW7t6gxCCCGEEMLR/+sOqBBCCCHEtUbmgMocUCGEEEII4WRSARVCCCGEcCKbzAGVCqgQQgghhHAuqYAKIYQQQjiRzAGVCqgQQgghhHAy6YAKIYQQQginkiF4IYQQQggn0jabqyO4nFRAhRBCCCGEU0kFVAghhBDCieS/4pQKqBBCCCGEcDKpgAohhBBCOJE8hgmU1vImCAdyQgghhPhfo5x5sJTB7Z32uzbo2z+d+r2VllRAhRBCCCGcSOaASgdUlCAz/2dXR3BQyXIbOeP7uzqGA4+Rc4jr1szVMRxUX7ENgNQHOro4SaGA6WsAsM68z8VJCpnvmWF8kX1tned43oZO/tzVKRyo4GGQv8zVMRxZbgIga0xvFwcp5PXmYgD2No9xcZJC9bfsBkA91sbFSRzpT9ZxOmeWq2M4qOIx0NUR/idJB1QIIYQQwolkDqjcBS+EEEIIIZxMKqBCCCGEEE5kkwqoVECFEEIIIYRzSQVUCCGEEMKJ5C54qYAKIYQQQggnkwqoEEIIIYQTyV3wUgEVQgghhBBOJh1QIYQQQgjhVDIEL4QQQgjhRDIELxVQIYQQQgjhZFIBFUIIIYRwInkMk1RAhRBCCCGEk0kFVAghhBDCibTN5uoILicVUCGEEEIIUYxSKkAptVwptd/+d5VLtPVVSp1QSk0uzb6lAyqEEEII4UTaqp32p5xeAFZoresCK+yvL+Z1YHVpdywdUCGEEEIIUZI+wFf2r78C+pbUSCl1IxAK/FLaHcscUHHF/lyzh/feno/NaqPvgNYMfTjWYf3X01cz96f1WCxmqlTx5pU37qJq1QBOnkxl1FNfYbVq8vOt3D24PXcObFeh2cxdH8Jcszk6P4f8pZPRSYccG1jcsdz2b5R/KNhs2A5twrrmGwBURAMsXYeigquTv3ACtv1rKzQbQJV/PYdX6w7onGxOvfsyufv3XLRt8OvvYwmPJH7YHRWeo9Lgp3Br0gadm8O5z8dijdtXrE3lkeMw+QWC2Uz+vu1kzpgI2oZX/4dwu6EjaBs64zRnPx+LTjtVrjxaa8YujeP3/Wl4uZkY27c2DcK9i7V7f8UxFvydQnpWPptHtyxYPndbMuOWHyXExx2Awa1CuaN5SLky/f7nHt58Zz42m407+7Vm+EOO5/nGzQcZ++4C9u6PZ8I7g+nVo2nBupPxp3np1dnEJ6ahFEyZPIzIiIAy5Viz7jBvfrACm01zx61NGH5va4f1ubn5PP/GYv7Zm4i/rxcTXruNyHA/8vKtvPT2MnbtS8RqtdGnV0MeubcNAKPHLmHVX4cIrFKJn79+sEy5ivp9zS7efHsONquNOwe0ZfjDPRzWb9x0gLFvz2HvvpNMeO9+et10AwDr1u/jrXfmFrQ7dDiRieMeoHu3JuXOBOB2yyOY6reEvBxyf5qAPnnwggYeuA96ERUQDjYb1j3ryf9lesFqc6OOWLoNBq2xJRwm74d3y50p5N+j8e7QCZ2dTfwro8nZs+uibSMmfoRbRBRH7rodgMBH/oVfvzuxnk4FIGXy+5z78/dyZ7qUL+4dw62N25N05jSNXx98VY9V1No/9jPxncXYbJrb+zfnvoc6OayfOeNPFszZgtlsokqVSox5rR/hVf3ZvOEQ77+3tKBd3OEUXn/3TjrHxjgte2k48zmgSqnhwPAii6ZoraeUcvNQrXU8gNY6XilV7MKqlDIB44F7gW6lzSUdUHFFrFYbb785l0+mDic01I/BAz+gc9cG1K4TVtAmOiaCb394Gi8vd374/i8+GL+Id8bfS3CQL9O/fQJ3dwuZ53K4o+84OndtSEiIX4VkM9VsjqlKOLnT/oUKr4el+3DyZhYfLbBumo8+thNMFtzufBVd4wZsR7aizySTv/RDzC36VEieC3m26oBbZDVO3nc77jGNCXhqDAkj7i2xrVeHWHRW1lXJ4dakDabQSNKfH4S5dgO87xtJxuuPFGt39qOXITsTgMojXse9VVdy168ga/F3ZM35AgCP7gPw6vMAmV+NL1em3w+kE5eazdInmvL3ibP8d9FhZg1rVKxd1/r+DG4VSq8Ptxdbd3PDQF7qXaNcOc6zWm28NnYuX35mnOd33PMBsV0aUKd24XkeHlaFt14fyLSvio84Pf/Sdzw6rDvt29bjXGYOJqXKnmPCcqZNvIvQEB/uHPY1sR1qU6dmUEGbHxfuwNfHk19mPcyiX3cz/pPVTHztdpb+tpe8PCs/z3iQrOw8bhkyjVu6xxAZ7ke/3o0YPKA5L7yxuEy5imV8czZfTv0XoaH+3DFwHLFdG1GnTnhBm/DwKrz15mCmTf/NYds2resxf87zAKSlnaPnza/Tvl10uTMBmOq1QAVFkDNhGCqqPu63jyDn02eKtctfMwfb4b/BbMF96FhM9Vpg27cJFVgVS+e7yPlsFGSfBe/yX6e823fCrVp1DvfphWfjpoS++DJH77+7xLaVY3tgy8wstvz0t19x+usvy52ltKavXcTkVT8y44GXnXZMq9XGuLELmTTlfkJCfXlw0Gd07BJNzdqFfZ/60eFM/+4RPL3c+WnWBiZP/IU337uLG1vV4uvZjwOQnp7Jnbd8QOu2tZ2W/Vpk72xetMOplPoVCCth1ZhSHuJxYLHW+pi6gmvddTcEr5QKU0p9r5Q6qJTapZRaqZTKVEptU0qlKqUO27/+9SLb11BKZdnb7FJKzVBKuV3Q5gP7RFrTBctvVkptUkrtVkrtUUqNsy+vr5RaZd/nbqXUFPtyN6XUV0qpHfblL9qXR9lz71ZK/aOUeqrIMUqc8KuUilZKrVVK5SilRl2Qq5dSaq9S6oBSqliPSyn1oVLqbNnecUc7dxwlKiqQyKhA3Nwt3NS7GatW/uPQpmXrOnh5GZWoJk2rk5iQDoCbuwV3d+MzT25efoV/AjTVboV11yoAdPw+8PAG7wvmS+fnGp1PAFs+tqRD4BNovM5IRqfEgb46dydWat+Fs78sBCB39w5MlX0wBwQVa6c8vfC9417Sv516VXK43dCB3D+NCoH14C5Upcoov8DiDe2dT8xmsLiB1o7LAeXhBRXwY/xtz2n6NAlCKUXTSB/OZFtJPpNbrF3TSB+C7VXOq+nvnUepHhVIVGQg7m4WbunVjBWrHM/zyIgAoutVxWRyvOAeOJhAfr6N9m3rAeBdyaPg38MV59gdT7XIKkRF+OPuZqZ392hW/HHAoc2KPw7Q9+aGANzUpT5rNx9Fa41SisysPPLzbWTn5ONmMVPZ28jRslkUfr6eZcpULOOOOKpHBRMVFYS7u4VbejdnxcodDm0iIwKJrh9xyY74sl+20bFjTJnfqwuZY9pg3boCAH1sL3h6g88F14O8HKPzCWDNR588iPI1/i2YW/Qif/1Co/MJcC693Jkqd4klY+F8ALJ3bMfs44s5KLhYO+VViSqD7+fU55+W+5jltebANlLPZTj1mLt2HieyWgARkQG4uVno0asxv690HC26sVUtPO3nSqMmUSQlFv/5rFy+izYd6ha0u5bYbNppfy5Ha91da92ohD/zgUSlVDiA/e+kEnbRFhihlDoCjAPuU0q9fbnjXlcdUGV0recCq7TWtbXWDYBngJu01s2ABcC/tdbNtNbdL7Grg/b2jYFI4K4ixzAB/YBjQKciyxsBk4EhWusYoBFwfnx3EjDRftwY4EP78jsBD611Y+BG4BGlVA0gHxhpb9sG+JdSqoF9m4tN+E0FnsT44RZ9T8zAR8DNQANgUJF9oZRqAfhf4r24IkmJ6YSGF+4uNNSf5BL+4Z8376f1tO9YWNFIiE/jrn7jubnbGzzwUNcKq34CUDkAfSal8PWZU6jKlxj29KiEuVYLbEd3XLxNBTIHhWBNTih4nZ+ciDmo+DCx/4P/ImP2DGzZ2Vclh6lKMLbUwmuI7XQypirFO8IAPiPH4z/pZ3RWJrkbVxUs9xrwMH7jf8S9bQ+y5n5R7kxJZ3IJ8/MoeB3q605iCR3QS/lldyp9P/mbp3/YR3x6TrnyJCalExZW5DwP8SfxEud5UUfiUvD18WLEM9Ppe9cE3pnwM1Zr2T7UJCafJTzEp+B1WLAPicmOnyWTks8SHuILgMViwsfbnbT0LG7qWo9KXm507PsxsQM+Y+iglvj7epUpxyUzJqYRdsE1obTvVVGLlmzh1t43Vlgu5RuETk8ueK0zUlC+JZ/nAHh6Y4puhe2gUV03BUWgAiNwHz4Oj0cmYKpb/myWkFDyEwuvAXlJCViCi18Dgh5/ktPfTMeWXXwUpMrAwdSYNY+wV97A5ONb7kzXouTEM4SEFv5uCAn1JTnp4p3gn+dupm2HusWWL1+yg543N74qGf+HLADut399PzD/wgZa68Fa62pa6xrAKGCG1vpSNysB11kHFOgK5GmtCz4Waq23aa3XlGVnWmsrsAGIuOAYO4FPgEFFlj8HvKm13mPfNl9r/bF9XThwvMh+z/doNOCtlLIAXkAukKG1jtdab7G3PQPsLpKhxAm/WuskrfVGIO+Cb6MVcEBrfUhrnQt8b9/H+c7pe/bsF6WUGm6v7G6aMqW000IcdlDi4kU/b2bXP8e5f2iXgmVh4f78MHck85e8wM/zN3Eq5cyVH+9KcuiLfPpTJtxueRbr1sWQnlhxGS7p8vncatfHEhFF1p8rr2KM0r9PZ8aPJO3pvig3NywNmhcsz/ppKukj7yB37XI8u/Uvd6SSjq5Ker8uoms9f359qhnzHmtCm1p+jJ536PIbXSpPCYFKO7SUb7Wyaethnh95Gz/OfIrjx1OZM39jGYOUlOOCJiWHZceueEwmxe/zHuPX2Q/z5fcbOXYirWw5rixiqd+r85KS09m3/yQd2lfgPL2SIlzsemAy4T7wefLXLkCftncQTWZMQVXJ/fx5cn94B/d+TxlV1IoP5fDKo1407lHVOLuy+CBe2uzvOXR7T47c3Y/8lGRCnr3kpf26pUtz4tstWbid3f+cZMgDHRyWpySf4eCBRNq0q3M1IpbbdXQX/NtAD6XUfqCH/TVKqRZKqc/Ls+PrbQ5oI2BzRe1MKeUJtAaeKrJ4EPAdRi9/rFLKTWudZz/2xSa6TQR+U0r9hXEH2Jda6zTgR4zOYDxQCXhGa516QYYawA3Aevuiy074vUAERrX2vOP27wlgBLDAvp+L7uCC+SE6M//ni7YNCfUjMb7wl1hiYhrBIcU/ha9bu48vpqzg8+mPFQy7O+wnxI/adULZsvkQPW5qWmx9aZma9cLc2LjhQSccQPkEFV66fALR506XuJ2l52PYTsdj3bKwzMcujcp9BuLT2+ig5ez9B3Nw4TQbS3Ao1lPJDu09GjTBvW4MEd8uBrMZs38AoeM/J3HksHLl8OjWD4/OtwGQf3gPpoDC08pUJRjbpW4iyssld+ufuN/Qgfx/Njmsyl23nMrPvEvWvGlXnGnmhgRmbzG+/8ZVvUlIzwGMal9iRi4hPm6X2NqRf6XCtnc2D2HCr8cu0frywkL9SEgocp4npRFSwnle8rb+NIiuSlSkMZTbrWsjtu+IK1OO0JDKxCcVfkhLSD5DSFDlC9r4EJ+UQViID/n5Ns6cy8Xf15OFy3fTsXVN3CxmAqt407xxBDv3JBAVUWEDIoDx/SZccE0o7Xt13pKlW+nRrSlubuZyZTG3vhVLy5sAsB3fj/IrHN5WvkHoMyWf5259n0SnnMD6V2FxR2ekYDu6B2xW9OlEbCnHUYFV0Sf2X1Em/7vuwa+fcSNh9j87sYQWXgPcQsLIT3a8Bng1aYZnTENqLfwVzGYsAQFETfmKY8Pvx5pamD9tzmwiP3D9EP3VEBLq6zCknpSYQXCwT7F2G9YdZPrU1XwybWix3zMrlu2kc2wMlnKeU//rtNanKOHGIq31JqDYLyat9XRgemn2fb1VQCtKbaXUNuAUcFRr/TeAUsod6A3M01pnYHQKe15uZ1rrL4EYYDbQBVinlPLAqE5agapATWCkUqrW+e2UUpWBn4Cn7ccrixI/UiulqmJMAfiwhPVl1rBRFEePpnDi+CnycvNZtngbXbo2dGizZ/cJ3vzvT0yc/CABgYUXjcSENLKzjQJuRnom27YeoUbN8t2pbNu2lLyvR5L39UhsBzZgbtAFABVeD3IyoYQOqLn9IHCvhHXllXeartTZ+bOIf2Qg8Y8MJOvPlVTueSsA7jGNsZ07izU1xbH9z7M5MbAnJwb3JuGpB8k7HlfuzidAzoq5ZLw8lIyXh5K3ZQ3u7XsBYK7dAJ11Fp1+wS9mD6/CeaEmM25N2mCNP2q8DI0saOZ2Qwds9uVX6p5WYcx9tDFzH21Mt+gqzP87Ba0124+fwcfDfEVzPYvOF1259zS1gso3v7FxwyiOHE3h2PFT5Obls2jpNmI7N7z8hvZt0zOySE01hsrXb9hPnVqhZcsRHU7csdMcP5lGbp6Vxb/uIba9Y0Untn1t5i0x5qcuW7WXNs2roZQiPNSXdVuM+aCZWbls3xVPrepluxP/khkbVePI0WTjvcrNZ9HiLcR2vbJhz0WLN3NL7+aXb3gZ1vULyZn8BDmTn8C6ey3mG4zfmyqqPuScgzPFrweW7vehPLzJW+w4+mPdtRZTLfvd+JV8UYER6NSEYttfTtoPM4kb1J+4Qf05u2oFvrcaNzl6Nm6K9ewZrCmOHdC0H7/n4E2dOXRrd44OHUxuXBzHhhsjoEXni/rE9iDn4JV1hq8XMQ0jOBaXysnjp8nLy2f50h107OJ4c9re3fG889oC3ps0mIDAysX28cs1Pvyubdppf65V11sF9B+gIp5Jc1Br3cw+oXaVUup2rfUCoBfgB+ywVwwrAZnAIvuxbwSK334LaK1PAtOAaUqpnRgV03uApfYKapJS6k+gBXDIfuPTT8C3Wus5RXaVqJQKt1ctLzbht6jjQFSR15HASYyqah3gwPnvRSl1QGtdrvEIi8XM82P68fjwqdhsmj79WlK7Thgff7iUBg2j6BLbkInjFpKZmcNzz3wNGMPuH3w0lMOHkpjwXmF19b4HulC3XvjFDnXFbIc3Y6rVHPeHPkbn5ZC/rPA/Y3C7dzx5X4+EyoFY2tyJ7dRx3O41ptNaty3BtuNXVGgd3Po8b8wFq90S3W4geV89XWH5stavwat1B6p+/TM6O5tT771SsC78M6Oj6gx529fi1qQNfu9+j87J5twXbxWs831tGhkvD0V5eOLz1Fvg5g4mE/m7t5Cz0qgOVbrzEUxh1YxH05xK4Nz0cRc7VKl1quvP7/vT6PXhdjzdTLzZp+BzGv0+3cHcR41fJOOWH2XRjhSy82x0nbCFAc1DGNElkq/XJ7ByXxoWk8LPy8zYvuW769ViMfPyi/0Y9thUrDbNgL4tqVsnjA8+WkqjhlF069KQv3ceZcQzX5GRkcnK1bv48ONfWDT335jNJp5/9jbuH/4ZaE3DBpHcOaD15Q9aYg4T/3m2Ow89+yM2m40BtzSmbq0gJn3+B42iw4jtUIc7bm3Cc68voufAqfj5ejLhVaPSfU//Gxg9dgm33fslGujfuxH16xgf+J595Wc2bjvG6bQsOvf7hCceas8dt5bt0UcWi5mXx9zBsOEfY7XZGNCvDXXrhPPBh4to1LAa3WIb8/eOOEY89TkZGVmsXLWTDz9awqIFowE4fuIU8QlptGpZsUOltr0b0fVa4vHsF8ZjmOZMLFjnMeJDciY/Ab6BuHW9G1vSUTz+NQmA/HULsW5ahm3/Zsx1muPx1Kdgs5G/9AvIKt+UoXN/rMa7Qydqzl9mPIbp1dEF66p/N4e4QZeezhL81Cg860UDmryTJ0h489Vy5SmNmUNfo0u95gRV9ufY2AW8snAq0/66+ChZRbBYzIwafQtPPTYDm9XGrX2bU6tOCFM+WkF0gwg6dY3mwwnLyMzMZcyoWQCEhvkx7kPjMVEnT5wmKTGdG1rUuKo5RfmoEucPXaPsNyGtAz7XWk+1L2sJVNJar1ZKTQcWaq1/vMQ+atjbNLK/7gc8p7Vuq5T6DmPI+jv7Om/gMFADozM3B+ittd5nv1npaa31BKVUL4wbh/KUUmHAVowO4P1ANDAUozO7Ebgb2IExvzNVa+3Qw1FKvQec0lq/bb+jPUBr/VyR9a8CZ7XW5+/AtwD7MErkJ+zHuEdr/c8F+z2rtS7+MbG4Sw7Bu0Ily23kjC//PMOK5DFyDnHdmrk6hoPqK7YBkPpARxcnKRQw3ZiebZ15n4uTFDLfM8P4IvvaOs/xvA2dXK4pVRVOBQ+D/GWujuHIYgyxZ43p7eIghbzeNB5ntbf5tfOsyfpbdgOgHmvjG8Jw4AAAIABJREFU4iSO9CfrOJ0zy9UxHFTxGAgljyZeNQc7NHZa56v2Hzuc+r2V1nU1BK+N3nI/jAmxB5VS/wCvYlT8ymoeRnWwM3ATRrXz/PHOAX8At9mH6Z8GvlNK7ca4Uel8+a4nsFMptR1YhnEnfgLG3emV7W03YswN/Rtoj/HA1lj7o5u2KaXOX00vNuE3TCl1HHgWeEkpdVwp5au1zseY67kM42amHy7sfAohhBDi2nEd3YR01VxvQ/Dnh7rvusi6B0qx/RGM4fHzrzVw/i6YYhOktNb9i3y9ECh214rW+lmMjuGFy89izMO8cPkfXOTT1iUm/CZgDK+XtM1i4JJPlC5l9VMIIYQQ4qq77jqgQgghhBDXs2v55iBn+X/bAVVKNQa+vmBxjta6bHcDCCGEEEKICvH/tgNqfxj8tXWXiBBCCCH+50kF9Dq7CUkIIYQQQlz//t9WQIUQQgghrkXX8t3pziIVUCGEEEII4VRSARVCCCGEcCKbzAGVCqgQQgghhHAuqYAKIYQQQjiRzebqBK4nFVAhhBBCCOFUUgEVQgghhHAiqYBKBVQIIYQQQjiZVECFEEIIIZxIKqBSARVCCCGEEE4mHVAhhBBCCOFUSmt5GKpwICeEEEKI/zXKmQfbGhPttN+1N+ze49TvrbRkDqgoZtqux10dwcHQBh/zg1t9V8dwcFfeXuZ4XVuZ+mftBWBpwLWTq1eqkSnzhZtdnKRQpbeXALC7WbSLkziK2baHtJzZro7hwN/jTu77ZairYziY0XMaAPtaNnBxkkL1Nu4CQMeNc3GSQqr6KABO58xycRJHVTwGoh5r4+oYDvQn61wd4X+SdECFEEIIIZxIbkKSOaBCCCGEEMLJpAIqhBBCCOFEUgGVCqgQQgghhHAyqYAKIYQQQjiRVEClAiqEEEIIIZxMKqBCCCGEEE4kFVCpgAohhBBCCCeTCqgQQgghhBNJBVQqoEIIIYQQwsmkAiqEEEII4URSAZUKqBBCCCGEcDKpgAohhBBCOJFUQKUCKoQQQgghnEw6oEIIIYQQwqlkCF4IIYQQwolkCF4qoEIIIYQQwsmkAiqu2KEtp1jxxT5sNk3T7lVpM6CGw/odv51k5VcH8AnwAKB570ia9oggPSmLue/sQNs0Vqvmxt6R3NAr8qrlvGHiGMJ6dcaalc2Gh14gbeuuYm1Mbm7cMOk/hHRqhbZpdrw8kRNzf6nQHE3GjyHsps5YM7PZPPwF0rYVz9Fx2Qw8w0KwZmUD8OdtQ8lJTsUrKpwWU9/Bzc8HZTaz8z/jSFz2e7kzxbw1hqAenbFlZbPjXy+Q8XfxTK0WzMAjNARrtpFp04Ch5KakEjGoH/X/+xzZ8YkAHP38G45//WO5M7nd9ijm+i0hL4ec2ePRJw9e0MADj8GjUQHhoG1Yd68nb+mXBavNjTvi1n0IoLHFHyL3+3fLnSn0uTFU7tAJW3Y28S+/SPae4u/TeZHvf4xbZCSH77gdgKBHR+Df/06sp1MBSPpwIuf+KP/Pbu0f+5jwzmJsNhu397+R+x/q7LB+5ow/mT9nExazCf8q3rz0Wj/Cq1YBICE+jTdfnUtSQgYomPjRfVSNqFLuTI0DGzEk+h5MSrH6+BoWHllcrE2r0Jb0q90HjebYmWN8smMKAHfVvYNmwU0AmH/wZ9Ynbix3nvOCR47Gu30ndHYWCf8dTc7e3RdtW3X8ZNwiooi7u4/D8ipDHiT4qX9zoHs7bOlpV5xhzcZjvPnJWmw2zR296jP87mYO63NzrTz/3ir+2Z+Cv48HE8Z0IzLMh7/3JPHy+2sA0MCIIc3p0aEmObn5DBm5kNw8K1arjZ4da/HkfTdeca7z1v6xn4nvLMZm09zevzn3PdTJYf3MGX+yYM4WzGYTVapUYsxr/Qiv6s/mDYd4/72lBe3iDqfw+rt30jk2psxZSuuLe8dwa+P2JJ05TePXB1/1410NWmtXR3A56YC6kFIqDHgfaAnkAEeAp4E5WutGJbS3AAnAVK31i0WW3wq8jlHRdgM+0Fp/ppSqD3wG+AMewBqt9fDyZLZZNcun7GXgqzfgE+jBV89tpE6rIIKiKju0i2kfSo/h9R2WVa7iwZC3W2BxM5Gblc8XT62nTqvggo5qRQrr1YnKdWqwJKYnAa2bcuPkV1nR/q5i7WJefJScpFSWNOwFSuEe4F+hOUJv6kTl2jX4pVFPqrRqSrNJr7KqU/EcABsfHEXalp0Oy6Kff4zjPy3h8NTv8ImuTbt5U1gW3a1cmYK6d6JS7RqsadETvxZNaTD+Vdb1KDnT9kdGkbFtZ7Hl8XMXs/v518uVoyhT/ZaYgqqSPe4hTFHRuPcdQc7HzxRrl/f7T9gO/Q1mCx7D3sJUrwW2fZtQgVVx6zqQ7E9HQtZZ8PYrdybvDp1wr1adg7ffhGfjpoSNeYUj9w4ssa1PbA9sWZnFlqd+8xWpM6aVO8t5VquN98b+zIdTHiQk1JcHBn1Kxy4x1KodUtCmXnQ4X333GJ5e7vw0az2TJy7jzffuBuC/Y37kgYe70LptHTIzczApVe5MCsV9MUN4d/N4UrNT+W+bl9mSvI2T504WtAmtFMJtNXvz+oaxZOZn4uPuA0DToCbU8K3OS2tfxWKyMKbFC2xP2UG2NbvcubzbGT+/I/174dmoCSEvvMKxB+8usW3lrt2xZRb/+VlCw6jUqi158SdL2OryrFYbr03+k2lv9yY0yJs7n5hHbNvq1Kle2On/celefCu788v0gSxaeZDxX2xg4phu1K0RwI8f9cNiNpF0KpO+j/5E17bVcXczM/3dW/D2ciMv38bgZxbQqWUkzWJCy5Rv3NiFTJpyPyGhvjw46DM6dommZpHzqX50ONO/e8R+Pm1g8sRfePO9u7ixVS2+nv04AOnpmdx5ywe0blu7TO/TlZq+dhGTV/3IjAdedsrxxNUhQ/AuopRSwFxglda6tta6ATAauNRVpCewF7jLvj1KKTdgCnCb1ropcAOwyt5+EjBRa91Max0DfFje3PH7M/AP98I/zAuzm4mYDqHs35BSqm3NbiYsbsYpZ83TV/UTYMTt3TjyzTwAUtdvx83PF8+w4GLtaj4wgN3vfGa80JrcU6crNEfVW7txdKaR4/SGi+e4KK1x8zU6925+PmTHJ5U7U2jvbpz83siUvmk7br6+eIReQaarwNygDflbVgBgO7YH5VUZfC6ozOXlGJ1PAGs+tpMHUH5BAFha9SJv7c9G5xPgXHq5M/l06Ub6wvkAZO/YjsnHF0tQ8fdJeVUi4N4HSJn6SbmPeTm7dh4nslogEZEBuLlZ6NGrMb+vdKzqtWhVC08vdwAaNYkiKTEDgEMHk8i32mjdtg4AlSp5FLQrj9p+tUjKTCI5KxmrtrIuYT3NQxyrfF0iOvPrsd/IzDc6eWdyzwAQUbkqe1L3YtM2cq25HD1zjCZBjcudCcC7cywZi+w/v51/Y/bxwRwYVKyd8qpElXvuJ3XaZ8XWBT/zPMkfjocyXqv+3ptMtaq+RIX74u5mpnfn2qz4K86hzYq1R+jbox4AN3WqydqtJ9Ba4+VpwWI2rpe5ufnYL/kopfD2cgMgP99GvtWGomwfJIzzKeCC82mPQ5sbi51Pxf9trVy+izYd6lbI+VQaaw5sI/VchlOOdbXYbM77c62SDqjrdAXytNafnl+gtd4GHLvENoOAD4CjQBv7Mh+MSvYp+z5ytNZ77evCgeNF9r+jvKHPpGbjG+RZ8Non0IOzp3KKtdu7LolpT69n7rt/k5FSWM3ISMlm2tPr+fjhP2jTr/pVqX4CeFUNJet4QsHrrBMJeEU49u3d/IwqTKP/PkWPDXNo+90HeIQEVmgOzxJyeFYt+TPGjZ+NJXbdPKJfeLxg2e43JxN1923cfGA17eZOYfuzb5Q7k0d4KFknCjNln0zAI7zkTI0nj6Xd6nnUHvW4w/LQ23rSfs0Cmk3/AM+IsHJnMvkGotMKP8jo9BRMvsU7CwU8vTFHt8Z2cBsAKigCU1AEHo+Ow+PxiZjqlX1I8jxLSCh5CfEFr/MTE7CEFH+fgv/1JKkzvkRnF6/aVbl7MDV/mE/4q29i8vEtd6akxAxCQwuruyGhviQnXfwX8YK5m2nboS4Ax+JS8PHx4vlnZnLvXR8xafxSrNby/3aq4unPqezUgtep2aep4uH44SHMO5SwSmG81PJFXm41hsaBxgDP+Q6nu8mdym6ViQmIJsAzoNyZACzBIeQlFp7n+UmJJf78gh59gtPfTseWneWw3LtTV/KTk8jdv7fYNqWVmHKO8ODC0aGwYG8ST51zaJOUkkl4sLeR2WzCx9udtAzjmrp9dxK3Pjyb2x/5iVefbF/QIbVabfR99Cfa3/U17ZpH0DQmhLJITjxDyBWcTz8XOZ+KWr5kBz1vrpgPDuJ/h3RAXacRsLm0jZVSXkA3YCHwHUZnFK11KrAAiFNKfaeUGqyUOv9znQj8ppRaopR6RilV4viyUmq4UmqTUmrTlClTLh2kpELABR++67QI5tHP2jP0/dbUaBLAog8K5835Bnky9P3WDP+kHTtXJnAurXjntUKUNLR4QRVDWSxUigon5a8tLG/Vn1Prt9L03ecrOMblc4Ax/L6i5e383n0wge1vpNo9xjy0yLtuIe6buSyp05m/+g2nxRfvlvy9XVmoUmXa/sgo/uxwO+tvGUyVtjdSdaCRKWnpSlY3i+XPjrdzatVaGn/0TvnyXCSTLvFkA0wmPAY9T/5fC9CpRgdDmcyooAhypjxP7ndv4z7gafD0ruhIxd4nj/rRuEdV58zKX4s1Pf3Ddxy8tQeHB/YlPyWZ0JEVe24V5iz5fFiycBu7/znBkAc6Aka1bNuWIzw5shdfznyUE8dTWTR/S0UkKGGZ4/tkVmZCK4Xy1qZ3+XjHZzzU8AEqWbzYeeoftqfs4D+tRvN4k0c4kH4Am7ZWQCZKdZ571IvGLaoaZ1etcNzUw5OABx/h1KflHjS6bKwSz3N7m6YxISyceiezJ/dlyqzt5OTmA2A2m5j36QBWzbyHv/cms+9wavF9lELJx77Y+bSd3f+cZMgDHRyWpySf4eCBRNq0q1OmDP+rpAIqHdDrya3ASq11JvAT0E8pZQbQWg/D6JxuAEYB0+zLvwRigNlAF2CdUqpYyVFrPUVr3UJr3WL48EtPEfUJ9HSoaJ45lUPlC6qYXr5uBUPtTXtEkHCo+CdqnwAPAqt5c2zXlU/qv5g6j91Dj03z6LFpHtnxSXhFFlbmvCLCyDrpOHyde+o0+ecyOTFvOQDHflxKlWYNyp2j1iP3ELtuHrHr5pFVQo6ShtGz7dnyz57j2KyFVGlp3JRR4/47OPHTEgBS12/D7OmBR9CV3zRS7aF7aLd6Hu1WzyMnIQmvIlVLz6ph5CQUz5Rjz2k9e474Hxfi19zIlHc6DZ2bB8CxGT/g26zhFecBsLS5Fc8nJ+P55GR0ximUf2HFU/kFoTNOlbide/+nsKWcJP/PeQXLbOkpWHetBZsVfToRnXwcU1DEFWeqMvAeas6aS81Zc8lLTsItLLwwb2gY+cmO75NXk2Z4xjSk9uIVVP/yWzyq16Da5zMAsKaeMq7+WpM2ZzaejcpfIQoJ9SWxyBBoUmIGQcE+xdptWHeA6VNXM27SENzdLfZt/agfHU5EZAAWi5nOsTHs2R1fbNsrdTr7NIFFqpYBnlU4neP47zo1O5UtyVuxaispWSnEn0sgtJJRjfz58EL+s+5V3t08HoUiITOxzFn87hxEtW/nUO3bOVhTknALLTzPLSGhxX5+no2b4hndkJrzlxM19Rvcq9Ug8tPpuEVG4VY1guoz51Jz/nIsIaFU/+anEofwLyU0yJv45LMFrxOSzxES4F1CG6Mqmm+1ceZcLv4+jtfU2tWq4OVpYd8RxylCvpU9aNUknDWbjlMWIaG+DkPqSYkZBJd4Ph1k+tTVvDfpnoLz6bwVy3bSOTYGi5u5TBnE/y7pgLrOP8CVjBMOArorpY5gVE4DMYbxAWN4XWs9EegBDCiy/KTWeprWug+Qj1F5LbPwuj6cjs8kLTELa56N3X8kUqel40X5bGphVfPAxmQCI40LbkZKNnk5RnUj+2weJ3anERhRvipVUQc+mcnyFn1Z3qIvJ+b/So0hfQEIaN2UvIwzZCckF9vm5MKVhHRuDUBobFsydh8s1uZKHfpsJr+16ctvbfoS//OvVLvHyFGlVck5lNmMe6DRqVQWC+G9u5Dxz34AMo/FE9ylLQA+9Wth8vQgJ/nKqx1Hv5jJX5378lfnviQt+pWqdxuZ/FoYmXISi2dyCyjMFHxTF87uNjIVnS8acnMs5/aV7T3LX7eQ7EkjyJ40gvx/1mJpbtxcZYqKRmefgzPF5+O69bwPPCuRt9Bxvp5111rMtZoaLyr5ooIisKVeeefq9KyZHB7Yj8MD+3F25Qr8bjWqvp6Nm2I7e4b8FMf3KW329xzo2YmDvbsR9+BgcuKOcHTYfQAO80V9YruTc2D/Fee5UEzDCI7FneLk8VTy8vJZvnQHnbpEO7TZu/skb782n/cmDSYgsHD4t0GjCDIysjmdanR2Nm04RM3a5Z/7eyjjMKGVQgnyCsKszLQJa83WpG0ObTYnbaVBgJGzsltlwrzDSM5KRqGo7GZcA6IqRxLlE8nOU/+UOUv67O84Org/Rwf35+yqFfjeYv/5NWqC7ewZrKcc56un/zSLQ727cLhPD449PITco0c4/ugD5B7cz6GbOnK4Tw8O9+lBflIicUMGFNv+chrXDybuRAbH4zPIzbOyePVBYttWc2gT27Y685bvA2DZ74dp06wqSimOx2eQb58icSLxDIePpRMZ6kNqWhYZZ41rbHZOPmu3nqBWVNluujPOp1ROHj9dcD51LHY+xfPOawuKnU/n/SLD72UiFVC5C96VfgPGKqUe1lpPBVBKtQQqXdhQKeULdACitNY59mUPAoOUUuuAFlrrVfbmzYA4e5tewAqtdZ79jvtA4ER5QpvMJno8XJ8f/rsVbYPG3cIJrlaZNTMPElbHl7qtgtm86Bj7N6ZgMiu8Klu45Qmjqnjq+DlWTj9gDC9paNW3OsHVi1/QKkL8ktWE39yZ3nuWk5+VxcZhowvW9dg0j+UtjA7Y36PH0Xr6uzSbMJqc5FQ2DnvxYrssk4Slqwm9qTM9/1mONTOLzY8U5ohdN4/f2vTF5OFO+wWfY3JzQ5lNJK1cy+FpPwCw44W3af7xG9R54gHQms0Pv1DuTMnLVxPUozOdNi/HmpXFjhGFmdqtnsdfnY1MLX40MmE2cWr1Wo7NMDJVH34vwTfHovOt5J1OZ8e/yv+e2fZuxBbdEs9/T4O8bHJnTyxY5/nkZLInjUD5BuEWOwhb0lE8nzCGRvPW/ox14zJs+zaj6zbH85nPQFvJW/wFZJ4pV6aza1bj3aETtX/+xXgM0yuF71PNWXM5PLDfJbcPeXoUHvVjQGvyTp4g4Y1XypUHwGIxM2r0rTz52FfYrDZu63sjteqE8tlHvxLTIIJOXWP4cMJSMjNzGT3qewDCwvwZ9+EQzGYTT47sxYiHp6E1RDeoSt8BLcqdyaZtzNjzDc81fxalTPx+4g9OnDtJ/9p9OZxxhK3J29hxaieNAxvyVrs3sGkb3+/7gbN553AzWRjT0jh/svKz+HTHVGy6Yn5jnvvzd7zbd6LG3KXo7GwSXhtTsK7at3M4Orh/hRznUixmE/8Z0Y6HRi/BZtMMuKk+dWsEMOmrTTSqF0xs2+rc0as+z72zip4PzMLPx4MJo2MB2PxPIlNfXobFbMJkUrzyRHuq+Hmy99ApXnhvNVabRts0vTrXomub6mXLZzEzavQtPPXYDGxWG7f2bU6tOiFM+WgF0Q0i6NQ1mg8nLCMzM5cxo2YBEBrmx7gPjUcfnTxxmqTEdG5oUaNC3q/Smjn0NbrUa05QZX+OjV3AKwunMu2vn52aQZSfkmdRuY5SqirGY5huBLIpfAzTLqDoONQHwI1a67uLbBuAcUd8HYw5obWBLOAc8JTWepNSagJwi33fAO9prb+5TCw9bdfjl2niXEMbfMwPbvUv39CJ7srbyxyvaytT/yzjZomlAddOrl6pRqbMF252cZJCld42pjTsbhZ9mZbOFbNtD2k5s10dw4G/x53c98tQV8dwMKOn8VirfS3LP12motTbaMxz13HjXJykkKo+CoDTObNcnMRRFY+BqMfaXL6hE+lP1kHJk5mvmsX+9Z3W+eqdttep31tpSQXUhbTWJ4GSHsDoVoptU4Hz42e9L9LmWeDZMgcUQgghhLgKpAMqhBBCCOFE1/LcTGeRm5CEEEIIIYRTSQVUCCGEEMKJpAIqFVAhhBBCCOFk0gEVQgghhBBOJUPwQgghhBBOJEPwUgEVQgghhBBOJhVQIYQQQggnssn/ASQVUCGEEEII4VxSARVCCCGEcKLrZQ6o/b/9ngXUwPjvwu/SWp8uoV014HMgCtBAb631kUvtWyqgQgghhBCiJC8AK7TWdYEV9tclmQG8p7WOAVoBSZfbsVRAhRBCCCGc6HqpgAJ9gC72r78CVgHPF22glGoAWLTWywG01mdLs2OpgAohhBBC/D+llBqulNpU5M/wK9g8VGsdD2D/O6SENvWANKXUHKXUVqXUe0op8+V2LBVQIYQQQggncmYFVGs9BZhysfVKqV+BsBJWjSnlISxAR+AG4CjGnNEHgC8ut5EQQgghhPgfpLXufrF1SqlEpVS41jpeKRVOyXM7jwNbtdaH7NvMA9pwmQ6oDMELIYQQQjiRzea8P+W0ALjf/vX9wPwS2mwEqiilgu2vY4Fdl9ux0lqehiocyAkhhBDif41y5sFmqvpO+117j95b5u9NKRUI/ABUwxhev1NrnaqUagE8qrUeZm/XAxiP8T5uBoZrrXMvuW/pgIqrRSk13D735JohmUrnWswE12YuyVQ6kqn0rsVckklUNBmCF1fTldxp5yySqXSuxUxwbeaSTKUjmUrvWswlmUSFkg6oEEIIIYRwKumACiGEEEIIp5IOqLiarsW5OZKpdK7FTHBt5pJMpSOZSu9azCWZRIWSm5CEEEIIIYRTSQVUCCGEEEI4lXRAhRBCCCGEU0kHVAghhBBCOJV0QIUQQgghhFNJB1RUKGUYopR62f66mlKqlYszjVVK+Rd5XUUp9YYL8/hfvtW1w/5frF1Tzp9f1wKl1O0uPn7QBa+HKKUmKaWGK6Wc+t8LXkgp9XVplrnatXiOX2uUUvtcnaEopZSbUuoGpVSIq7OIspEOqKhoHwNtgUH212eAj1wXB4CbtdZp519orU8DvV2YJ0Up9atS6qHrpDP6hasDlGCYKw6qlOp/wZ8BwJTzr12RCfilSL6XgHsx/i/mHsAEF2U6r2HRF0opM3Cji7JcikvOcaVUY6XUOqXUMaXUFKVUlSLrNrgik/3YZ5RSGfY/Z5RSZ4Da55e7KNOnSqmG9q/9gO3ADGCrUmrQJTcW1ySLqwOI/3daa62bK6W2gtHZU0q5uziTWSnlobXOAVBKeQEeLsyzG3gfo5P+rlLqD+A7YL7WOssVgZRSCy62Cgh0ZpaCA1/8F50CvJyZpYgfgKVAkj0HgDdwG6CBOS7IVLTK2R/oqLU+p5SaCWxxQR6UUi8CowGvIj9HBeTiomc3XovnOPAJ8CqwDuND1R9Kqdu11gcBNxdlApgO+AH/1lonAiilDmuta7owU0et9aP2rx8E9mmt+yqlwoAlGNdQcR2RDqioaHn2KocGUEoFAzbXRuIbYIVS6kuMXEOBr1yYJ09rvRBYaO8M3wbcDXyklFqmtb7HBZk6AkOAsxcsV4CrplCkAS3P/wIsSil1zAV5wKjuvw1sBD7VWmulVBet9YMuygNGJ+8GjBEts9b6HIDWOk8pZXVFIK31W8BbSqm3tNYvuiJDCa7Fc7yy1nqp/etxSqnNwFKl1L3Yr6GuoLV+Qil1I/CdUmoeMNmVeexyi3zdA5gNoLVOcPFME1FG0gEVFW0SMBcIUUq9CdwBvOTKQFrrd5VSfwPdMX7ZvK61XubCSAVXS3vF8wfgB/uwUl8XZVoHZGqtV1+4Qim19//aO+9wu6pqfb8fIC3UoPTeRIMUzU8UUOnCFVTgUoIoKiJeAQEbRRAF5YKAV4qAUq6ICkYQKUq5QESKlAABEjoEUBBBqghIyff7Y86ds7PZ5ySQfdac52S8z3Oe7D3nOVnfs/bca4015igF9EDaXlsOeIMBCvyqYS0A2L4pxwvuBVwpaT/K35j/Rt9W+9OSlrD9N0mLAK8V1IXtAyQtRfoc52gb/1MBOTWucUla0PZzALbH5bCOc4GRhTSRtdwsaRNgT+AqYO6SeoBnJW0JPAqsB+wKIGkOyu2IBDNBdEIKeo6k1YCNSYbWFbbvKqhlduBS25uU0tCJpK/bPrq0jmDmyIbV/wCjba9YWk8nee3PZfvFghqOIHn37wRa3ljbLpq4VQuSdgIetH19x/iywMG2dyujbFokLQGsbfsPBTWsSnJwLA78yPbP8vhHgc1sf62UtuCtEQZo0FMkdXtq/6ftVxsXk8mxX59ueRmC/pG0GLAUyav3WLft74b1LAhs3q6J9EDx7IB/OAsiaTSwDMnreZ/tuwtLankW12jFX9dAbWu8VrIj4RNM+927oKRDIRheRBZ80GtuAZ4E7gXuy68nS7olxxSV4GXgDkmn5fI0x0k6rpAWJC0o6QhJd0t6Kv/clceKZMXncibXA38EfgAcBVyVM3TfW0jTZ0jraQNgXlKyz4bAzXmuhKYaP7uPSBpPik09HdgdOE3SHyUtU0JTGw9SNplmKjWu8YGQVCRZKx97P+Bs0i7WjaSYZ5FiQvcvpGk3Savk15L0vzlL//YcAx0MMcIDGvQUSScD57ViLCVtRvJgjQWOtb1OAU27dBu3XSQRSdKlwJXAGbYfz2OLA7sAm9huvCahpAk0xq1uAAAgAElEQVTA7rZv6Bj/APAT22sW0HQPqarCsx3jCwM32F61gKYaP7tbSVuQT0paAfih7a1zrOo3bG/WtKY2becCawJXAFO9oLa/UkBLjWu8vzhPAbfZXrpJPVMPnmp+jurcuVKqaDLJ9ioFNE0khQG8mkMXvgZsBqwNHGL7Q01rCmaOMECDniJpvO3R3cYkTbC9ViFd8wDL2i6VbNCu5R7b73yzc4Os6b7+biqS7re9cgFN95Ky4J/rGF8QGF/oJljjZ3e77TXy69mBm2y/N7+fZHvUgP/B4Gqr5uGv0jX+OvAw05bScn6/lO0iJewk3Q181PbDHePLAZcVWudT7x9KJcZusH1sfn9La80HQ4fIgg96zdNt2zcAOwDP5BtjkXJMkrYCjgbmBFaQtBZwaMFEiIclfZPkRWvV2FsM+CxQqrzQxZJ+T8o8b2lYBvgMqe5lCb4P3CLpsjZNy5JKsBxWSFONn914SaeRvIyfIG0xI2leYPZCmoBkaFb08FfjGn8Q2Nj2I50TKldqDGAfUum6+5j2u7cyKSu+BFOUkqGeISW5fr9tLrLghyDhAQ16ilJbwEOA9fPQNcChwHOkm9D9BTTdDGwE/NH22nnsDtvvaVpLPvbCwP4kY6HVRu7vwAXAkbafLqRrC/qSDgT8lZR0UDLzdWHgox2aLs3drErpqeqzk/Q2YDfg3aTuMKfbfj0bfot2erEa1jb14c928Ye/2ta4pD2Aa2zf1mVuL9vHF5DVOv5spPqo7efqJttFassqlWD6Cemh6sJWhQBJHwG+aftjJXQFb50wQIOekb2cR9j+Rmkt7Ui6wfY6km5tM0CnblsGQTA41PbwF8w8kuaz3VnMv6ljzwHM3/4A2vL02/5nCU3BWyey4IOekZ+Ma+zzPDEHrc8uaRVJxwPXlRbVDUlFOupIml3S7pIOk7Rux1zRRgLdkHRHaQ2dlPrsBkLSxYUlvNal/FkRr8cQXOONJ7TNIHeWOrDt11rGZ86E34hUG7TxnbVg5gkPaNBTJB0DrEJqk/av1rjtEj2yW5rmBb5FypgEuBT4nu2XS2nqD0mP2F62wHFPJZU6uhH4NHCV7a/muSIB/pK26W+K1AbzHU3qmR4FP7v+PhsBF9leokk90wjoi03dH9gW+ArwNvf19G5SS3VrfCBKrad87K/2NwV8y3axLk2S1gF2ArYmdYvagxRGUSQsJ3jrhAEa9BSlfuud2PbnGxcDKPWiXw64v5bi5UptQbtOAavanqtJPfCGTOo5gBOBtwNjgOtb26cNa3oV+CXdPWb/aXv+hiXV+tm9TmqV2K0h9gdsF0vQ6Hj4E+nh77ASD3+VrvEL+psCNrI9okk9Uw8uvUyqk9qtleu+thuveavU2nl74BHgLFLL5/G2V2haS9AbwgANhi2SvgAcDjwArAB80XZ/F/zGkPR3UmJN5xO7gOtsL1lA0922V+sY+zZJ56KFSh7dDOxie2KXub/YbrzIeqWf3URga9v3dZkrcp5qpNI1/gywM9AZUyng17YXa1oTgKTrgL1s39xlrtR370ngHuBHJM/+y5IedIVtcIMZI8owBT1F0tzArsAoYO7WeCEP6D6kYspPSlqR5E0rboACFwHz2Z7QOSHpj83LAVIpn81tTy1HY/tQSY8BJxXStA/wfD9zWzcppI0aP7vv0H88/14N6ngDSu1BDwSWp+1+UygBsMY1fj3wou2rOieUGjGU4nNAfxUdRvczPtgsTvKkjwF+JGkcMI+kOWx389QGlRMe0KCnSPoNcDcpRudQ4FPAXbb3LqBlmriuGuO8gmA4k42obwB30FYHuGRpqGDokx0dW5KM0fWBK2zvVFZV8GYJAzToKa1SR614q1yj8FLbGxXQ8gR9BfEBdmx/7wLtAFtIEn019gw8Btzogl9ISavRVyOxpekC23cV0jMHyZu+NbBkm6bzgdPc0SawQV1VfXY5YeQ526d1jO9FKk/zoxK6soZrbK8//d9shtrWeJuuxdo1tZocFNSzIHAA8Emglez3BOm7d0Qt8fQAkhYAdrN9TGktwZsjDNCgp0i60fb7Jf0J+DLwOOnm3HicjvppA9jC5XrBb0ZKgLgPeDQPL03qMvJl25cV0LQfyZtwNqngdEvTjsDZto8ooOks4FngjA5NuwAjbe9QQFONn91E4L22X+kYn4tUOLxYvVtJG5PWVWcv+MarYlS6xtcmbf8vyLTr6VnSerqlaU1Z16XAlaSOX4/nscVJ371NbFdVIqpkxYDgrRMGaNBTcuLPucAawP8C8wHftn1yUWEVIekuYAvbD3WMrwD8wfa7Cmi6lxQv+2rH+JzApEIJGgP1Xb/X9qoFNNX42fVb2H2guSaQ9AtgNWASfVvwRapiVLrGJwC7276hY/wDwE9sr9m0pnz8gb57/c6VIpLthiaRhBT0FNun5pdXAUWzEyVdyABFr12uF/wc9Hlg2nkUeFvDWlpMIW1zd8bmLUFb7F7DPCNpO+Bc21OAVnvA7XhjFnpT1PjZIWmxzm3bvK1bmjVLGsAd1LjGR3QanwC2r5dUpART5mFJ3yR5QP8OU9fTZ+nrDV8T4UkbgoQBGvSUvO23LW/Mej20gJyjCxxzRjgduEnS2fRdzJcFdgBO6/evBpd9gCsk3dehaWVgz0KadgSOBE7M5WoELETaGtyxkKZun90yWU+pz+4o4PeSvga0tmzfB/yA8t+B6yW923ax7jlt1LjGL5b0e+DnTLuePgNc0u9fDT47kJoHXCVp0Tz2d1IVke1LCFLqftbN0BRQw8NW8CaJLfigp0i6BHgOuBl4vTVeOkA8b7O1tmzvKZXA0qbn3cDHSYkHInnVLih5o87exVZyTUvTTU4tVosiaRHS9eofFWh5F32JLLV8dluQDIbVSTfpSaRkkaKtOHPIwkrAZFIMqEhb8EXiUmtc4/mz67ae/lBKU41IWoVkaHZ6YJcjJW5FO84hRhigQU+RNNH26qV1tCNpA1Iiy0OkC/wypALnfyooaxokvb20cSVpWeB5289KWp5U7+8u25MKano/yWC5KRvtm2dNpXucBzOApOW6jZcqw1TjGq+VXDFgKVKXqH+1jU9TS7VBPRcBB9q+vWN8NHCI7a2a1hTMHP0VLw6Ct8p1kmqJ+WpxDLCZ7Y/Y/jCp88n/lBIjaQtJkyVdI2ltSZOAGyT9NWcNl9C0Pylu9/qcSHYJsAUwVv33hR5sTYcAxwEnSfpv4ARSUtsBkr5VSNPmba8XlHSqpNsl/apUzKWky9peH1BCQ39kQ3MhYKv8s1BB47O6NT4Qkn5a8NhfIZVc2guYJOkTbdOHl1HF8p3GJ4Dt8aSQr2CIER7QoCe0xefMAawCPEgFW25Z2+2dx+821qCeCaRyMAuROut8LCcdvAv4ZYli+dkIHg3MS/IUr5g7SI0Abijh1c5rai1gLlI5r6VtPy9pnqyp8c9Pbc0MJJ2adZ0CbAN8xPYnC2i61bmPuSprtiBpb2A3oFV2aWvgp7aPL6ClxjU+sr8p4DbbSzepZ+rB03fvg7ZfyJ7ic4AzbR/bvt4a1nS/7ZXf7FxQL5GEFPSKLUsLGIDxkk4DzszvP0WKUS3FlFbha0kv2r4ewPZdOUatBK/bfknSK8BLwFNZ078kFZLEazk270VJD9h+Pmt6SVKprOV2RtteK7/+H02n7uwgUrMXYVdgndYWrqQjgT8DjRug1LnGnyRl5bcLcH6/aNe/aIbZbb8AYPuhHMZ0Tg6pKHWybpK0m+1T2gcl7UrZ63nwFgkDNOgViwJv74zNk7QVqdtIydZ7/wXsAXyFdPH8E6mYeCmelbQ7sACp1NC+wFhgE+CFQppukfQrYASpaPgZOaFsI6BUcs0rkua1/SIpqxuY2qWllAG6aN6uFbCAJLlvG6nUw8OKki7Imlqvp1Kw3BgkTe0JPq9TzoCpcY0/CGxs+5HOCUklyx09Lmkt2xMAsid0S1IViFIhVvsA50lqdyCMBuYkedaDIUZswQc9QdIfgc92KdC9MmnLrfFWnLUiaRngIJKn4zuk7fhdSUb6112gLaBS28vtsqZzSJnCOwGPAD9uT0JoUNNctv/dZfztwBK27yig6ZCOoRPzNu7iwA9sf6aApo8MNG/7qqa0dJKN9V2A8/LQJ4GfuUB70ErX+B7ANbZv6zK3V4lQhXzspUk7EI93mVvP9rUFZLWOvyGp2gOkBgJXltISzBxhgAY9QQN3Y7mtREcPSW8IWG+nZFxqMGNIdfVdrxFJC7TCE7rMLdvNu9Ykkt4LrE/efbB9a0k9wYzRT8WAu21PLCosGDbEFnzQK+YZYK5UR48pJKPlV8CFpLivKshP8duSSkK9RuotfortBwrpuYWUKHJWKQ2daIC+65JK9V0fSSpa/hip8PyBwAeBu4DDbZfo0PRHoJUYdYXt9koKv2vNNUlHcs1D+WfqnO2nC2iqbo3D1HJHrTqgrYesC0rshLRp2h/YHfi3pKOBrwPXAt+VdJrtH5bSFgwfwgANesXlkr4PHNTunZL0XVLnmsaxvVa+uI8hGaF35n8vs/1aCU0Ako4gFVS+AlicVKT7AVKQ/+G2f1NA1sKkrPxxkh4HzgJ+bfuxAlpaHAts0iWsYwXgD0DjfdeBXwB3kGJSd86vjwQ2BX5GMiSapj2msjOrulS85T9IBdVb37POJJsSbXqrW+OS9iNdn84GbszDSwNnSTrb9hGFpH0aeDf9VAwAwgANZprYgg96Qr4wnUraLp2Qh9cExgNfaGVUlkTSDsCPgSNtH1VQx9RwhRyXdpXt9SQtDFxdqBxMe3mhD5FuituQPHtn2W68JqFSy8R3dT4sKHW1urNE2RVJE/KDjYC/2l6qc66ApvbPbpoyTKXKMkk6FtiA5DU7ixTnWPRmU+kavxcY5Y7ObHmNT7K9StOa8vFvt72GpNmBvwGL256S56prNhIMTcIDGvSEHMA/RtKKwKg8PMn2g+2/J2mUG+w6ImkpUp/urYFngH3pS4goxZS2bcglgdkBbD+jgvVgWti+Grha0l4kz94OQImi2DX2XZ8tPyjMD8wnaflcpmYRUjZuCdoz81uvye/fUUKQ7b3zWt6A5E07Xqlg/km2J5fQ1E5Fa3wK6RrQWSVkCcpVeoD+KwZsTLmKAcEwIzygQaM06ZGRdBXJUBhLynqdJu6sRBxa1rUD8APgHmA14L9s/17SO4Bjbe9UQNPZtnds+rjTQ5X1XZc0BmhlcH+ZVOLLpO3K7xbyonVm5k+D7e82paUbkhYiPTQcRmqleMp0/mSwdFS3xpU6a51AinNuPWQtC6wM7OkCLS+zrs6KAeuQPMbFKgYEw48wQINGUYNdNCQ9RF+R7vaF3urOVCIOLQlISRorAvfbfraUjqFKyVIweVtStl/LN+q1gEdt/62EnhlF0gG2/7uhY40gPTjsQPLC/pYUb1mytmWVKDWfaFV6aD1k3ZSbMFSBpLeRSh89avuJ0nqC4UEYoEGjlIpJGwpIeiepDuhuBY49YC/sElmv2dDbnnRjvsT2xFwM+0BgnqYeZDo0Dbh2bd/SlJY3S8O7D/8iefXOAu6no1uT7d92+7tB1lTjGp8XeLUVA5qvAf8BPGS7WKiQpJOB421PUmr88GdSE4GRpGvUWaW0BcOHiAENhj2StgautP1cfr8QsIHt3xXSswZwNCn263ektoQnkra5jimhKeuZAFwM/Jty2dPtnEaK+bwROE7Sw6SSR/uX+uxISXWTSC0U4Y3Z3TU3XGjyM/0N6Xysln/aMX294ZukxjV+CakJxX1KTTv+DPwS2FLS+20fUEjXh2x/Kb/+HHCv7U8qNVy4mPRgEQQzRRigwaAjacm2UievFJBwSLs3IRdWPoRk/JXgFOAk0s1mc+AWUnmoT9l+uZCm95Li9D5GanN3FnBF4czl0cAatqdImptU2mflbt1ZGuRrpPqtL5FK55xXQ4WHGaSxz9L2Z5s61pugxjW+sO378utdSNn4e+Us+JuBUgZo+3V6U9IDBbYfryBPMhgmxBZ8MOhIesT2sgWPf3tn16OBOjc1oGeacj1KPZ+XryXmS9K6pISDTYD9bF8wnT8ZLB1VlBTqRq5FOoYU5/gwqQj9hIH/qiwNx1/vbPsX/W17ly5kXtEan3ptknQtcFTLu69CHeTysceRdmMeBcYBq2Xjcw5gou1Or3YQvGnCAxo0QelH5vGSfkiqAWpgL5J3oRRzS1qbvvPyArBGqwRTyTjCnIm/NvAeUjJEyYSD1dTXTlXASvl9K4msWCtV25MlnU/qAPZpYFX66t82iqQ9bZ8wA7/aZIODVvez+Rs85gxR2Rq/XanT0KOkzPfLYGqYUEl2B44jNcrYp23XYWPg98VUBcOK8IAGg04FHtARwMEkb4dIF/nvlSolkr0L/WHbjccRSvocKWN5blLZlbGls10lLTfQvO3O2omDTq5zuyPJ8/kX0jb8RQVDJ6ryDNdMpWt8HmBvUt3P023flsfXBVayfWZJfUEwmIQBGvQEScfTPcZMwC62F2hYUvAmkDSF1FbykTzUmbX88cZFZfJ29yiSprs6mxs0rGUKcDtwPvA8bzxPJTKpqzVAs7dxN2B52nbcbH++gJbq1rikTW3/Xz9zR9rer2lN+dg/AB60fXLH+L6krkhFdAXDi9iCD3rF+Lc4N2hI+pHtfSRdSBfjuKRR1Q1JmwLftL1pgcNvWOCYAyJpAVJ719Gk7W0Ba0q6GdjV9vMFZB1K31qar8Dxu7GGpG7nohWqUPLh73zgauByUhmfklS3xoEfS9rX9tRt7VwX9HTS9ncptiTV/ezkWNIDWBigwUwTBmjQE2yf0d9cjnEqQWv7qtTxuyJpI+Bk+sowHQ78nGQwfL+QrHWAY2pJhMocR2r7t6P7+lCLFE5xAvCZpgXZ/k7Tx5wB7ihRE3UGmbcib1mNa3wz4BJJc9n+ba72cA7Ju75VQV1ufec6BqfU0C44GB7MVlpAMEuwfYmD2r45/3tVt58SmjLHAF8EFiHdbK4HzrT9vhIFujPLATdLWq/Q8buxnu3vtN8InTiUVA+0cSTNLWkXSR9X4puSLpJ0rKS3l9BUORdJ+o/SIjLVrXHbD5Fi0w+T9CVS3/V7be/UKk5fiBclrdI5mMdeKqAnGIZEDGgw6Ej6i+1lChz3DgaofVgqi7pLeaEHbK9UQks7ucvP8cDdpDql7YZf45n5ku63vXI/c/fZfsMNsgFNY4FXSVneCwMTgQuB9YG1bG9ZQNOBtg9v+rgDIemfpO+eSOfqFdJ5g4JhARWu8dZ1YAnSLsj/AT8oqSnr2oJ0nr5HX8WQ0aS6pPvY/kMJXcHwIgzQoCco9TbvOgXcZnvpJvXANFnUe+R/W1vynwJezJ60xpH0IPD1tqGj298X9IIiaQPgXFKyRuviUCoz/wzgAeCw9mLhkg4GVrX96QKaJtpePddD/KvtxdvmitRtzE0V+ruQ2/ZhTeqpncrWeHUVMVpIWh34Bn2xoJNIdUrvKKUpGF6EARr0BEmT6fN4dGLbKzYsaSqSrrW93vTGGtTzvwNMu1CG8KKk0IAVgS+3ysGUJCchnUbqYDOBtL7WBm4FvmD72QKapnqvaymUL+lrXYbnBb4ALGK7aLKUpG1IHmIDV7tcC9zq1vhQI8eobmW7yZqywTAlDNBg2CNpArCn7Wvy+3WBE93WjWhWJ3tljwBOcWUXBUkrAe8mPdxMsv1AQS1PkGp/ilRT8uzWFLC97cVKaQOQND+pruSuwFhS0k2xWpeSTiQVWG/1Dt8BeMD2Hv3/1aBpqW6NZ+O8HZNazk6w/c8Ckt6ApNlJyVJjgI+SHiL+s6yqYDgQBmjQE9pimVoY+Iftv5TQ046k95HKmiyYh54FPl8wvqqzPWHrpnON7ckFJCHpHbaf7DK+DCkL/agCmna2/Yv8ej3b17bNzWj3n15r2mWg+YGqQQwmOQTmq6TwkjOAY20/U0JLO5ImAau3DL5cYugO26MKaKlxjXfbDRkJrEEqNXZlw5KmIunDwE7Ax4AbgfWAFW2/WEpTMLwIAzToCf3EMo0E5gTGuII+2XlLV7afK6zjkC7DI0nehe/YPrvLfGPkbO7tSB6PpYDzbH994L8aFB3VbXe3I2k+UshEkY5abTqOArYBfgr82PYLJfW0I+m3wL7OXatyXPYRtscU1lXFGu+PfJ7G2l6n0PH/SirYfxLwO9v/lDTZ9gol9ATDk6gDGvQE212LPEsaTarn+OFmFfV50Do9jq0ydi7QtSYf97vdxrMX63L6tnUbI2/dbk3yeKwKnEfydjSePNYuq5/X3d43hqT/ImUDj8jvXwCOtH1iIUlfA/4NHAR8q61MYw2F6BcB7pJ0Y37//4A/S7oAmm0GUeka74rthyW9raCEc4FPkkImXpd0PgNUFAmCt0IYoMGgYnt89hSVYET+d/5Cx39T2H66YJHnJ0jbbAeRQgEsaetCWlq4n9fd3jeCpIOAdYENnFuCKvWHP1bSSNvfa1qT7ZrrOX+7tIA2alzjXZH0TtJDRRFs7y1pH1L3qDHAUcACkrYH/lCTlz0YusQWfDCoSFqMdMF6X2kttaPUIemgQuVg9gV2JBntvwJ+Dfxf4eoFLwL3kzx5K+XX5Pcr2h7R398OoqZ7gDVtv9wxPg+p3NiqTWsaCuTwl/Ze8E8X0FDjGu/WJngkqS7ozrb/3LyqN5K9sZuTjNHNbEfThWCmCQM06AmSjqf7hXRdYG/bFzavKpHrSe7dKtsjaWFSdnDj5Y7y8bsVyB8JPAbsYvuu5lUlsjdvDOlGvQpwCCk+7t4CWpYbaL4VV9gkku6x/c5+5u62vVrTmmpG0heBw0jdc6bQFxZQ0uiraY1/pGPIwFPAfbZfaVrPjCDpw7b/VFpHMPQJAzToCV2yg1sX0ptKloEBkHSrO3pldxtrUE+nYWXgqdLJLJ1Ieg8pXm57V9CpqQYkXQEcbvuKjvGNgIP7i4WeVZF0H/BB2/8oraUbpde4pA/Yvr7p406PXHppe1KC1iW2J0raEjgQmKfUtTMYXoQBGvQEScvafqS0jm5Iuo0Us/dMfj8SuMr2e8oq60PSCFLQ/062P1ZQx0IkrxCkntTFKgZI2hUY2SqPI+lRUjyvgG/aPqmAplHA+cA1pBaFJiXWrAd8wvakpjXVjKRLgG1qKt1T2Rpvr/TwZ9sfLKWlHUk/A5YhxcyuAzwMfBDYv1QjgWD4EUlIQa/4HaljDZLOtb1tYT3tHANcJ+kcksGwPVC8d7akOYH/IHlgNidlnp5cUMtPSUbwZJKRt5yk84AvFdoO/BLpvLR4wvZSuRvLZaQSMY1ie5JSi8KdgFGk8/QnYPfOuNAASNUCrpN0A21JNba/0rSQStd4e9Lh3AWO3x+jgTVsT8nft38AK9t+vLCuYBgRBmjQK9ovpMXiu7ph++eSxgMbkXRuY/vOUnokbUpfV5FxpB7177f9uVKaSJnBbwOWaXVgyWVrfgwcnH+aZjbbT7W9/w2A7Zdz0k8R8vHHkbKqDdwVxme//AS4ktR3fUphLVWu8RyTPlvb66nX0hLJWplXbE/JGl6WdG8Yn0GviS34oCcMVDS8JvJW99ak4vhFtrolTQGuBj7r3PlI0oOFEzMmkozgFzvG5wOut716AU332165y/hswP0lzlfO5j4VeB+pP/1swJqk7fhdbT/ftKaakXSd7XVL64Bq1/hD9CVndVIsWautAgVMW4WilUS2RgldwfAiPKBBr1hT0vOkC9Q8+TVUUAy7pq3uzPtIGbiXK/WnPhuYvaAegCnd4vRsvyCp1FPqZZK+Z/ugjvFDSVvwJTgOuJPUunEKQK7dejBwAvCZQrpqZVzOhL+QabfgS3j2qlvjtpefkd+TNKrh+OJ3NXisYBYlPKDBsKXLVvevgeNn9KLfBJLWI2ncluRRO8/2TwvouA3YgO6emHG212xW0VRv9amkJJ/b8vCawHjgCyWKYUu6z/Yqb3ZuVkXS5C7DRTx7Na7xGaXWXaWaEqeCoUcYoMGwpcat7v7I28qbkjxrn8tjjXk9at0KhKl1G0flt3fafqBjvsnz1DUsIM+FAToDSJqzRMJPzWt8epQsGzcQteoKhgaxBR8MZ2rc6u5K3s69NP+0OJNcWaCB4y8/I79XYCsQp5aXDw7wK42dJ+BaSd8GDnPb07ukg4Hq6jnWQg5T2JAUBrMVsFjTGmpe4zNArZ6iWnUFQ4CaewgHwUxh+1bb++UC098B1gbmlHRxjkurnVJ94QfizNICutDkedoLeA9wv6RzJZ0j6QFSaMCeDeoYEkhaR9KxpDqSF5B2JGrvFlXjGg+CYUcYoMEsge1rbe9J6uzxI1JRZWBqcfEaqdG7UKNR3Nh5sv287e2AzYCfAT8n9cb+z/aC5hWvqUaQ9P3cBelwUgmmtYEnbZ/RaghRMTWu8SrbclLnuQqGCLEFH8xSlN7qHgbUaBQ3To5DfWCAX5nV19QXgXtIzQIuyrUkh8raaUxnbsv7bOvhRdKGpEL5DwMntGJlbX+gKU1Zx2W2N5uBX/30oIsJhi3hAQ2Cip7iJS3Z9rZWr0dxhsB5qmZNFWJx4PvAx0nhCmeSyrOF02NaxgIjACStRWq28AgppOPEgrreMSO/ZHviYAsJhi9xMQiCurx61wPLQvNej/6QtKTtx/LbWoy96s5TBzWtqcax/TpwMXBxbuW4JTAv8KikK2zvVFTgwDS5xudp+27tDJxu+5hcFWNCgzo6WVDSNv1N2v5tk2KC4UkYoEFQFzV6zmo09mo8T0EXcpvSc4BzcuvLfg2bwaTS7e72dbwRcEDWMCVXDijFgqSHhq4lq4AwQIOZJgzQYJakUq8e1Ok5q9HYq+48VbymiiFpLlKTheUpf78ZS2rD+1zbdvd/07fd/YUCmq6UNBb4G7AwcCWApCWAlwvoafGw7c8XPH4wC1D6ghAEpSjm1ZN0PN0NKNTNYmkAAAcpSURBVAELNallBili7A3B81Sjp7g05wPPATfT1oqzEDVud+8D7AAsAaxv+9U8vgowspAmgHdKWs/2te2Dkj4EPNbZDCII3gphgAazKiW9euPf4tygUamxV915mg41eopLs7TtzUuLyFS33Z2bGZwNKQlJ0t7A9sBkUrm4UtwA/LPL+EskXVs1KycYjoQBGsyqFNvCtX1Gf3OSjm5SSxvVGXuVnqeBqC4soAKuk/Qe23eUFkKF292SViV1axsDPAX8mtQie8MSetpY1PbtnYO2x0tavnk5wXAkDNBg2FKpV296bA98vemDDkFjr8h5GqJrqiTrA5+VNJm0BS+S42+NAlpq3O6+m9Qdaivb9wNI2reQlnbmHmBunsZUBMOaMECD4Ux1Xr0ZoMZt3CLG3nQodZ6G4poqyRalBbSodLt7W5IHdJykS7K+Gq4BN0nazfYp7YOSdiXF8wbBTBMGaDBsqdWrJ6k/b4uo4+bTSRFNNZ6nWtdUrdh+GEDSogzsVRt0atzutn0ecJ6kEaSSUPsCi0k6CTjP9mWFpO2TdX2KPoNzNDAnqZJAEMw0Sg+FQTBrIekR28sWOvZk0jZu1xp7tldsWNL0jL3bbC/dpB6o8zwNRMk1VSuSPg4cAywJPAEsB9xle1QBLVNI2927tm13P1jhOhoJbAfsYHujwlo2BFbPbyfZvrKknmB4EQZoMEsi6S+2lymtoxaGmrFXI7Gm3oik20gZ55fbXjsbNGNsf7GAlq1JHtB1gdZ296m2V2haSxAEsQUfDGNq3MIFkPTejiED/7D9lxJ6AGq8Cdd4nmpdUxXzqu2nJM0maTbb4yQdWUJIxdvdQTBLEh7QYNhSq1dP0rguwyNJ8VVjbDdeFLtSY6/G81TlmqoVSZeTjL0jgEVI2/D/z/a6RYVlatruDoJZjTBAg6ASJI0Gfmj7wwWOXZ2x1x8lz1Pw5sjexpeA2YBPkXqM/9L2U0WFBUFQnDBAg2FLjV696SHpFtuduotRq7FX6jwNxTVVGknLAavYvlzSvMDstrt12QmCYBYiYkCD4cwxXcZGSqrOqwcgaTEq66aTO5/MV1pHO4XP05BaU6WRtBvwRZI3fSVgKeBkYOOSuoIgKE8YoMGwpb/6ftmrdxxQxKvXTzedkaTs3L2bV9Q/JY29Gs9TrWuqYvYA3k/qLY7t+3JN0CAIZnHCAA1mOSrw6nV2zDGpMPZXbT9RQE+Vxh4Vnqf+qGBN1cq/bb8ipZwtSXNQmZc/CIIyhAEazHJUsNU9zvYjBY/fjRqNvRrPU1cqWFO1cpWkA4F5JG0KfBm4sLCmIAgqIJKQgmHL9Lx6tovcCNsTaCSda3vbEjo6NC1bm7FX6Xmqck3ViqTZgF2BzUilqy4lFX+PG08QzOKEBzQYztTo1YNpa0jWUjfyd0BVxh51nqda11SV2J4CnJJ/giAIphIGaDCcqXUL1/28LkmNxl6N56nWNVUVkm4faN72Gk1pCYKgTsIADYYzNXr1ANaU9DzJ6Jsnvya/t+0FCmiq0dir8TzVuqZqYwppHf2KFPP5Ulk5QRDURhigwXCmRq8etmcvraEL1Rl7lZ6nKtdUbdheS9JqwBiSEXpn/vcy268VFRcEQRXMVlpAEAwiNXr1qsT27LYXsD2/7Tny69b7Ep7GWok1NYPYvtv2ITmR7ELg58C+hWUFQVAJkQUfDFskvQ78i+zVA15sTVFuCzcYwsSamnEkLQXsCGwNPAOMBc6z/UJRYUEQVEEYoEEQBEFPkXQVMD/J6DwHeLp93vbT3f4uCIJZhzBAgyAIgp4i6SH6QhTabzItT3HEzwbBLE4YoEEQBEERJI2yPam0jiAImieSkIIgCIJSnFlaQBAEZQgDNAiCICiFpv8rQRAMR8IADYIgCEoRMWBBMIsSBmgQBEEQBEHQKGGABkEQBKV4pbSAIAjKEFnwQRAEQU+RtBzwrO3n8vsNgU8CDwMn2A7DMwhmccIDGgRBEPSascAIAElrAb8BHgHWBE4sqCsIgkqYo7SAIAiCYNgxj+3H8uudgdNtHyNpNmBCQV1BEFRCeECDIAiCXtNeXmkj4AoA21OI0ktBEBAe0CAIgqD3XClpLPA3YGHgSgBJSwAvlxQWBEEdhAEaBEEQ9Jp9gB2AJYD1bb+ax1cBRhZTFQRBNUQWfBAEQTBo5CSknYDtgcnAb20fX1ZVEASlCQ9oEARB0FMkrQrsCIwBngJ+TXJ4bFhUWBAE1RAe0CAIgqCnSJoCXA3savv+PPag7RXLKguCoBYiCz4IgiDoNdsCjwPjJJ0iaWMi+z0IgjbCAxoEQRAMCpJGkDogjSGVYzoDOM/2ZUWFBUFQnDBAgyAIgkFH0khgO2AH2xuV1hMEQVnCAA2CIAiCIAgaJWJAgyAIgiAIgkYJAzQIgiAIgiBolDBAgyAIgiAIgkYJAzQIgiAIgiBolP8P1FAwKJs2v/EAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# obtaining pairwise correlation values for the variables in the train dataset\n",
+    "# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\n",
+    "pearsoncorr = data.corr(method='pearson')\n",
+    "\n",
+    "# visualizing the correlation matrix as a heatmap to make interpretation easier\n",
+    "plt.figure(figsize=(10,10))\n",
+    "top_corr = pearsoncorr.index\n",
+    "sb.heatmap(pearsoncorr, \n",
+    "            xticklabels=pearsoncorr.columns,\n",
+    "            yticklabels=pearsoncorr.columns,\n",
+    "            cmap='RdYlGn',\n",
+    "            annot=True,\n",
+    "            linewidth=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the p-values associated with the correlation values using the code below.\n",
+    "\n",
+    "`from scipy.stats import pearsonr`\n",
+    "\n",
+    "`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n",
+    "\n",
+    "In this example, all values were too low to be informative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using a scatter plot matrix to visualise correlations\n",
+    "# plt.figure(figsize=(60,60))\n",
+    "# sb.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\n",
+    "These variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n",
+    "\n",
+    "Variables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2830, array([0, 1]))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n",
+    "\n",
+    "#this confirms that NT_EFC195 is a categorical variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CLASS</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   CLASS  NT_EFC195\n",
+       "0      1          0\n",
+       "1      1          1\n",
+       "2      1          0\n",
+       "3      1          0\n",
+       "4      1          0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           3.404241e-224\n",
+       "FULL_AcidicMolPerc    4.220004e-295\n",
+       "FULL_AURR980107       1.887905e-277\n",
+       "FULL_DAYM780201       6.997934e-245\n",
+       "FULL_GEOR030101        2.669597e-48\n",
+       "FULL_OOBM850104       7.441087e-154\n",
+       "NT_EFC195              2.189203e-48\n",
+       "AS_MeanAmphiMoment     0.000000e+00\n",
+       "AS_DAYM780201         4.911002e-142\n",
+       "AS_FUKS010112          6.540694e-02\n",
+       "CT_RACS820104          5.329249e-51\n",
+       "CLASS                  1.000000e+00\n",
+       "Name: CLASS, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# getting the associated p-values. The value of 1 at the bottom should be ignored \n",
+    "from scipy.stats import pearsonr\n",
+    "data.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f817a0b9048>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# checking the distribution and skewness of variables\n",
+    "plt.figure(figsize=(10,6))\n",
+    "data.skew().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NT_EFC195\n",
+       "0    2769\n",
+       "1     269\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.groupby('NT_EFC195').size() # majority of the instances are of Class 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a3d90f0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a3754e0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a39c940>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a344da0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2f6240>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a31e6a0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2c7b00>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a270f98>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a270fd0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2a2860>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a08ecc0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a041160>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n",
+    "\n",
+    "Tranformation possibilities\n",
+    "* using the minimum and maximum scaler\n",
+    "* standardisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data transformation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# converting Pandas dataframe to ndArray\n",
+    "dataArray = data.to_numpy()\n",
+    "\n",
+    "# seperating the predictor and response variables\n",
+    "target = dataArray[:,11]\n",
+    "predictors = dataArray[:,0:11]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using minMaxScaler to set all values between 0 and 1\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "rescaledPredictors = scaler.fit_transform(predictors)\n",
+    "                        \n",
+    "\n",
+    "# using StandardScaler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler1 = StandardScaler().fit(predictors)\n",
+    "standardizedPredictors = scaler1.transform(predictors)\n",
+    "                        \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            predictor        score         pvalue\n",
+      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
+      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
+      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
+      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
+      "0         FULL_Charge  1214.902838  3.404241e-224\n",
+      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
+      "8       AS_DAYM780201   717.317408  4.911002e-142\n"
+     ]
+    }
+   ],
+   "source": [
+    "# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n",
+    "\n",
+    "# untransformed data\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif\n",
+    "bestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "fit = bestFeatures.fit(predictors, target)\n",
+    "\n",
+    "scores = pd.DataFrame(fit.scores_) \n",
+    "pvalues = pd.DataFrame(fit.pvalues_)\n",
+    "columns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "featureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\n",
+    "featureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n",
+    "\n",
+    "print(featureValues.nlargest(7, 'score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         re_predictor     re_score      re_pvalue\n",
+      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
+      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
+      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
+      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
+      "0         FULL_Charge  1214.902838  3.404241e-224\n",
+      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
+      "8       AS_DAYM780201   717.317408  4.911002e-142\n",
+      "         st_predictor     st_score      st_pvalue\n",
+      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
+      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
+      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
+      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
+      "0         FULL_Charge  1214.902838  3.404241e-224\n",
+      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
+      "8       AS_DAYM780201   717.317408  4.911002e-142\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(None, None)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the transformed data\n",
+    "\n",
+    "# rescaledPredictors\n",
+    "reBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "reFit = reBestFeatures.fit(rescaledPredictors, target)\n",
+    "\n",
+    "reScores = pd.DataFrame(reFit.scores_) \n",
+    "rePvalues = pd.DataFrame(reFit.pvalues_)\n",
+    "reColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "reFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\n",
+    "reFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "# standardizedPredictors\n",
+    "stBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "stFit = stBestFeatures.fit(standardizedPredictors, target)\n",
+    "\n",
+    "stScores = pd.DataFrame(stFit.scores_) \n",
+    "stPvalues = pd.DataFrame(stFit.pvalues_)\n",
+    "stColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "stFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\n",
+    "stFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n",
+    "\n",
+    "print(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.08173854 0.13773548 0.0922014  0.09119859 0.04664165 0.08280402\n",
+      " 0.03303368 0.297985   0.05331243 0.03219657 0.05115262]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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2DUbEq8ACshUiZIH2JuB+4L2StknlVgFfIlupPhER95U1dQvZyvWDwMPAima6+xDwfEQ8mavzBrCYLHBfGhFLgEHAs7l6TSlttbStfCIwNSVVrFNJREyIiNqIqO2zSb+1qWpmZmuhpwTdOrJVH+lnXUQ0Ae8FzgVWAdMlfXQt21XueCwwMQXZ28gCKQAR0QDMAa5qpo1JqWwpaLc0/nzefsBKsu3xIcA3JO1cNp7V3Ze9vwq4LyLub2YOLdUxM7NuoNt/T1fSlsAhwB6SAugDhKRvRcQKsu3cP0p6nmxFPL2N7W4O1ABPSNqTbMV7V7YTzIbAU2TXV0tWpdcaIuL/JL0FHAacRbbizfezPvBJsmu3JZ8hW7m/Bbwg6W9ALdmKdXCu3A7Ac7m2vgNsDZyeK9PUWh0zM+s+esJK93iya5Y7RURNRAwm2xb+sKTtIbuTGdgTeLotDaZrxFcBt6frwHXABan9mojYHhgkaac2jvG/gG9HxMpm8g4FHksr85JngEPSncebAvsDj5HdxDVM0hBJG5KtvqekMX8BOIJslZ8P/lOAk1Jb+wNLI2JxG8dtZmYF6vYrXbKAeGFZ2q3AdWTXd0tf+3mI7Ean1tyTbmpaD5gMfD+ljyW7Czhvckq/qNIAI+LvrWSP5d3bzlcC15JtWQu4NiJmA0g6E5hGtqK/JiLmpjpXk/1S8UBajd8WEd8D7gSOBuYBy4DPlzqRdD/ZHc+bSWoCTomIaZXmY2ZmXUPZzbBmmdra2qivr6/2MMzMehRJMyOitlK5nrC9bGZm1iv0hO3ltSLpQaBvWfKJEdFYjfGYmZmV9LqgGxGjqj0GMzOz5nh72czMrCAOumZmZgVx0DUzMyuIg66ZmVlBHHTNzMwK4qBrZmZWEAddMzOzgvS67+laxzQuWkrN+DuqPYxea+GFH6v2EMysirzSNTMzK8g6GXQlrZTUkHvVSBon6YqycvdKqk3HCyVtVZb/rjqt9LmZpF9Imi9prqT7JI1Kfc/pvNmZmVl3ta5uLy+PiJH5hPS4vK70K7LnAA+LiFWSdgZ2A57vSKOS1o+ItztjgGZm1rXWyZVu0SQNBUYB55ceQB8RT0VE6eJpH0m/TCvgP0naONU7VdIMSbMk3Sppk5R+naQfSboHuEjS1pLukvRwWk0/XVqVS/qcpIfSiv4XkvoUfwbMzAzW3aC7cW5reXIB/e0ONETEyhbyhwFXRsTuwCvAcSn9tojYNyL2Ah4FTsnV2RU4NCK+AXwH+HNEvB+YDOwIIGk34ATggLSyXwl8trxzSadJqpdUv3LZ0o7O1czMWuDt5XdEC2VbSu9MCyKiIR3PBGrS8R6SfgBsAWwGTMvV+W0uiB8IHAsQEVMlvZzSPwrsA8xI2+cbAy+Udx4RE4AJAH0HDitivmZm66R1Neg25yWgf1naAODFTmh7LrCXpPVK28tlVuSOV5IFR4DrgDERMUvSOOCgXLk3csctXZAWcH1EnNueQZuZWedaV7eXmzMDOEDSdgDpruW+wLMdbTgi5gP1wHeVlpyShkkaXaHq5sBiSRvQzLZwzl+BT6d2D+edXx6mA8dL2iblDZC0U/tnYmZmHeGVbhIRz0s6C7hT0nrA60Bd2cp0tqTS+0nAbGCcpDG5MvtHRFMzXXwBuAyYJ2kZ2cr6mxWG9Z/Ag8DTQCNZEG7Od4GbJJ0A/AVYDLwWES9KOh/4U5rTW8AZqT0zMyuYInwJr6eT1BdYGRFvS/oA8PNmrlm3Sd+Bw2LgyZd37gBtNf9FKrPeSdLMiKitVM4r3d5hR2BSWs2+CZza3oZGDOpHvQODmVmXcNDtZJIeJLsWnHdiRDR2VZ8R8SSwd1e1b2ZmncNBt5NFxKhqj8HMzLon371sZmZWEAddMzOzgjjompmZFcRB18zMrCAOumZmZgVx0DUzMyuIg66ZmVlBHHTNzMwK4j+OYWtoXLSUmvF3VHsYvZr//rLZussrXTMzs4IUHnQlrZTUkHvVSBon6YqycvemZ9oiaaGkrcry31WnlT77SbpB0vz0ukFSv1z+7pL+LOkJSU9K+s/cc2/HSfpXGutcSbdI2iTlXSApJO2Sa+vslFYae52kRkmzJU0tzSPVXZQ7D0fn2jhX0jxJj0s6Ipd+jaQXJM0pm98ASXelsd8lqX9Z/r7pvB/flvNlZmZdoxor3eURMTL3WlhAn78GnoqIoRExFFgA/ApA0sbAFODCiNgV2Av4IPDlXP2b01h3J3uKzwm5vEZgbO798cAjqe31gZ8AB0fEnmTP3z0zV/bHufNwZ6ozPLW3O3AkcJWkPqn8dSmt3HhgekQMI3tw/fhSRqp7ETCt4lkyM7Mu1eu3l9MqdB/g+7nk7wG1koYCnwH+FhF/AoiIZWSBcXwzba0PbAq8nEu+HRid8ncGlgL/KlVJr03Tyvk9wHMVhjwamBgRKyJiATAP2C+N7T5gSQt1rk/H1wNjcnlfAW4FXqjQr5mZdbFqBN2Nc1uqkwvobzjQEBErSwnpuIFsNbk7MDNfISLmA5tJek9KOkFSA7AIGAD8Plf8VeBZSXsAdcDNuXbeAr5Ethp+Lo3l17m6Z6Zt52tyW8KDgGdzZZpSWmu2jYjFqc/FwDYAkgYBxwJXt1ZZ0mmS6iXVr1y2tEJXZmbWXtXeXj42pUULZVtKXxtqoZ1Sekv5+f5vjoiRwHZkAfSbZeUmkm0JjwFW/yIhaQOyoLs3sD3Z9vK5KfvnwFBgJLAYuCw3rpbGsbYuB76d/4WjORExISJqI6K2zyb9WitqZmYd0F22l18C+pelDQBe7IS25wJ7S1o913S8F/Boyq/NV0jbxK9HxGv59IgIslXuh8v6+D1wIvBMRLyaSx+Z6s1PdSeRXS8mIp6PiJURsQr4JWkLmWxlOzjXxg5U3pJ+XtLANPaBvLOVXAtMlLSQ7FrzVZLGNN+EmZl1te4SdGcAB0jaDiDd+duXNbdZ2yUi5gH/BM7PJZ8PPJzybgQOlHRo6ntj4KfAxS00eSAwv6yP5cC3gR+WlV0EDJe0dXp/GFmgLwXHkmOB0h3JU4CxkvpKGgIMAx6qMM0pwMnp+GTgd2lcQyKiJiJqgFuAL0fE7RXaMjOzLtIt/jhGRDwv6SzgzrQKfR2oS6vAktmSSu8nkW3Vjitbue0fEU3NdHEK8DNJ88i2bx9IaUTEckmjU/6VQB/gN0D+60gnSDqQ7JeUJmBcM3OY2Ezac5K+C9wn6S3g6VzdiyWNJNs6XgicnurMlTSJ7A7ot4EzStvDkm4CDgK2ktQEfCcifg1cCEySdArwDPCpZs6BmZlVmbJdT7NMbW1t1NfXV3sYZmY9iqSZEVFbqVx32V42MzPr9brF9nJnkfQg2bXgvBMjorEa4zEzM8vrVUE3IkZVewxmZmYt8faymZlZQRx0zczMCuKga2ZmVhAHXTMzs4I46JqZmRXEQdfMzKwgDrpmZmYF6VXf07WOa1y0lJrxd1R7GOu8hRd+rNpDMLMu4JWumZlZQRx0zczMClK1oCtppaSG3KtG0jhJV5SVuzc9XxdJCyVtVZb/rjqt9LlQUmN6PSLpB5L6lpU5W9K/JfVL77eRtKD0rN+UdpWk8ZIOkhTpkXqlvL1T2jnp/c25OS6U1JDSN5B0fRrLo5LOzbVxpKTHJc2TND6XfmNKnyPpGkkbpHRJ+mkqP1vS+3N1pkp6RdIf2nKOzMys61Rzpbs8IkbmXgsL6vfgiBgB7AfsDEwoy68DZpA9WJ6IeAG4CLgUIAW0A4HLUvlG4IRc/bHArNKbiDihNEfgVuC2lPUpoG8ayz7A6ekXjz7AlcBRwHCgTtLwVOdG4H3ACGBj4Asp/Siyh90PA04Dfp4bzyXAiW09OWZm1nXW2e3liHgd+CIwRtIAAElDgc2A88mCb8kEYKikg8kebn9mRLyV8p4BNpK0rSQBRwJ/LO8v5X0auKk0BGBTSeuTBdA3gVfJfhmYFxFPRcSbwERgdBrznZEADwE7pLZGAzekrH8AW0gamOpMB17ryLkyM7POUc2gu3Fu23VyNQYQEa8CC8hWiJAF2puA+4H3StomlVsFfIlspfpERNxX1tQtZCvXDwIPAyua6e5DwPMR8WSuzhvAYrLAfWlELAEGAc/m6jWltNXStvKJwNSUVLFOaySdJqleUv3KZUvbWs3MzNZSNb8ytDxtueZFC2VbSu8Myh2PBY6NiFWSbiMLpFcCRESDpDnAVc20MQm4mWzr9yay4FuuFNBL9gNWAtsD/YH7Jd1dNp6S8vlfBdwXEfc3M4eW6rQoIiaQttn7DhzWlefazGyd1t2+p/sSWQDKGwC82BWdSdocqAGekLQn2Yr3rmwnmA2Bp0hBN1mVXmuIiP+T9BZwGHAWZUE3bSF/kuzabclngKlpm/oFSX8DaslWrINz5XYAnsu19R1ga+D0XJmm1uqYmVn30N2u6c4ADijdKZzuWu7LmlunnULSZmQrxtsj4mWylegFEVGTXtsDgyTt1MYm/wv4dkSsbCbvUOCxiGjKpT0DHJLuPN4U2B94jOwcDJM0RNKGZKvvKWnMXwCOAOrSlnfJFOCk1Nb+wNKIWNzGcZuZWUG61Uo3Ip6XdBZwp6T1gNd5d4CZLan0fhIwGxgnaUyuzP5lAS7vnnRT03rAZOD7KX0s2V3AeZNT+kVtGPvfW8key5pby5CtoK8F5pBtD18bEbMBJJ0JTAP6ANdExNxU52rgaeCBtBq/LSK+B9wJHA3MA5YBny91Iul+sm3vzSQ1AadExLRK8zEzs86n7EZYs0xtbW3U19dXexhmZj2KpJkRUVupXHfbXjYzM+u1utX2cmeR9CDZteC8EyOisRrjMTMzg14adCNiVLXHYGZmVs7by2ZmZgVx0DUzMyuIg66ZmVlBHHTNzMwK4qBrZmZWEAddMzOzgjjompmZFaRXfk/X2q9x0VJqxt9R7WEYsPDCj1V7CGbWybzSNTMzK4iDrpmZWUGqGnQlrZTUkHvVSBon6YqycvemZ+siaaGkrcry31WnQr97SwpJR+TSaiTNKSt3gaRz0vF1khakcc6S9NGy8T2e0mdIGpnLO0HSbElzJV2cS99R0j2S/pnyj87lnStpXmozP8ZrJL3QzDgHSLpL0pPpZ/+U/s3cuZ2TzveAtp4nMzPrXNVe6S6PiJG518KC+q0D/pp+ro1vRsRI4Gtkz7bN+2xE7AVcBVwCIGnLdPzRiNgd2DYXrM8HJkXE3mTP270q1Rme3u8OHAlcJalPqnNdSis3HpgeEcOA6ek9EXFJ6dwC5wJ/iYglazlnMzPrJNUOuoVLD7A/HhgHHC5po3Y08wAwqA15OwNPRMS/0vu7gePScQDvScf9gOfS8WhgYkSsiIgFZA+m3w8gIu4Dmguao4Hr0/H1wJhmytQBNzU3YEmnSaqXVL9y2dIWpmVmZh1V7aC7cW77c3JBfR4ALIiI+cC9wNGtF2/WkcDtbcibB7wvbV2vTxYMB6e8C4DPSWoC7gS+ktIHAc/m2mui5QBfsm1ELAZIP7fJZ0raJI3r1uYqR8SEiKiNiNo+m/Sr0JWZmbVXtb8ytDxtfeZFC2VbSl9bdcDEdDwROBG4rY39XpKuy24D7F9W7kZJmwJ9gPcDRMTLkr4E3AysAv5OtvotjeO6iLhM0geA30jaA1CFMbTHMcDfvLVsZlZd1V7pNucloH9Z2gDgxY42nK6NHgf8l6SFwM+AoyRt3sZ+vwnsQnY99vqysp8FhgD/C1xZSoyI30fEqIj4APA48GTKOgWYlMo8AGwEbEW2sh2ca3cH3tl6bsnzkgamOQ4EXijLH0sLW8tmZlac7hh0ZwAHSNoOIN213Jc1t1zb61BgVkQMjoiaiNiJbMt1TES8Diwu3eiU7vI9kuyGq9UiYhXwE2C9/J3FKe8tsoC8v6TdUjvbpJ/9gS8Dv0rFnwFKfe1GFnT/BUwBxkrqK2kIMAx4qMK8pgAnp+OTgd+VMiT1Az6STzMzs+rodkE3Ip4HzgLulNQAXA7UpWBXMltSU3r9KKWNy6U1SdqhmebrgPJrx7cCn0nHJwHnp37/DHw3XfstH2MAPwC+1UzecuAy4JyU9BNJjwB/Ay6MiCdS+jeAUyXNIluFjovMXLIV8CPAVOCsDshhAAAN8ElEQVSMiFgJIOkmshu13pvmeEpq60LgMElPAoel9yXHAn+KiDeaOR9mZlYgZfHDLFNbWxv19fXVHoaZWY8iaWZE1FYq1+1WumZmZr1Vte9e7jKSHiS7Fpx3YkQ0VmM8ZmZmvTboRsSoao/BzMwsz9vLZmZmBXHQNTMzK4iDrpmZWUEcdM3MzArioGtmZlYQB10zM7OCOOiamZkVpNd+T9fap3HRUmrG31HtYVgvsfDCj1V7CGbdile6ZmZmBSk86EpaKakh96qRNE7SFWXl7k2P9UPSQklbleW/q06FfveWFOWP42uh7BclndRMeo2kOem4VtJPK7SzUNL9ZWkNpTZaqXeQpD+k43GS/pXqPSLp1ErjNzOz7qka28vLI2JkPkFSEf3WkT0btw6Y1lrBiLi6UmMRUQ+05XE8m0saHBHPlp6x2w43R8SZ6dm8cyVNSY9AbJWk9SPi7Xb2aWZmnWyd2F5WFtWPB8YBh0vaKJd3kqTZkmZJ+k1Ku0DSOel4n5T3AHBGrl5+NbqZpGslNaa2jst1Pwk4IR3XkT07t9TGRrl6/5R0cGvziIgXgPnATpI2lXSNpBmp7ujU5jhJv5X0e+BPKe1bqY9Zki5spQszM+tC1VjpbpweEg+wICKOLaDPA1Jf8yXdCxwN3CZpd+A84ICIeFHSgGbqXgt8JSL+IumSFtr/T2BpRIwAkNQ/l3cLcB1wKXAM8FngxJR3BkBEjJD0PuBPknZtaRKSdgZ2Bualcf85Iv5D0hbAQ5LuTkU/AOwZEUskHQWMAUZFxLLm5ijpNOA0gD7v2bql7s3MrIO6xfYyEC2UbSl9bdUBE9PxRLKgdxtwCHBLRLwIEBFL8pUk9QO2iIi/pKTfAEc10/6hwNjVg454OZe3BHhZ0ljgUWBZLu9A4GepzmOSngaaC7onSDoQWAGcnoLp4cAnSityYCNgx3R8V24uhwLXRsSy5uaY0iYAEwD6DhzWWefczMzKdJevDL0E9C9LGwC82NGGJfUBjiMLUOcBAraUtHk6bi3IVMpva7mbgSvJtrfL67XFzRFxZjN1j4uIx9dIlEYBb6zF2MzMrCDd5ZruDOAASdtBdmcw2QPon+2Etg8FZkXE4IioiYidgFvJtlynA5+WtGXqd42t14h4BViaVpmQbQ0350/A6qBYtr0MMBm4mHffwHVfqc20rbwj8DhtMw34SrpejaS9Wxnbf0jaJJVrbgvdzMwK0C2CbroT9yzgznS993KgLiJW5YrNltSUXj9KaeNyaU2Sdmim+TqyoJd3K/CZiJgL/BD4i6RZwI/KKwOfB65MN1Itb2EKPwD6S5qT2lnjhqiIeC0iLoqIN8vqXQX0kdRIthoeFxErWuij3PeBDcjOy5z0/l0iYiowBahP5/ac5sqZmVnXU4R3Hu0dfQcOi4EnX17tYVgv4b9IZesKSTMjorZSue5yTde6iRGD+lHvfyjNzLpErwq6kh4kuxacd2JENFZjPGZmZnm9KuhGxKhqj8HMzKwl3eJGKjMzs3WBg66ZmVlBHHTNzMwK4qBrZmZWEAddMzOzgjjompmZFcRB18zMrCC96nu61nGNi5ZSM/6Oag/DzKxQRf3JUq90zczMCuKga2ZmVpA2BV1Jx0oKSe9L79eT9NP0KLtGSTMkDWml/kJJ95elNaRH0nU6SetLelHS/3Ryu6+3kP5FSSel4+skLZO0eS7/J+n8bdWZ42krSf+vGv2amdma2rrSrQP+CoxN708Atgf2jIgRwLHAKxXa2FzSYABJu7VjrGvjcLKHwX+69JD3rhQRV0fEDbmkecBoyH5BIXu+7qKuHkcrHHTNzLqBikFX0mbAAcApvBN0BwKLSw+Zj4imiHi5QlOTyII1ZEH8plwffSRdklbMsyWdXupb0nRJD6cVdSmQ1Uh6VNIvJc2V9CdJG+f6qgN+AjwD7J/rZ6Gk/5b0gKR6Se+XNE3SfElfTGUOknSfpMmSHpF0dQqcpTZ+KGmWpH9I2jalXSAp/3D4m3JzPQj4G/B2ro2vp12COZK+lpvTY5J+ldJvlHSopL9JelLSfqncppKuSefqn7lzMk7SbZKmpvIXp/QLgY3TzsKNFT4jMzPrQm1Z6Y4BpkbEE8ASSe8nC6DHpH/IL5O0dxvauQX4ZDo+Bvh9Lu8UYGlE7AvsC5yatqv/DRwbEe8nWy1ellu5DgOujIjdyVbZxwGk4PtR4A9kwa+ubBzPRsQHgPuB64DjyQLz93Jl9gO+AYwAhubGvSnwj4jYC7gPOLWFuT4JbC2pf+p/YilD0j7A54FRqd9Tc+dvF7JfFvYE3gd8BjgQOId3VqvnAX9O5+pg4BJJm6a8kWTBfgRwgqTBETEeWB4RIyPis80NVtJp6ZeQ+pXLlrYwJTMz66i2BN180JgI1EVEE/Be4FxgFTBd0kcrtLMEeFnSWOBRYFku73DgJEkNwIPAlmRBVcB/S5oN3A0MArZNdRZEREM6ngnUpOOPA/dExDLgVuBYSX1yfU1JPxuBByPitYj4F/BvSVukvIci4qmIWEkWuA9M6W+SBfPyPptzG9nOwCiyAF9yIDA5It6IiNdTuQ/l5tSYdhDmAtMjItJYS30dDoxP5+peYCNgx5Q3PSKWRsS/gUeAnVoZ32oRMSEiaiOits8m/dpSxczM2qHV7+lK2hI4BNhDUgB9gJD0rYhYAfwR+KOk58lWxNMr9HczcCUwrrwr4CsRMa2s/3HA1sA+EfGWpIVkQQZgRa7oSqC0vVwHHJDKQhbADyYL2vl6q8raWMU75yPKxld6/1YKgqU+Wzt/E4GHgesjYlXu0nJr15jLx5Mfa6kvAcdFxOP5ipJG8e5z4u9hm5l1I5VWuscDN0TEThFRExGDgQXAhyVtD6tvFNoTeLoN/U0GLgamlaVPA74kaYPU5q5py7Qf8EIKuAdTYeUm6T1kK8kd03hrgDN49xZzJftJGpLmdgLZTWRrJSKeIdsKvqos6z5gjKRN0hyPZc2VcCXTgK+UttnbuLX/VuncmplZ9VRaCdUBF5al3Up2LXSJpL4p7SHgikqdRcRrwEUAZTcV/4ps+/ThFEz+RbZyvhH4vaR6oAF4rEIXnyS73plf8f0OuDg31rZ4gGzeI8iC5OS1qLtaRPyimbSHJV1Hds4AfhUR/5RU08Zmvw9cDsxO52oh2ZZ6ayak8g+3dF3XzMy6nt7ZLTXI7l4GzomISoGsV6qtrY36+vpqD8PMrEeRNDMiaiuV81+kMjMzK0in3mgj6UGgfBv3xIho7Mx+ulJE3Et2V7CZmVmn6tSgGxGjOrM9MzOz3sTby2ZmZgVx0DUzMyuI7162NUh6jexhEb3NVsCL1R5EJ+uNc4LeOa/eOCfonfNq75x2ioitKxXyXyyyco+35bb3nkZSfW+bV2+cE/TOefXGOUHvnFdXz8nby2ZmZgVx0DUzMyuIg66Vm1DtAXSR3jiv3jgn6J3z6o1zgt45ry6dk2+kMjMzK4hXumZmZgVx0F2HSDpS0uOS5kka30x+X0k3p/wH808+knRuSn9c0hFFjrs17Z2TpBpJyyU1pNfVRY+9NW2Y14clPSzpbUnHl+WdLOnJ9Dq5uFG3roNzWpn7rKYUN+rK2jCvr0t6RNJsSdMl7ZTL66mfVWtz6smf1RclNaax/1XS8Fxe5/wbGBF+rQMvoA8wH9gZ2BCYBQwvK/Nl4Op0PBa4OR0PT+X7AkNSO316+JxqgDnVnkMH5lVD9hzrG4Djc+kDgKfSz/7puH9PnlPKe73ac+jAvA4GNknHX8r9N9iTP6tm59QLPqv35I4/AUxNx532b6BXuuuO/YB5EfFURLwJTARGl5UZDVyfjm8BPpqe2TsamBgRKyJiATAvtVdtHZlTd1ZxXhGxMCJmA6vK6h4B3BURSyLiZeAu4MgiBl1BR+bUnbVlXvdExLL09h/ADum4J39WLc2pO2vLvF7Nvd0UKN301Gn/BjrorjsGAc/m3jeltGbLRMTbwFJgyzbWrYaOzAlgiKR/SvqLpA919WDXQkfOd0/+rFqzkaR6Sf+QNKZzh9YhazuvU4A/trNuUToyJ+jhn5WkMyTNBy4Gvro2ddvCf5Fq3dHc6q781vWWyrSlbjV0ZE6LgR0j4iVJ+wC3S9q97DfdaunI+e7Jn1VrdoyI5yTtDPxZUmNEzO+ksXVEm+cl6XNALfCRta1bsI7MCXr4ZxURVwJXSvoMcD5wclvrtoVXuuuOJmBw7v0OwHMtlZG0PtAPWNLGutXQ7jmlbaKXACJiJtk1ml27fMRt05Hz3ZM/qxZFxHPp51Nkz7veuzMH1wFtmpekQ4HzgE9ExIq1qVsFHZlTj/+sciYCpZV6531W1b647VcxL7JdjafIbgIo3USwe1mZM1jzpqNJ6Xh31ryJ4Cm6x41UHZnT1qU5kN1YsQgYUO05tXVeubLX8e4bqRaQ3ZjTPx1XfV4dnFN/oG863gp4krIbYLrzvMiCznxgWFl6j/2sWplTT/+shuWOjwHq03Gn/RtY9RPhV3Ev4GjgifQ/y3kp7Xtkv6kCbAT8luwmgYeAnXN1z0v1HgeOqvZcOjon4Dhgbvof6WHgmGrPZS3ntS/Zb99vAC8Bc3N1/yPNdx7w+WrPpaNzAj4INKbPqhE4pdpzWct53Q08DzSk15Re8Fk1O6de8Fn9JP270ADcQy4od9a/gf6LVGZmZgXxNV0zM7OCOOiamZkVxEHXzMysIA66ZmZmBXHQNTMzK4iDrpmZWUEcdM3MzArioGtmZlaQ/w+gxj4MCrLZHAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# using feature importance\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "model = ExtraTreesClassifier()\n",
+    "model.fit(predictors, target)\n",
+    "print(model.feature_importances_)\n",
+    "\n",
+    "# visualising feature importance\n",
+    "importances = pd.Series(model.feature_importances_, index=data.columns[0:11])\n",
+    "importances.nlargest(10).plot(kind='barh')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Building the classification model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
+       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
+       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
+       "                   warm_start=False)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\n",
+    "from sklearn.model_selection import train_test_split # random split\n",
+    "from sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\n",
+    "p_train, p_test, t_train, t_test = train_test_split(predictors, target, \n",
+    "                                                    test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on untransformed data\n",
+    "logit.fit(p_train, t_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Measuring model performance\n",
+    "\n",
+    "We can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict on test data\n",
+    "predictions = logit.predict(p_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 15.0, 'Predicted label')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the confusion matrix\n",
+    "from sklearn import metrics\n",
+    "cm = metrics.confusion_matrix(t_test, predictions)\n",
+    "cm\n",
+    "sb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n",
+    "          square=True, cmap='Blues') \n",
+    "plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.77631578947368\n",
+      "Precision:  92.82608695652173\n",
+      "Recall:  91.04477611940298\n",
+      "MCC:  0.8356480321235562\n"
+     ]
+    }
+   ],
+   "source": [
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n",
+    "                                                      # higher values are better\n",
+    "# not affected by unbalanced classes\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ROC curve of true positive rate against false positive rate\n",
+    "# shows tradeoff between sensitivy and specificity\n",
+    "\n",
+    "pred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\n",
+    "fpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\n",
+    "auc = metrics.roc_auc_score(t_test, pred_probs)\n",
+    "plt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('tpr'), plt.xlabel('fpr')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['False' 'True']\n",
+      "386\n",
+      "372\n"
+     ]
+    }
+   ],
+   "source": [
+    "# performance on new data\n",
+    "new_pred = logit.predict(new.values)\n",
+    "\n",
+    "pred_df = pd.DataFrame(new_pred) \n",
+    "pred_df.columns=[\"CLASS\"]\n",
+    "pred_df.index.name=\"Index\" \n",
+    "pred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df.to_csv(\"ilujumba.csv\") \n",
+    "print(pred_df['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on rescaled data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.66666666666666\n",
+      "Precision:  92.81045751633987\n",
+      "Recall:  90.8315565031983\n",
+      "MCC:  0.8335025900424667\n"
+     ]
+    }
+   ],
+   "source": [
+    "p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit1 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit1.fit(p1_train, t1_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions1 = logit1.predict(p1_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t1_test, predictions1))\n",
+    "\n",
+    "# rescaling new data\n",
+    "newArray = new.to_numpy()\n",
+    "rescaledNew = scaler.fit_transform(newArray)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['False' 'True']\n",
+      "378\n",
+      "380\n"
+     ]
+    }
+   ],
+   "source": [
+    "# performance on new data (rescaled)\n",
+    "new_pred1 = logit1.predict(rescaledNew)\n",
+    "\n",
+    "pred_df1 = pd.DataFrame(new_pred1) \n",
+    "pred_df1.columns=[\"CLASS\"]\n",
+    "pred_df1.index.name=\"Index\" \n",
+    "pred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df1.to_csv(\"ilujumba1.csv\") \n",
+    "print(pred_df1['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df1.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df1.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on standardized data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.8859649122807\n",
+      "Precision:  93.21663019693655\n",
+      "Recall:  90.8315565031983\n",
+      "MCC:  0.8379994044962448\n",
+      "['False' 'True']\n",
+      "388\n",
+      "370\n"
+     ]
+    }
+   ],
+   "source": [
+    "p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit2 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit2.fit(p2_train, t2_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions2 = logit2.predict(p2_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\n",
+    "print('MCC: ',matthews_corrcoef(t2_test, predictions2))\n",
+    "\n",
+    "# standardizing new data\n",
+    "standardizedNew = scaler1.transform(newArray)\n",
+    "\n",
+    "# performance on new data (standaridized)\n",
+    "new_pred2 = logit2.predict(standardizedNew)\n",
+    "\n",
+    "pred_df2 = pd.DataFrame(new_pred2) \n",
+    "pred_df2.columns=[\"CLASS\"]\n",
+    "pred_df2.index.name=\"Index\" \n",
+    "pred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df2.to_csv(\"ilujumba2.csv\") \n",
+    "print(pred_df2['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df2.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df2.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using selected features, rescaled data and Logistic regression\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  90.5701754385965\n",
+      "Precision:  91.36069114470843\n",
+      "Recall:  90.19189765458422\n",
+      "MCC:  0.8113912389992642\n",
+      "['False' 'True']\n",
+      "390\n",
+      "368\n"
+     ]
+    }
+   ],
+   "source": [
+    "p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit3 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit3.fit(p3_train, t3_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions3 = logit3.predict(p3_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t3_test, predictions3))\n",
+    "\n",
+    "# rescaling new data\n",
+    "# newArray = new.to_numpy()\n",
+    "# rescaledNew = scaler.fit_transform(newArray)\n",
+    "\n",
+    "# performance on new data (rescaled)\n",
+    "new_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n",
+    "\n",
+    "pred_df3 = pd.DataFrame(new_pred3) \n",
+    "pred_df3.columns=[\"CLASS\"]\n",
+    "pred_df3.index.name=\"Index\" \n",
+    "pred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df3.to_csv(\"ilujumba3.csv\") \n",
+    "print(pred_df3['CLASS'].unique())\n",
+    "\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df3.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df3.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using cross-validation and Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8387328752276078\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42)\n",
+    "model6 = LogisticRegression()\n",
+    "model6.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model6, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model6_pred = model6.predict(rescaledNew)\n",
+    "df6 = pd.DataFrame(model6_pred)\n",
+    "df6.columns = ['CLASS']\n",
+    "df6.index.name = 'Index'\n",
+    "df6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n",
+    "\n",
+    "df6.to_csv('ilujumba7.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier with kfold cross-validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.887773129281193\n",
+      "['True' 'False']\n",
+      "370\n",
+      "388\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model7 = GaussianNB()\n",
+    "model7.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model7, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model7_pred = model7.predict(newArray)\n",
+    "df7 = pd.DataFrame(model7_pred)\n",
+    "df7.columns = ['CLASS']\n",
+    "df7.index.name = 'Index'\n",
+    "df7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df7.to_csv('ilujumba7.csv')\n",
+    "print(df7['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df7.groupby('CLASS').size()[0].sum())\n",
+    "print(df7.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier on rescaled features\n",
+    "\n",
+    "Assumes that all features are independent of each other and each feature contributes equally to the resulting class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.887773129281193\n",
+      "['True' 'False']\n",
+      "347\n",
+      "411\n"
+     ]
+    }
+   ],
+   "source": [
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model8 = GaussianNB()\n",
+    "model8.fit(rescaledPredictors, target)\n",
+    "\n",
+    "results1 = cross_val_score(model8, rescaledPredictors, target)\n",
+    "print(results1.mean())\n",
+    "\n",
+    "model8_pred = model8.predict(rescaledNew)\n",
+    "df8 = pd.DataFrame(model8_pred)\n",
+    "df8.columns = ['CLASS']\n",
+    "df8.index.name = 'Index'\n",
+    "df8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df8.to_csv('ilujumba8.csv')\n",
+    "print(df8['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df8.groupby('CLASS').size()[0].sum())\n",
+    "print(df8.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes and kfold validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean for results 0.9101496004863645\n",
+      "cross-predicted accuracy  0.6405529953917051\n",
+      "['True' 'False']\n",
+      "370\n",
+      "388\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model9 = GaussianNB()\n",
+    "model9.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\n",
+    "print('mean for results', results.mean())\n",
+    "\n",
+    "predic = cross_val_predict(model9, predictors, target, cv =10)\n",
+    "accuracy = metrics.r2_score(target, predic)\n",
+    "print('cross-predicted accuracy ', accuracy)\n",
+    "\n",
+    "model9_pred = model9.predict(newArray)\n",
+    "df9 = pd.DataFrame(model9_pred)\n",
+    "df9.columns = ['CLASS']\n",
+    "df9.index.name = 'Index'\n",
+    "df9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df9.to_csv('ilujumba9.csv')\n",
+    "print(df9['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df9.groupby('CLASS').size()[0].sum())\n",
+    "print(df9.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing several algorithms to look at the nature of the decision boundaries created"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing classifiers from the sklearn library\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPClassifier #1\n",
+    "from sklearn.neighbors import KNeighborsClassifier #2\n",
+    "from sklearn.svm import SVC #3\n",
+    "from sklearn.gaussian_process import GaussianProcessClassifier #4\n",
+    "from sklearn.gaussian_process.kernels import RBF #5\n",
+    "from sklearn.tree import DecisionTreeClassifier #6\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\n",
+    "from sklearn.naive_bayes import GaussianNB #9\n",
+    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\n",
+    "from sklearn.linear_model import LogisticRegression #11\n",
+    "\n",
+    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
+    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
+    "         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n",
+    "\n",
+    "classifiers = [\n",
+    "    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n",
+    "    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n",
+    "    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n",
+    "    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n",
+    "    DecisionTreeClassifier(),\n",
+    "    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n",
+    "    MLPClassifier(max_iter=1000), #iterations until converge\n",
+    "    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n",
+    "    GaussianNB(),\n",
+    "    QuadraticDiscriminantAnalysis(),\n",
+    "    LogisticRegression()]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dimensionality reduction\n",
+    "https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# preprocessing the dataset\n",
+    "dataArray = data.to_numpy()\n",
+    "X, y = dataArray[:,0:11], dataArray[0:,11]\n",
+    "X = StandardScaler().fit_transform(X)\n",
+    "\n",
+    "# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA()\n",
+    "pca.fit_transform(X)\n",
+    "pca_variance = pca.explained_variance_\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Variance ratio')\n",
+    "plt.xlabel('Principal components')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3038, 8)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\n",
+    "ninety_five = pca2.fit_transform(X)\n",
+    "ninety_five.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Explained variance:  0.9528002773163116\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eight features explain 95% of the variance in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca2 = PCA(3) # keeping features three principal components\n",
+    "principalComponents = pca2.fit_transform(X)\n",
+    "\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "plt.figure(figsize=(10,6))\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n",
+    "           linewidths=1, alpha=.5,\n",
+    "           edgecolor='k', s= 200,\n",
+    "           c=data['CLASS'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3038, 3)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "principalComponents.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\n",
+    "finalDf = pd.concat([principalDf, data['CLASS']], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>PC3</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.423532</td>\n",
+       "      <td>-0.528139</td>\n",
+       "      <td>0.010959</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.428729</td>\n",
+       "      <td>-1.258821</td>\n",
+       "      <td>-1.767912</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.211100</td>\n",
+       "      <td>2.074380</td>\n",
+       "      <td>-1.508877</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.912438</td>\n",
+       "      <td>2.516293</td>\n",
+       "      <td>-0.041182</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.947851</td>\n",
+       "      <td>2.369553</td>\n",
+       "      <td>-0.713456</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        PC1       PC2       PC3  CLASS\n",
+       "0 -0.423532 -0.528139  0.010959      1\n",
+       "1 -1.428729 -1.258821 -1.767912      1\n",
+       "2 -1.211100  2.074380 -1.508877      1\n",
+       "3 -0.912438  2.516293 -0.041182      1\n",
+       "4 -0.947851  2.369553 -0.713456      1"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finalDf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variance explained by three PCs:  65.30291340147794 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualising the top 2 principal components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize = (6,6))\n",
+    "ax = fig.add_subplot(111) \n",
+    "ax.set(xlim=(-10,10), ylim=(-10,10))\n",
+    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
+    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
+    "ax.set_title('top 2 components', fontsize = 20)\n",
+    "\n",
+    "targets = [0, 1]\n",
+    "colors = ['r', 'g']\n",
+    "\n",
+    "for target, color in zip(targets,colors):\n",
+    "    indices = finalDf['CLASS'] == target\n",
+    "    ax.scatter(finalDf.loc[indices, 'PC1']\n",
+    "               , finalDf.loc[indices, 'PC2']\n",
+    "               , c = color\n",
+    "               , s = 50)\n",
+    "ax.legend(targets)\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting the into training and test part\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating mesh for the contour plot\n",
+    "\n",
+    "h = .02  # step size in the mesh\n",
+    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
+    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca4 = PCA(n_components=2)\n",
+    "\n",
+    "# applying PCA on training set\n",
+    "pca4.fit(X_train)\n",
+    "\n",
+    "#applying transform on training and testing sets\n",
+    "train_ = pca4.transform(X_train)\n",
+    "test_ = pca4.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Explained variance:  0.5330557904151474\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((2126, 2), (912, 2))"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_.shape, test_.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-f281cd67b166>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfigure\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mds_cnt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "figure = plt.figure(figsize=(27, 15))\n",
+    "i = 1\n",
+    "\n",
+    "datasets=[data]\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
+    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "        # Plot the top 2 principal components for training data\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n",
+    "                    edgecolors='k')\n",
+    "        # Plot the top 2 principal components for the testing data\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
+    "                    edgecolors='k')\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        i += 1\n",
+    "\n",
+    "        # iterate over classifiers\n",
+    "\n",
+    "    for name, clf in zip(names, classifiers):\n",
+    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "        clf.fit(train_, y_train)\n",
+    "        score = clf.score(test_, y_test)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n",
+    "        # Plot the testing points\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.6)\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(name)\n",
+    "            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n",
+    "            i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 288c1de3a57ca1d487d7f7b9baf94b0eb7fb9d7e Mon Sep 17 00:00:00 2001
From: Atwine <twinmugume@gmail.com>
Date: Thu, 12 Mar 2020 13:37:45 +0300
Subject: [PATCH 05/10] Make changes

---
 ...Ibra Lujumba_kaggle_final-checkpoint.ipynb | 2079 +++++++++++++++++
 .../Ibra Lujumba_kaggle_final.ipynb           |   33 +-
 2 files changed, 2110 insertions(+), 2 deletions(-)
 create mode 100644 Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle_final-checkpoint.ipynb

diff --git a/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle_final-checkpoint.ipynb b/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle_final-checkpoint.ipynb
new file mode 100644
index 0000000..6d19baa
--- /dev/null
+++ b/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle_final-checkpoint.ipynb	
@@ -0,0 +1,2079 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ACE_Uganda Kaggle competition 1\n",
+    "### by Ibra Lujumba"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Exploratory Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Importing python modules for data analysis and visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # manipulation of arrays\n",
+    "import pandas as pd # manipulating dataframes\n",
+    "import matplotlib.pyplot as plt # data visualisation\n",
+    "import seaborn as sb # data visualisation,it is based on plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ignoring warnings that may arise\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###### Importing the datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AMP_TrainSet.csv  Test.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls ../input/ace-class-assignment/\n",
+    "\n",
+    "# reading in the data\n",
+    "data = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\n",
+    "new = pd.read_csv('../input/ace-class-assignment/Test.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Checking the dimensions of the data as well as the datatype of each column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((3038, 12), (758, 11))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking dimensions of the datasets\n",
+    "data.shape, new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(FULL_Charge           float64\n",
+       " FULL_AcidicMolPerc    float64\n",
+       " FULL_AURR980107       float64\n",
+       " FULL_DAYM780201       float64\n",
+       " FULL_GEOR030101       float64\n",
+       " FULL_OOBM850104       float64\n",
+       " NT_EFC195               int64\n",
+       " AS_MeanAmphiMoment    float64\n",
+       " AS_DAYM780201         float64\n",
+       " AS_FUKS010112         float64\n",
+       " CT_RACS820104         float64\n",
+       " CLASS                   int64\n",
+       " dtype: object,\n",
+       " FULL_Charge           float64\n",
+       " FULL_AcidicMolPerc    float64\n",
+       " FULL_AURR980107       float64\n",
+       " FULL_DAYM780201       float64\n",
+       " FULL_GEOR030101       float64\n",
+       " FULL_OOBM850104       float64\n",
+       " NT_EFC195               int64\n",
+       " AS_MeanAmphiMoment    float64\n",
+       " AS_DAYM780201         float64\n",
+       " AS_FUKS010112         float64\n",
+       " CT_RACS820104         float64\n",
+       " dtype: object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the datatypes of the variables\n",
+    "data.dtypes, new.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the values in all the variables exists as either floats or integers.\n",
+    "\n",
+    "Proceeding to work with the training dataset to build the classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.060237</td>\n",
+       "      <td>8.521520</td>\n",
+       "      <td>0.971410</td>\n",
+       "      <td>73.668760</td>\n",
+       "      <td>0.994007</td>\n",
+       "      <td>-2.432927</td>\n",
+       "      <td>0.088545</td>\n",
+       "      <td>15.683233</td>\n",
+       "      <td>73.650828</td>\n",
+       "      <td>5.911361</td>\n",
+       "      <td>1.235255</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.819929</td>\n",
+       "      <td>7.586652</td>\n",
+       "      <td>0.107413</td>\n",
+       "      <td>8.527489</td>\n",
+       "      <td>0.031333</td>\n",
+       "      <td>1.707223</td>\n",
+       "      <td>0.284133</td>\n",
+       "      <td>11.575665</td>\n",
+       "      <td>9.166092</td>\n",
+       "      <td>0.693689</td>\n",
+       "      <td>0.210012</td>\n",
+       "      <td>0.500082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-16.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.684000</td>\n",
+       "      <td>42.750000</td>\n",
+       "      <td>0.866000</td>\n",
+       "      <td>-10.432000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.041000</td>\n",
+       "      <td>42.778000</td>\n",
+       "      <td>3.533000</td>\n",
+       "      <td>0.785000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.516000</td>\n",
+       "      <td>0.895000</td>\n",
+       "      <td>68.294000</td>\n",
+       "      <td>0.974000</td>\n",
+       "      <td>-3.606000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.587500</td>\n",
+       "      <td>67.556000</td>\n",
+       "      <td>5.459250</td>\n",
+       "      <td>1.082000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.143000</td>\n",
+       "      <td>0.963000</td>\n",
+       "      <td>74.059500</td>\n",
+       "      <td>0.994000</td>\n",
+       "      <td>-2.296500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.988500</td>\n",
+       "      <td>73.697000</td>\n",
+       "      <td>5.925500</td>\n",
+       "      <td>1.184000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>13.158000</td>\n",
+       "      <td>1.041000</td>\n",
+       "      <td>79.343750</td>\n",
+       "      <td>1.011000</td>\n",
+       "      <td>-1.283250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>26.807750</td>\n",
+       "      <td>79.778000</td>\n",
+       "      <td>6.382000</td>\n",
+       "      <td>1.351000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>46.667000</td>\n",
+       "      <td>1.451000</td>\n",
+       "      <td>101.682000</td>\n",
+       "      <td>1.196000</td>\n",
+       "      <td>3.576000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>51.280000</td>\n",
+       "      <td>103.167000</td>\n",
+       "      <td>8.662000</td>\n",
+       "      <td>2.192000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "count  3038.000000         3038.000000      3038.000000      3038.000000   \n",
+       "mean      2.060237            8.521520         0.971410        73.668760   \n",
+       "std       3.819929            7.586652         0.107413         8.527489   \n",
+       "min     -16.000000            0.000000         0.684000        42.750000   \n",
+       "25%       0.000000            2.516000         0.895000        68.294000   \n",
+       "50%       2.000000            7.143000         0.963000        74.059500   \n",
+       "75%       4.000000           13.158000         1.041000        79.343750   \n",
+       "max      30.000000           46.667000         1.451000       101.682000   \n",
+       "\n",
+       "       FULL_GEOR030101  FULL_OOBM850104    NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "count      3038.000000      3038.000000  3038.000000         3038.000000   \n",
+       "mean          0.994007        -2.432927     0.088545           15.683233   \n",
+       "std           0.031333         1.707223     0.284133           11.575665   \n",
+       "min           0.866000       -10.432000     0.000000            0.041000   \n",
+       "25%           0.974000        -3.606000     0.000000            5.587500   \n",
+       "50%           0.994000        -2.296500     0.000000           14.988500   \n",
+       "75%           1.011000        -1.283250     0.000000           26.807750   \n",
+       "max           1.196000         3.576000     1.000000           51.280000   \n",
+       "\n",
+       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104        CLASS  \n",
+       "count    3038.000000    3038.000000    3038.000000  3038.000000  \n",
+       "mean       73.650828       5.911361       1.235255     0.500000  \n",
+       "std         9.166092       0.693689       0.210012     0.500082  \n",
+       "min        42.778000       3.533000       0.785000     0.000000  \n",
+       "25%        67.556000       5.459250       1.082000     0.000000  \n",
+       "50%        73.697000       5.925500       1.184000     0.500000  \n",
+       "75%        79.778000       6.382000       1.351000     1.000000  \n",
+       "max       103.167000       8.662000       2.192000     1.000000  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# getting the descriptive statistics of the train dataset such as arithmetic mean, \n",
+    "# standard deviation, quartiles and number of non-NA values in each column \n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\n",
+    "AS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\n",
+    "For all the variables, the data points are not widely spread"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CLASS\n",
+       "0    1519\n",
+       "1    1519\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the proprotions of classses\n",
+    "data.groupby('CLASS').size()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Both classes have an equal number of entries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f817a5bfd68>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# obtaining pairwise correlation values for the variables in the train dataset\n",
+    "# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\n",
+    "pearsoncorr = data.corr(method='pearson')\n",
+    "\n",
+    "# visualizing the correlation matrix as a heatmap to make interpretation easier\n",
+    "plt.figure(figsize=(10,10))\n",
+    "top_corr = pearsoncorr.index\n",
+    "sb.heatmap(pearsoncorr, \n",
+    "            xticklabels=pearsoncorr.columns,\n",
+    "            yticklabels=pearsoncorr.columns,\n",
+    "            cmap='RdYlGn',\n",
+    "            annot=True,\n",
+    "            linewidth=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the p-values associated with the correlation values using the code below.\n",
+    "\n",
+    "`from scipy.stats import pearsonr`\n",
+    "\n",
+    "`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n",
+    "\n",
+    "In this example, all values were too low to be informative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using a scatter plot matrix to visualise correlations\n",
+    "# plt.figure(figsize=(60,60))\n",
+    "# sb.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\n",
+    "These variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n",
+    "\n",
+    "Variables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2830, array([0, 1]))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n",
+    "\n",
+    "#this confirms that NT_EFC195 is a categorical variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CLASS</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   CLASS  NT_EFC195\n",
+       "0      1          0\n",
+       "1      1          1\n",
+       "2      1          0\n",
+       "3      1          0\n",
+       "4      1          0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           3.404241e-224\n",
+       "FULL_AcidicMolPerc    4.220004e-295\n",
+       "FULL_AURR980107       1.887905e-277\n",
+       "FULL_DAYM780201       6.997934e-245\n",
+       "FULL_GEOR030101        2.669597e-48\n",
+       "FULL_OOBM850104       7.441087e-154\n",
+       "NT_EFC195              2.189203e-48\n",
+       "AS_MeanAmphiMoment     0.000000e+00\n",
+       "AS_DAYM780201         4.911002e-142\n",
+       "AS_FUKS010112          6.540694e-02\n",
+       "CT_RACS820104          5.329249e-51\n",
+       "CLASS                  1.000000e+00\n",
+       "Name: CLASS, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# getting the associated p-values. The value of 1 at the bottom should be ignored \n",
+    "from scipy.stats import pearsonr\n",
+    "data.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f817a0b9048>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# checking the distribution and skewness of variables\n",
+    "plt.figure(figsize=(10,6))\n",
+    "data.skew().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NT_EFC195\n",
+       "0    2769\n",
+       "1     269\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.groupby('NT_EFC195').size() # majority of the instances are of Class 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a3d90f0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a3754e0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a39c940>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a344da0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2f6240>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a31e6a0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2c7b00>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a270f98>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a270fd0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2a2860>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a08ecc0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a041160>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n",
+    "\n",
+    "Tranformation possibilities\n",
+    "* using the minimum and maximum scaler\n",
+    "* standardisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data transformation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# converting Pandas dataframe to ndArray\n",
+    "dataArray = data.to_numpy()\n",
+    "\n",
+    "# seperating the predictor and response variables\n",
+    "target = dataArray[:,11]\n",
+    "predictors = dataArray[:,0:11]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using minMaxScaler to set all values between 0 and 1\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "rescaledPredictors = scaler.fit_transform(predictors)\n",
+    "                        \n",
+    "\n",
+    "# using StandardScaler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler1 = StandardScaler().fit(predictors)\n",
+    "standardizedPredictors = scaler1.transform(predictors)\n",
+    "                        \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            predictor        score         pvalue\n",
+      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
+      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
+      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
+      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
+      "0         FULL_Charge  1214.902838  3.404241e-224\n",
+      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
+      "8       AS_DAYM780201   717.317408  4.911002e-142\n"
+     ]
+    }
+   ],
+   "source": [
+    "# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n",
+    "\n",
+    "# untransformed data\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif\n",
+    "bestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "fit = bestFeatures.fit(predictors, target)\n",
+    "\n",
+    "scores = pd.DataFrame(fit.scores_) \n",
+    "pvalues = pd.DataFrame(fit.pvalues_)\n",
+    "columns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "featureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\n",
+    "featureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n",
+    "\n",
+    "print(featureValues.nlargest(7, 'score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         re_predictor     re_score      re_pvalue\n",
+      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
+      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
+      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
+      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
+      "0         FULL_Charge  1214.902838  3.404241e-224\n",
+      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
+      "8       AS_DAYM780201   717.317408  4.911002e-142\n",
+      "         st_predictor     st_score      st_pvalue\n",
+      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
+      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
+      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
+      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
+      "0         FULL_Charge  1214.902838  3.404241e-224\n",
+      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
+      "8       AS_DAYM780201   717.317408  4.911002e-142\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(None, None)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the transformed data\n",
+    "\n",
+    "# rescaledPredictors\n",
+    "reBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "reFit = reBestFeatures.fit(rescaledPredictors, target)\n",
+    "\n",
+    "reScores = pd.DataFrame(reFit.scores_) \n",
+    "rePvalues = pd.DataFrame(reFit.pvalues_)\n",
+    "reColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "reFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\n",
+    "reFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "# standardizedPredictors\n",
+    "stBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "stFit = stBestFeatures.fit(standardizedPredictors, target)\n",
+    "\n",
+    "stScores = pd.DataFrame(stFit.scores_) \n",
+    "stPvalues = pd.DataFrame(stFit.pvalues_)\n",
+    "stColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "stFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\n",
+    "stFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n",
+    "\n",
+    "print(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.08173854 0.13773548 0.0922014  0.09119859 0.04664165 0.08280402\n",
+      " 0.03303368 0.297985   0.05331243 0.03219657 0.05115262]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# using feature importance\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "model = ExtraTreesClassifier()\n",
+    "model.fit(predictors, target)\n",
+    "print(model.feature_importances_)\n",
+    "\n",
+    "# visualising feature importance\n",
+    "importances = pd.Series(model.feature_importances_, index=data.columns[0:11])\n",
+    "importances.nlargest(10).plot(kind='barh')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Building the classification model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
+       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
+       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
+       "                   warm_start=False)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\n",
+    "from sklearn.model_selection import train_test_split # random split\n",
+    "from sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\n",
+    "p_train, p_test, t_train, t_test = train_test_split(predictors, target, \n",
+    "                                                    test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on untransformed data\n",
+    "logit.fit(p_train, t_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Measuring model performance\n",
+    "\n",
+    "We can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict on test data\n",
+    "predictions = logit.predict(p_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 15.0, 'Predicted label')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the confusion matrix\n",
+    "from sklearn import metrics\n",
+    "cm = metrics.confusion_matrix(t_test, predictions)\n",
+    "cm\n",
+    "sb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n",
+    "          square=True, cmap='Blues') \n",
+    "plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.77631578947368\n",
+      "Precision:  92.82608695652173\n",
+      "Recall:  91.04477611940298\n",
+      "MCC:  0.8356480321235562\n"
+     ]
+    }
+   ],
+   "source": [
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n",
+    "                                                      # higher values are better\n",
+    "# not affected by unbalanced classes\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ROC curve of true positive rate against false positive rate\n",
+    "# shows tradeoff between sensitivy and specificity\n",
+    "\n",
+    "pred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\n",
+    "fpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\n",
+    "auc = metrics.roc_auc_score(t_test, pred_probs)\n",
+    "plt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('tpr'), plt.xlabel('fpr')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['False' 'True']\n",
+      "386\n",
+      "372\n"
+     ]
+    }
+   ],
+   "source": [
+    "# performance on new data\n",
+    "new_pred = logit.predict(new.values)\n",
+    "\n",
+    "pred_df = pd.DataFrame(new_pred) \n",
+    "pred_df.columns=[\"CLASS\"]\n",
+    "pred_df.index.name=\"Index\" \n",
+    "pred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df.to_csv(\"ilujumba.csv\") \n",
+    "print(pred_df['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on rescaled data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.66666666666666\n",
+      "Precision:  92.81045751633987\n",
+      "Recall:  90.8315565031983\n",
+      "MCC:  0.8335025900424667\n"
+     ]
+    }
+   ],
+   "source": [
+    "p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit1 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit1.fit(p1_train, t1_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions1 = logit1.predict(p1_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t1_test, predictions1))\n",
+    "\n",
+    "# rescaling new data\n",
+    "newArray = new.to_numpy()\n",
+    "rescaledNew = scaler.fit_transform(newArray)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['False' 'True']\n",
+      "378\n",
+      "380\n"
+     ]
+    }
+   ],
+   "source": [
+    "# performance on new data (rescaled)\n",
+    "new_pred1 = logit1.predict(rescaledNew)\n",
+    "\n",
+    "pred_df1 = pd.DataFrame(new_pred1) \n",
+    "pred_df1.columns=[\"CLASS\"]\n",
+    "pred_df1.index.name=\"Index\" \n",
+    "pred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df1.to_csv(\"ilujumba1.csv\") \n",
+    "print(pred_df1['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df1.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df1.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on standardized data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.8859649122807\n",
+      "Precision:  93.21663019693655\n",
+      "Recall:  90.8315565031983\n",
+      "MCC:  0.8379994044962448\n",
+      "['False' 'True']\n",
+      "388\n",
+      "370\n"
+     ]
+    }
+   ],
+   "source": [
+    "p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit2 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit2.fit(p2_train, t2_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions2 = logit2.predict(p2_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\n",
+    "print('MCC: ',matthews_corrcoef(t2_test, predictions2))\n",
+    "\n",
+    "# standardizing new data\n",
+    "standardizedNew = scaler1.transform(newArray)\n",
+    "\n",
+    "# performance on new data (standaridized)\n",
+    "new_pred2 = logit2.predict(standardizedNew)\n",
+    "\n",
+    "pred_df2 = pd.DataFrame(new_pred2) \n",
+    "pred_df2.columns=[\"CLASS\"]\n",
+    "pred_df2.index.name=\"Index\" \n",
+    "pred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df2.to_csv(\"ilujumba2.csv\") \n",
+    "print(pred_df2['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df2.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df2.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using selected features, rescaled data and Logistic regression\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  90.5701754385965\n",
+      "Precision:  91.36069114470843\n",
+      "Recall:  90.19189765458422\n",
+      "MCC:  0.8113912389992642\n",
+      "['False' 'True']\n",
+      "390\n",
+      "368\n"
+     ]
+    }
+   ],
+   "source": [
+    "p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit3 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit3.fit(p3_train, t3_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions3 = logit3.predict(p3_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t3_test, predictions3))\n",
+    "\n",
+    "# rescaling new data\n",
+    "# newArray = new.to_numpy()\n",
+    "# rescaledNew = scaler.fit_transform(newArray)\n",
+    "\n",
+    "# performance on new data (rescaled)\n",
+    "new_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n",
+    "\n",
+    "pred_df3 = pd.DataFrame(new_pred3) \n",
+    "pred_df3.columns=[\"CLASS\"]\n",
+    "pred_df3.index.name=\"Index\" \n",
+    "pred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df3.to_csv(\"ilujumba3.csv\") \n",
+    "print(pred_df3['CLASS'].unique())\n",
+    "\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df3.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df3.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using cross-validation and Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8387328752276078\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42)\n",
+    "model6 = LogisticRegression()\n",
+    "model6.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model6, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model6_pred = model6.predict(rescaledNew)\n",
+    "df6 = pd.DataFrame(model6_pred)\n",
+    "df6.columns = ['CLASS']\n",
+    "df6.index.name = 'Index'\n",
+    "df6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n",
+    "\n",
+    "df6.to_csv('ilujumba7.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier with kfold cross-validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.887773129281193\n",
+      "['True' 'False']\n",
+      "370\n",
+      "388\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model7 = GaussianNB()\n",
+    "model7.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model7, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model7_pred = model7.predict(newArray)\n",
+    "df7 = pd.DataFrame(model7_pred)\n",
+    "df7.columns = ['CLASS']\n",
+    "df7.index.name = 'Index'\n",
+    "df7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df7.to_csv('ilujumba7.csv')\n",
+    "print(df7['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df7.groupby('CLASS').size()[0].sum())\n",
+    "print(df7.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier on rescaled features\n",
+    "\n",
+    "Assumes that all features are independent of each other and each feature contributes equally to the resulting class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.887773129281193\n",
+      "['True' 'False']\n",
+      "347\n",
+      "411\n"
+     ]
+    }
+   ],
+   "source": [
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model8 = GaussianNB()\n",
+    "model8.fit(rescaledPredictors, target)\n",
+    "\n",
+    "results1 = cross_val_score(model8, rescaledPredictors, target)\n",
+    "print(results1.mean())\n",
+    "\n",
+    "model8_pred = model8.predict(rescaledNew)\n",
+    "df8 = pd.DataFrame(model8_pred)\n",
+    "df8.columns = ['CLASS']\n",
+    "df8.index.name = 'Index'\n",
+    "df8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df8.to_csv('ilujumba8.csv')\n",
+    "print(df8['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df8.groupby('CLASS').size()[0].sum())\n",
+    "print(df8.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes and kfold validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean for results 0.9101496004863645\n",
+      "cross-predicted accuracy  0.6405529953917051\n",
+      "['True' 'False']\n",
+      "370\n",
+      "388\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model9 = GaussianNB()\n",
+    "model9.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\n",
+    "print('mean for results', results.mean())\n",
+    "\n",
+    "predic = cross_val_predict(model9, predictors, target, cv =10)\n",
+    "accuracy = metrics.r2_score(target, predic)\n",
+    "print('cross-predicted accuracy ', accuracy)\n",
+    "\n",
+    "model9_pred = model9.predict(newArray)\n",
+    "df9 = pd.DataFrame(model9_pred)\n",
+    "df9.columns = ['CLASS']\n",
+    "df9.index.name = 'Index'\n",
+    "df9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df9.to_csv('ilujumba9.csv')\n",
+    "print(df9['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df9.groupby('CLASS').size()[0].sum())\n",
+    "print(df9.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing several algorithms to look at the nature of the decision boundaries created"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing classifiers from the sklearn library\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPClassifier #1\n",
+    "from sklearn.neighbors import KNeighborsClassifier #2\n",
+    "from sklearn.svm import SVC #3\n",
+    "from sklearn.gaussian_process import GaussianProcessClassifier #4\n",
+    "from sklearn.gaussian_process.kernels import RBF #5\n",
+    "from sklearn.tree import DecisionTreeClassifier #6\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\n",
+    "from sklearn.naive_bayes import GaussianNB #9\n",
+    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\n",
+    "from sklearn.linear_model import LogisticRegression #11\n",
+    "\n",
+    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
+    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
+    "         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n",
+    "\n",
+    "classifiers = [\n",
+    "    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n",
+    "    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n",
+    "    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n",
+    "    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n",
+    "    DecisionTreeClassifier(),\n",
+    "    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n",
+    "    MLPClassifier(max_iter=1000), #iterations until converge\n",
+    "    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n",
+    "    GaussianNB(),\n",
+    "    QuadraticDiscriminantAnalysis(),\n",
+    "    LogisticRegression()]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dimensionality reduction\n",
+    "https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# preprocessing the dataset\n",
+    "dataArray = data.to_numpy()\n",
+    "X, y = dataArray[:,0:11], dataArray[0:,11]\n",
+    "X = StandardScaler().fit_transform(X)\n",
+    "\n",
+    "# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA()\n",
+    "pca.fit_transform(X)\n",
+    "pca_variance = pca.explained_variance_\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Variance ratio')\n",
+    "plt.xlabel('Principal components')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3038, 8)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\n",
+    "ninety_five = pca2.fit_transform(X)\n",
+    "ninety_five.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Explained variance:  0.9528002773163116\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eight features explain 95% of the variance in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca2 = PCA(3) # keeping features three principal components\n",
+    "principalComponents = pca2.fit_transform(X)\n",
+    "\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "plt.figure(figsize=(10,6))\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n",
+    "           linewidths=1, alpha=.5,\n",
+    "           edgecolor='k', s= 200,\n",
+    "           c=data['CLASS'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3038, 3)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "principalComponents.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\n",
+    "finalDf = pd.concat([principalDf, data['CLASS']], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>PC3</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.423532</td>\n",
+       "      <td>-0.528139</td>\n",
+       "      <td>0.010959</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.428729</td>\n",
+       "      <td>-1.258821</td>\n",
+       "      <td>-1.767912</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.211100</td>\n",
+       "      <td>2.074380</td>\n",
+       "      <td>-1.508877</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.912438</td>\n",
+       "      <td>2.516293</td>\n",
+       "      <td>-0.041182</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.947851</td>\n",
+       "      <td>2.369553</td>\n",
+       "      <td>-0.713456</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        PC1       PC2       PC3  CLASS\n",
+       "0 -0.423532 -0.528139  0.010959      1\n",
+       "1 -1.428729 -1.258821 -1.767912      1\n",
+       "2 -1.211100  2.074380 -1.508877      1\n",
+       "3 -0.912438  2.516293 -0.041182      1\n",
+       "4 -0.947851  2.369553 -0.713456      1"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finalDf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variance explained by three PCs:  65.30291340147794 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualising the top 2 principal components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize = (6,6))\n",
+    "ax = fig.add_subplot(111) \n",
+    "ax.set(xlim=(-10,10), ylim=(-10,10))\n",
+    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
+    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
+    "ax.set_title('top 2 components', fontsize = 20)\n",
+    "\n",
+    "targets = [0, 1]\n",
+    "colors = ['r', 'g']\n",
+    "\n",
+    "for target, color in zip(targets,colors):\n",
+    "    indices = finalDf['CLASS'] == target\n",
+    "    ax.scatter(finalDf.loc[indices, 'PC1']\n",
+    "               , finalDf.loc[indices, 'PC2']\n",
+    "               , c = color\n",
+    "               , s = 50)\n",
+    "ax.legend(targets)\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting the into training and test part\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating mesh for the contour plot\n",
+    "\n",
+    "h = .02  # step size in the mesh\n",
+    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
+    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca4 = PCA(n_components=2)\n",
+    "\n",
+    "# applying PCA on training set\n",
+    "pca4.fit(X_train)\n",
+    "\n",
+    "#applying transform on training and testing sets\n",
+    "train_ = pca4.transform(X_train)\n",
+    "test_ = pca4.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Explained variance:  0.5330557904151474\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((2126, 2), (912, 2))"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_.shape, test_.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-f281cd67b166>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfigure\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mds_cnt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "figure = plt.figure(figsize=(27, 15))\n",
+    "i = 1\n",
+    "\n",
+    "datasets=[data]\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
+    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "        # Plot the top 2 principal components for training data\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n",
+    "                    edgecolors='k')\n",
+    "        # Plot the top 2 principal components for the testing data\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
+    "                    edgecolors='k')\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        i += 1\n",
+    "\n",
+    "        # iterate over classifiers\n",
+    "\n",
+    "    for name, clf in zip(names, classifiers):\n",
+    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "        clf.fit(train_, y_train)\n",
+    "        score = clf.score(test_, y_test)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n",
+    "        # Plot the testing points\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.6)\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(name)\n",
+    "            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n",
+    "            i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Assignment Colab/Ibra Lujumba_kaggle_final.ipynb b/Assignment Colab/Ibra Lujumba_kaggle_final.ipynb
index 70f0df8..593af72 100644
--- a/Assignment Colab/Ibra Lujumba_kaggle_final.ipynb	
+++ b/Assignment Colab/Ibra Lujumba_kaggle_final.ipynb	
@@ -720,7 +720,7 @@
     }
    ],
    "source": [
-    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
+    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))\n"
    ]
   },
   {
@@ -2042,7 +2042,36 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,

From d422d49a0853adc50ca6f1e17e74cbb3b9a7c2df Mon Sep 17 00:00:00 2001
From: Ibra Lujumba <ibra.lujumba@gmail.com>
Date: Fri, 13 Mar 2020 14:37:44 +0300
Subject: [PATCH 06/10] Submission

Made suggested corrections
Added more comments
Made code more readable and understandable
---
 Assignment Colab/Ibra Lujumba_kaggle.ipynb    |    1 +
 .../Ibra Lujumba_kaggle_final.ipynb           | 2050 -----------------
 2 files changed, 1 insertion(+), 2050 deletions(-)
 create mode 100644 Assignment Colab/Ibra Lujumba_kaggle.ipynb
 delete mode 100644 Assignment Colab/Ibra Lujumba_kaggle_final.ipynb

diff --git a/Assignment Colab/Ibra Lujumba_kaggle.ipynb b/Assignment Colab/Ibra Lujumba_kaggle.ipynb
new file mode 100644
index 0000000..600d184
--- /dev/null
+++ b/Assignment Colab/Ibra Lujumba_kaggle.ipynb	
@@ -0,0 +1 @@
+{"cells":[{"metadata":{},"cell_type":"markdown","source":"## ACE_Uganda Kaggle competition 1\n### by Ibra Lujumba"},{"metadata":{},"cell_type":"markdown","source":"#### Exploratory Data Analysis "},{"metadata":{},"cell_type":"markdown","source":"This is done to try to understand the properties of the data before any machine learning algorithm id used to make predictions about the data."},{"metadata":{},"cell_type":"markdown","source":"##### Importing python modules for data analysis and visualization"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np # manipulation of arrays\nimport pandas as pd # manipulating dataframes\nimport matplotlib.pyplot as plt # data visualisation\nimport seaborn as sb # data visualisation,it is based on plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#ignoring warnings that may arise\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"###### Importing the datasets"},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input/ace-class-assignment/\n\n# reading in the data\ndata = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\nnew = pd.read_csv('../input/ace-class-assignment/Test.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"##### Checking the dimensions of the data as well as the datatype of each column"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking dimensions of the datasets\ndata.shape, new.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the datatypes of the variables\ndata.dtypes, new.dtypes","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"All the values in all the variables exists as either floats or integers.\n\nProceeding to work with the training dataset to build the classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the descriptive statistics of the train dataset such as arithmetic mean, \n# standard deviation, quartiles and number of non-NA values in each column \ndata.describe()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"raw","source":"From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\nAS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\nFor all the variables, the data points are not widely spread"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the proprotions of classses\ndata.groupby('CLASS').size()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"raw","source":"Both classes have an equal number of entries"},{"metadata":{"trusted":true},"cell_type":"code","source":"# obtaining pairwise correlation values for the variables in the train dataset\n# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\npearsoncorr = data.corr(method='pearson')\n\n# visualizing the correlation matrix as a heatmap to make interpretation easier\nplt.figure(figsize=(10,10))\ntop_corr = pearsoncorr.index\nsb.heatmap(pearsoncorr, \n            xticklabels=pearsoncorr.columns,\n            yticklabels=pearsoncorr.columns,\n            cmap='RdYlGn',\n            annot=True,\n            linewidth=0.5)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."},{"metadata":{},"cell_type":"markdown","source":"You can get the p-values associated with the correlation values using the code below.\n\n`from scipy.stats import pearsonr`\n\n`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n\nIn this example, all values were too low to be informative"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using a scatter plot matrix to visualise correlations\n# plt.figure(figsize=(60,60))\n# sb.pairplot(data)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\nThese variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n\nVariables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"},{"metadata":{"trusted":true},"cell_type":"code","source":"len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n\n#this confirms that NT_EFC195 is a categorical variable","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the associated p-values. The value of 1 at the bottom should be ignored \nfrom scipy.stats import pearsonr\ndata.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the distribution and skewness of variables\nplt.figure(figsize=(10,6))\ndata.skew().plot(kind='bar')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Most of the variables are minimally skewed except NT_EFC195. Further checks will be done to try to understand the properties of this variable.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"data.groupby('NT_EFC195').size() # majority of the instances are of Class 0.","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The skewedness in this variable can be understood by having most of its values at zeros\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n\nTranformation possibilities\n* using the minimum and maximum scaler\n* standardisation"},{"metadata":{},"cell_type":"markdown","source":"#### Data transformation"},{"metadata":{},"cell_type":"markdown","source":"Better performance of algorithms can be obtained if the data is transformed.\nSome algorithms are may take features with large values as the most important features in the predictions"},{"metadata":{},"cell_type":"markdown","source":"Seperating the predictor variables from the target variable"},{"metadata":{"trusted":true},"cell_type":"code","source":"# converting Pandas dataframe to ndArray\ndataArray = data.to_numpy()\n\n# seperating the predictor and response variables\ntarget = dataArray[:,11]\npredictors = dataArray[:,0:11]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using minMaxScaler to set all values between 0 and 1\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler(feature_range=(0,1))\nrescaledPredictors = scaler.fit_transform(predictors)\n                        \n\n# using StandardScaler\nfrom sklearn.preprocessing import StandardScaler\nscaler1 = StandardScaler().fit(predictors)\nstandardizedPredictors = scaler1.transform(predictors)\n                        \n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Feature selection"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n\n# untransformed data\nfrom sklearn.feature_selection import SelectKBest, f_classif\nbestFeatures = SelectKBest(score_func=f_classif, k=7)\nfit = bestFeatures.fit(predictors, target)\n\nscores = pd.DataFrame(fit.scores_) \npvalues = pd.DataFrame(fit.pvalues_)\ncolumns = pd.DataFrame(data.columns[0:11])\n\nfeatureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\nfeatureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n\nprint(featureValues.nlargest(7, 'score'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the transformed data\n\n# rescaledPredictors\nreBestFeatures = SelectKBest(score_func=f_classif, k=7)\nreFit = reBestFeatures.fit(rescaledPredictors, target)\n\nreScores = pd.DataFrame(reFit.scores_) \nrePvalues = pd.DataFrame(reFit.pvalues_)\nreColumns = pd.DataFrame(data.columns[0:11])\n\nreFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\nreFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n\n\n\n# standardizedPredictors\nstBestFeatures = SelectKBest(score_func=f_classif, k=7)\nstFit = stBestFeatures.fit(standardizedPredictors, target)\n\nstScores = pd.DataFrame(stFit.scores_) \nstPvalues = pd.DataFrame(stFit.pvalues_)\nstColumns = pd.DataFrame(data.columns[0:11])\n\nstFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\nstFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n\nprint(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using feature importance\nfrom sklearn.ensemble import ExtraTreesClassifier\nmodel = ExtraTreesClassifier()\nmodel.fit(predictors, target)\nprint(model.feature_importances_)\n\n# visualising feature importance\nimportances = pd.Series(model.feature_importances_, index=data.columns[0:11])\nimportances.nlargest(10).plot(kind='barh')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"#### Building the classification model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\nfrom sklearn.model_selection import train_test_split # random split\nfrom sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\np_train, p_test, t_train, t_test = train_test_split(predictors, target, \n                                                    test_size=0.30,random_state=42)\n\nlogit = LogisticRegression() # making instance of model\n\n# fitting the model on untransformed data\nlogit.fit(p_train, t_train)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Measuring model performance\n\nWe can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# predict on test data\npredictions = logit.predict(p_test) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# the confusion matrix\nfrom sklearn import metrics\ncm = metrics.confusion_matrix(t_test, predictions)\ncm\nsb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n          square=True, cmap='Blues') \nplt.ylabel('Actual label'); plt.xlabel('Predicted label')\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\nprint(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\nprint(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n                                                      # higher values are better\n# not affected by unbalanced classes\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ROC curve of true positive rate against false positive rate\n# shows tradeoff between sensitivy and specificity\n\npred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\nfpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\nauc = metrics.roc_auc_score(t_test, pred_probs)\nplt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\nplt.legend(loc=4)\nplt.ylabel('tpr'), plt.xlabel('fpr')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data\nnew_pred = logit.predict(new.values)\n\npred_df = pd.DataFrame(new_pred) \npred_df.columns=[\"CLASS\"]\npred_df.index.name=\"Index\" \npred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df.to_csv(\"ilujumba.csv\") \nprint(pred_df['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df.groupby('CLASS').size()[0].sum())\nprint(pred_df.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on rescaled data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit1 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit1.fit(p1_train, t1_train)\n\n# predict on test data\npredictions1 = logit1.predict(p1_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\nprint(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\nprint(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t1_test, predictions1))\n\n# rescaling new data\nnewArray = new.to_numpy()\nrescaledNew = scaler.fit_transform(newArray)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data (rescaled)\nnew_pred1 = logit1.predict(rescaledNew)\n\npred_df1 = pd.DataFrame(new_pred1) \npred_df1.columns=[\"CLASS\"]\npred_df1.index.name=\"Index\" \npred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df1.to_csv(\"ilujumba1.csv\") \nprint(pred_df1['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df1.groupby('CLASS').size()[0].sum())\nprint(pred_df1.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on standardized data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit2 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit2.fit(p2_train, t2_train)\n\n# predict on test data\npredictions2 = logit2.predict(p2_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\nprint(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\nprint(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\nprint('MCC: ',matthews_corrcoef(t2_test, predictions2))\n\n# standardizing new data\nstandardizedNew = scaler1.transform(newArray)\n\n# performance on new data (standaridized)\nnew_pred2 = logit2.predict(standardizedNew)\n\npred_df2 = pd.DataFrame(new_pred2) \npred_df2.columns=[\"CLASS\"]\npred_df2.index.name=\"Index\" \npred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df2.to_csv(\"ilujumba2.csv\") \nprint(pred_df2['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df2.groupby('CLASS').size()[0].sum())\nprint(pred_df2.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Using selected features, rescaled data and Logistic regression\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n                                                        test_size=0.30,random_state=42)\n\nlogit3 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit3.fit(p3_train, t3_train)\n\n# predict on test data\npredictions3 = logit3.predict(p3_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\nprint(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\nprint(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t3_test, predictions3))\n\n# rescaling new data\n# newArray = new.to_numpy()\n# rescaledNew = scaler.fit_transform(newArray)\n\n# performance on new data (rescaled)\nnew_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n\npred_df3 = pd.DataFrame(new_pred3) \npred_df3.columns=[\"CLASS\"]\npred_df3.index.name=\"Index\" \npred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df3.to_csv(\"ilujumba3.csv\") \nprint(pred_df3['CLASS'].unique())\n\n\n#printing the numbers of False and True\nprint(pred_df3.groupby('CLASS').size()[0].sum())\nprint(pred_df3.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Using cross-validation and Logistic Regression"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\n\nkfold = KFold(n_splits=10, random_state=42)\nmodel6 = LogisticRegression()\nmodel6.fit(predictors, target)\n\nresults = cross_val_score(model6, predictors, target)\nprint(results.mean())\n\nmodel6_pred = model6.predict(rescaledNew)\ndf6 = pd.DataFrame(model6_pred)\ndf6.columns = ['CLASS']\ndf6.index.name = 'Index'\ndf6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n\ndf6.to_csv('ilujumba7.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier with kfold cross-validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel7 = GaussianNB()\nmodel7.fit(predictors, target)\n\nresults = cross_val_score(model7, predictors, target)\nprint(results.mean())\n\nmodel7_pred = model7.predict(newArray)\ndf7 = pd.DataFrame(model7_pred)\ndf7.columns = ['CLASS']\ndf7.index.name = 'Index'\ndf7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf7.to_csv('ilujumba7.csv')\nprint(df7['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df7.groupby('CLASS').size()[0].sum())\nprint(df7.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier on rescaled features\n\nAssumes that all features are independent of each other and each feature contributes equally to the resulting class"},{"metadata":{"trusted":true},"cell_type":"code","source":"kfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel8 = GaussianNB()\nmodel8.fit(rescaledPredictors, target)\n\nresults1 = cross_val_score(model8, rescaledPredictors, target)\nprint(results1.mean())\n\nmodel8_pred = model8.predict(rescaledNew)\ndf8 = pd.DataFrame(model8_pred)\ndf8.columns = ['CLASS']\ndf8.index.name = 'Index'\ndf8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf8.to_csv('ilujumba8.csv')\nprint(df8['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df8.groupby('CLASS').size()[0].sum())\nprint(df8.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes and kfold validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import cross_val_predict\nfrom sklearn.naive_bayes import GaussianNB\n\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel9 = GaussianNB()\nmodel9.fit(predictors, target)\n\nresults = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\nprint('mean for results', results.mean())\n\npredic = cross_val_predict(model9, predictors, target, cv =10)\naccuracy = metrics.r2_score(target, predic)\nprint('cross-predicted accuracy ', accuracy)\n\nmodel9_pred = model9.predict(newArray)\ndf9 = pd.DataFrame(model9_pred)\ndf9.columns = ['CLASS']\ndf9.index.name = 'Index'\ndf9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf9.to_csv('ilujumba9.csv')\nprint(df9['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df9.groupby('CLASS').size()[0].sum())\nprint(df9.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Comparing several algorithms to look at the nature of the decision boundaries created"},{"metadata":{},"cell_type":"markdown","source":"https://medium.com/cascade-bio-blog/creating-visualizations-to-better-understand-your-data-and-models-part-2-28d5c46e956"},{"metadata":{},"cell_type":"markdown","source":"Algorithms define a st of hyperplanes that divide the datapoints to their respective classes and span the feature space trained on. Visualising enables one to understand the limitations of a given algorithm on a dataset given to it.\nThus decision boundaries enable one to understand to how the training data selected affects performance of the algorithm."},{"metadata":{},"cell_type":"markdown","source":"Ten sklearn classifier algorithms were compared"},{"metadata":{"trusted":true},"cell_type":"code","source":"#importing classifiers from the sklearn library\n\nfrom matplotlib.colors import ListedColormap\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neural_network import MLPClassifier #1\nfrom sklearn.neighbors import KNeighborsClassifier #2\nfrom sklearn.svm import SVC #3\nfrom sklearn.gaussian_process import GaussianProcessClassifier #4\nfrom sklearn.gaussian_process.kernels import RBF #5\nfrom sklearn.tree import DecisionTreeClassifier #6\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\nfrom sklearn.naive_bayes import GaussianNB #9\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\nfrom sklearn.linear_model import LogisticRegression #11\n\nnames = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n\nclassifiers = [\n    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n    DecisionTreeClassifier(),\n    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n    MLPClassifier(max_iter=1000), #iterations until converge\n    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n    GaussianNB(),\n    QuadraticDiscriminantAnalysis(),\n    LogisticRegression()]\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Dimensionality reduction\nhttps://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"},{"metadata":{},"cell_type":"markdown","source":"Since the data is multi-dimensional, it was reduced using Principal Component Analysis (PCA) to reduce it to two components.\nTrial runs were done to check how much of the variation in the data is explained by the principal components.\n\nAnother thing to keep in mind is that PCA works best on standardised/normalised data"},{"metadata":{"trusted":true},"cell_type":"code","source":"# preprocessing the dataset\ndataArray = data.to_numpy()\nX, y = dataArray[:,0:11], dataArray[0:,11]\nX = StandardScaler().fit_transform(X)\n\n# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\nfrom sklearn.decomposition import PCA\npca = PCA()\npca.fit_transform(X)\npca_variance = pca.explained_variance_\nplt.figure(figsize=(8, 6))\nplt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\nplt.legend()\nplt.ylabel('Variance ratio')\nplt.xlabel('Principal components')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\nninety_five = pca2.fit_transform(X)\nninety_five.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Eight features explain 95% of the variance in the dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"pca2 = PCA(3) # keeping features three principal components\nprincipalComponents = pca2.fit_transform(X)\n\nfrom mpl_toolkits.mplot3d import Axes3D\nplt.figure(figsize=(10,6))\nax = plt.axes(projection='3d')\nax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n           linewidths=1, alpha=.5,\n           edgecolor='k', s= 200,\n           c=data['CLASS'])\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The three pincipal components wete visualised using a 3D plot. The figure above shows clustering of the three components. Each component is not exactly independent of the others so the clusters overlap to some extent"},{"metadata":{"trusted":true},"cell_type":"code","source":"#converting principal component ndarrays to DataFrame format\nprincipalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\nfinalDf = pd.concat([principalDf, data['CLASS']], axis = 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"finalDf.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Visualising the top 2 principal components"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = plt.figure(figsize = (6,6))\nax = fig.add_subplot(111) \nax.set(xlim=(-10,10), ylim=(-10,10))\nax.set_xlabel('Principal Component 1', fontsize = 15)\nax.set_ylabel('Principal Component 2', fontsize = 15)\nax.set_title('top 2 components', fontsize = 20)\n\ntargets = [0, 1]\ncolors = ['r', 'g']\n\nfor target, color in zip(targets,colors):\n    indices = finalDf['CLASS'] == target\n    ax.scatter(finalDf.loc[indices, 'PC1']\n               , finalDf.loc[indices, 'PC2']\n               , c = color\n               , s = 50)\nax.legend(targets)\nax.grid()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# splitting the into training and test part\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"A mesh grid is required. This can be thought of as a matrix of coordinates upon which the model will make decisions.\nThese are then visualised to reveal decision boundaries.\nThe mesh grip was created based on the data and a step size of 0.02"},{"metadata":{"trusted":true},"cell_type":"code","source":"# creating mesh for the contour plot\n\nh = .02  # step size in the mesh\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Two principal components were used to enable visualisation on a scatter plot.\n\nThe parameters for the PCA were generated on the training data and these were applied on both the training and training sets"},{"metadata":{"trusted":true},"cell_type":"code","source":"pca4 = PCA(n_components=2)\n\n# applying PCA on training set\npca4.fit(X_train)\n\n#applying transform on training and testing sets\ntrain_ = pca4.transform(X_train)\ntest_ = pca4.transform(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_.shape, test_.shape","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"After transforming the data and the creating the meshgrid, decision boundaries for the algorithms were created by iterating over the classifiers."},{"metadata":{"trusted":true},"cell_type":"code","source":"figure = plt.figure(figsize=(27, 15))\ni = 1\n\ndatasets=[data]\nfor ds_cnt, ds in enumerate(datasets):\n    # just plot the dataset first\n    cm = plt.cm.RdBu\n    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n\n    if ds_cnt == 0:\n        ax.set_title(\"Input data\")\n        # Plot the top 2 principal components for training data\n        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n                    edgecolors='k')\n        # Plot the top 2 principal components for the testing data\n        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n                    edgecolors='k')\n        ax.set_xlim(xx.min(), xx.max())\n        ax.set_ylim(yy.min(), yy.max())\n        ax.set_xticks(())\n        ax.set_yticks(())\n        i += 1\n\n        # iterate over classifiers\n\n    for name, clf in zip(names, classifiers):\n        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n        clf.fit(train_, y_train)\n        score = clf.score(test_, y_test)\n\n        # Plot the decision boundary. For that, we will assign a color to each\n        # point in the mesh [x_min, x_max]x[y_min, y_max].\n\n        if hasattr(clf, \"decision_function\"):\n            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n        else:\n            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n\n        # Put the result into a color plot\n        Z = Z.reshape(xx.shape)\n        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n\n        # Plot the training points\n        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n        # Plot the testing points\n        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.4)\n\n        ax.set_xlim(xx.min(), xx.max())\n        ax.set_ylim(yy.min(), yy.max())\n        ax.set_xticks(())\n        ax.set_yticks(())\n        if ds_cnt == 0:\n            ax.set_title(name)\n            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n            i += 1\n\nplt.tight_layout()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Accuracies of the different algorithms are indicated on the lower right corner.\n\nThe plots show training points in solid colors and testing points semi-transparent. Contour decision boundaries were used which seperate points based on shared characteritics.\n"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}
\ No newline at end of file
diff --git a/Assignment Colab/Ibra Lujumba_kaggle_final.ipynb b/Assignment Colab/Ibra Lujumba_kaggle_final.ipynb
deleted file mode 100644
index 70f0df8..0000000
--- a/Assignment Colab/Ibra Lujumba_kaggle_final.ipynb	
+++ /dev/null
@@ -1,2050 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ACE_Uganda Kaggle competition 1\n",
-    "### by Ibra Lujumba"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Exploratory Data Analysis "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Importing python modules for data analysis and visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np # manipulation of arrays\n",
-    "import pandas as pd # manipulating dataframes\n",
-    "import matplotlib.pyplot as plt # data visualisation\n",
-    "import seaborn as sb # data visualisation,it is based on plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#ignoring warnings that may arise\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###### Importing the datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AMP_TrainSet.csv  Test.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls ../input/ace-class-assignment/\n",
-    "\n",
-    "# reading in the data\n",
-    "data = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\n",
-    "new = pd.read_csv('../input/ace-class-assignment/Test.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Checking the dimensions of the data as well as the datatype of each column"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((3038, 12), (758, 11))"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# checking dimensions of the datasets\n",
-    "data.shape, new.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(FULL_Charge           float64\n",
-       " FULL_AcidicMolPerc    float64\n",
-       " FULL_AURR980107       float64\n",
-       " FULL_DAYM780201       float64\n",
-       " FULL_GEOR030101       float64\n",
-       " FULL_OOBM850104       float64\n",
-       " NT_EFC195               int64\n",
-       " AS_MeanAmphiMoment    float64\n",
-       " AS_DAYM780201         float64\n",
-       " AS_FUKS010112         float64\n",
-       " CT_RACS820104         float64\n",
-       " CLASS                   int64\n",
-       " dtype: object,\n",
-       " FULL_Charge           float64\n",
-       " FULL_AcidicMolPerc    float64\n",
-       " FULL_AURR980107       float64\n",
-       " FULL_DAYM780201       float64\n",
-       " FULL_GEOR030101       float64\n",
-       " FULL_OOBM850104       float64\n",
-       " NT_EFC195               int64\n",
-       " AS_MeanAmphiMoment    float64\n",
-       " AS_DAYM780201         float64\n",
-       " AS_FUKS010112         float64\n",
-       " CT_RACS820104         float64\n",
-       " dtype: object)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# checking the datatypes of the variables\n",
-    "data.dtypes, new.dtypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "All the values in all the variables exists as either floats or integers.\n",
-    "\n",
-    "Proceeding to work with the training dataset to build the classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>FULL_Charge</th>\n",
-       "      <th>FULL_AcidicMolPerc</th>\n",
-       "      <th>FULL_AURR980107</th>\n",
-       "      <th>FULL_DAYM780201</th>\n",
-       "      <th>FULL_GEOR030101</th>\n",
-       "      <th>FULL_OOBM850104</th>\n",
-       "      <th>NT_EFC195</th>\n",
-       "      <th>AS_MeanAmphiMoment</th>\n",
-       "      <th>AS_DAYM780201</th>\n",
-       "      <th>AS_FUKS010112</th>\n",
-       "      <th>CT_RACS820104</th>\n",
-       "      <th>CLASS</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.060237</td>\n",
-       "      <td>8.521520</td>\n",
-       "      <td>0.971410</td>\n",
-       "      <td>73.668760</td>\n",
-       "      <td>0.994007</td>\n",
-       "      <td>-2.432927</td>\n",
-       "      <td>0.088545</td>\n",
-       "      <td>15.683233</td>\n",
-       "      <td>73.650828</td>\n",
-       "      <td>5.911361</td>\n",
-       "      <td>1.235255</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>3.819929</td>\n",
-       "      <td>7.586652</td>\n",
-       "      <td>0.107413</td>\n",
-       "      <td>8.527489</td>\n",
-       "      <td>0.031333</td>\n",
-       "      <td>1.707223</td>\n",
-       "      <td>0.284133</td>\n",
-       "      <td>11.575665</td>\n",
-       "      <td>9.166092</td>\n",
-       "      <td>0.693689</td>\n",
-       "      <td>0.210012</td>\n",
-       "      <td>0.500082</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-16.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.684000</td>\n",
-       "      <td>42.750000</td>\n",
-       "      <td>0.866000</td>\n",
-       "      <td>-10.432000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.041000</td>\n",
-       "      <td>42.778000</td>\n",
-       "      <td>3.533000</td>\n",
-       "      <td>0.785000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.516000</td>\n",
-       "      <td>0.895000</td>\n",
-       "      <td>68.294000</td>\n",
-       "      <td>0.974000</td>\n",
-       "      <td>-3.606000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.587500</td>\n",
-       "      <td>67.556000</td>\n",
-       "      <td>5.459250</td>\n",
-       "      <td>1.082000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>7.143000</td>\n",
-       "      <td>0.963000</td>\n",
-       "      <td>74.059500</td>\n",
-       "      <td>0.994000</td>\n",
-       "      <td>-2.296500</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>14.988500</td>\n",
-       "      <td>73.697000</td>\n",
-       "      <td>5.925500</td>\n",
-       "      <td>1.184000</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>13.158000</td>\n",
-       "      <td>1.041000</td>\n",
-       "      <td>79.343750</td>\n",
-       "      <td>1.011000</td>\n",
-       "      <td>-1.283250</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>26.807750</td>\n",
-       "      <td>79.778000</td>\n",
-       "      <td>6.382000</td>\n",
-       "      <td>1.351000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>30.000000</td>\n",
-       "      <td>46.667000</td>\n",
-       "      <td>1.451000</td>\n",
-       "      <td>101.682000</td>\n",
-       "      <td>1.196000</td>\n",
-       "      <td>3.576000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>51.280000</td>\n",
-       "      <td>103.167000</td>\n",
-       "      <td>8.662000</td>\n",
-       "      <td>2.192000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
-       "count  3038.000000         3038.000000      3038.000000      3038.000000   \n",
-       "mean      2.060237            8.521520         0.971410        73.668760   \n",
-       "std       3.819929            7.586652         0.107413         8.527489   \n",
-       "min     -16.000000            0.000000         0.684000        42.750000   \n",
-       "25%       0.000000            2.516000         0.895000        68.294000   \n",
-       "50%       2.000000            7.143000         0.963000        74.059500   \n",
-       "75%       4.000000           13.158000         1.041000        79.343750   \n",
-       "max      30.000000           46.667000         1.451000       101.682000   \n",
-       "\n",
-       "       FULL_GEOR030101  FULL_OOBM850104    NT_EFC195  AS_MeanAmphiMoment  \\\n",
-       "count      3038.000000      3038.000000  3038.000000         3038.000000   \n",
-       "mean          0.994007        -2.432927     0.088545           15.683233   \n",
-       "std           0.031333         1.707223     0.284133           11.575665   \n",
-       "min           0.866000       -10.432000     0.000000            0.041000   \n",
-       "25%           0.974000        -3.606000     0.000000            5.587500   \n",
-       "50%           0.994000        -2.296500     0.000000           14.988500   \n",
-       "75%           1.011000        -1.283250     0.000000           26.807750   \n",
-       "max           1.196000         3.576000     1.000000           51.280000   \n",
-       "\n",
-       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104        CLASS  \n",
-       "count    3038.000000    3038.000000    3038.000000  3038.000000  \n",
-       "mean       73.650828       5.911361       1.235255     0.500000  \n",
-       "std         9.166092       0.693689       0.210012     0.500082  \n",
-       "min        42.778000       3.533000       0.785000     0.000000  \n",
-       "25%        67.556000       5.459250       1.082000     0.000000  \n",
-       "50%        73.697000       5.925500       1.184000     0.500000  \n",
-       "75%        79.778000       6.382000       1.351000     1.000000  \n",
-       "max       103.167000       8.662000       2.192000     1.000000  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# getting the descriptive statistics of the train dataset such as arithmetic mean, \n",
-    "# standard deviation, quartiles and number of non-NA values in each column \n",
-    "data.describe()"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\n",
-    "AS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\n",
-    "For all the variables, the data points are not widely spread"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CLASS\n",
-       "0    1519\n",
-       "1    1519\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# checking the proprotions of classses\n",
-    "data.groupby('CLASS').size()"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "Both classes have an equal number of entries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f817a5bfd68>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# obtaining pairwise correlation values for the variables in the train dataset\n",
-    "# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\n",
-    "pearsoncorr = data.corr(method='pearson')\n",
-    "\n",
-    "# visualizing the correlation matrix as a heatmap to make interpretation easier\n",
-    "plt.figure(figsize=(10,10))\n",
-    "top_corr = pearsoncorr.index\n",
-    "sb.heatmap(pearsoncorr, \n",
-    "            xticklabels=pearsoncorr.columns,\n",
-    "            yticklabels=pearsoncorr.columns,\n",
-    "            cmap='RdYlGn',\n",
-    "            annot=True,\n",
-    "            linewidth=0.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can get the p-values associated with the correlation values using the code below.\n",
-    "\n",
-    "`from scipy.stats import pearsonr`\n",
-    "\n",
-    "`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n",
-    "\n",
-    "In this example, all values were too low to be informative"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# using a scatter plot matrix to visualise correlations\n",
-    "# plt.figure(figsize=(60,60))\n",
-    "# sb.pairplot(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\n",
-    "These variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n",
-    "\n",
-    "Variables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2830, array([0, 1]))"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n",
-    "\n",
-    "#this confirms that NT_EFC195 is a categorical variable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CLASS</th>\n",
-       "      <th>NT_EFC195</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   CLASS  NT_EFC195\n",
-       "0      1          0\n",
-       "1      1          1\n",
-       "2      1          0\n",
-       "3      1          0\n",
-       "4      1          0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "FULL_Charge           3.404241e-224\n",
-       "FULL_AcidicMolPerc    4.220004e-295\n",
-       "FULL_AURR980107       1.887905e-277\n",
-       "FULL_DAYM780201       6.997934e-245\n",
-       "FULL_GEOR030101        2.669597e-48\n",
-       "FULL_OOBM850104       7.441087e-154\n",
-       "NT_EFC195              2.189203e-48\n",
-       "AS_MeanAmphiMoment     0.000000e+00\n",
-       "AS_DAYM780201         4.911002e-142\n",
-       "AS_FUKS010112          6.540694e-02\n",
-       "CT_RACS820104          5.329249e-51\n",
-       "CLASS                  1.000000e+00\n",
-       "Name: CLASS, dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# getting the associated p-values. The value of 1 at the bottom should be ignored \n",
-    "from scipy.stats import pearsonr\n",
-    "data.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f817a0b9048>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# checking the distribution and skewness of variables\n",
-    "plt.figure(figsize=(10,6))\n",
-    "data.skew().plot(kind='bar')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "NT_EFC195\n",
-       "0    2769\n",
-       "1     269\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.groupby('NT_EFC195').size() # majority of the instances are of Class 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a3d90f0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a3754e0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a39c940>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a344da0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2f6240>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a31e6a0>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2c7b00>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a270f98>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a270fd0>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2a2860>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a08ecc0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a041160>]],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 12 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n",
-    "\n",
-    "Tranformation possibilities\n",
-    "* using the minimum and maximum scaler\n",
-    "* standardisation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Data transformation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# converting Pandas dataframe to ndArray\n",
-    "dataArray = data.to_numpy()\n",
-    "\n",
-    "# seperating the predictor and response variables\n",
-    "target = dataArray[:,11]\n",
-    "predictors = dataArray[:,0:11]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# using minMaxScaler to set all values between 0 and 1\n",
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "scaler = MinMaxScaler(feature_range=(0,1))\n",
-    "rescaledPredictors = scaler.fit_transform(predictors)\n",
-    "                        \n",
-    "\n",
-    "# using StandardScaler\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "scaler1 = StandardScaler().fit(predictors)\n",
-    "standardizedPredictors = scaler1.transform(predictors)\n",
-    "                        \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Feature selection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            predictor        score         pvalue\n",
-      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
-      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
-      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
-      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
-      "0         FULL_Charge  1214.902838  3.404241e-224\n",
-      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
-      "8       AS_DAYM780201   717.317408  4.911002e-142\n"
-     ]
-    }
-   ],
-   "source": [
-    "# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n",
-    "\n",
-    "# untransformed data\n",
-    "from sklearn.feature_selection import SelectKBest, f_classif\n",
-    "bestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
-    "fit = bestFeatures.fit(predictors, target)\n",
-    "\n",
-    "scores = pd.DataFrame(fit.scores_) \n",
-    "pvalues = pd.DataFrame(fit.pvalues_)\n",
-    "columns = pd.DataFrame(data.columns[0:11])\n",
-    "\n",
-    "featureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\n",
-    "featureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n",
-    "\n",
-    "print(featureValues.nlargest(7, 'score'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         re_predictor     re_score      re_pvalue\n",
-      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
-      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
-      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
-      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
-      "0         FULL_Charge  1214.902838  3.404241e-224\n",
-      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
-      "8       AS_DAYM780201   717.317408  4.911002e-142\n",
-      "         st_predictor     st_score      st_pvalue\n",
-      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
-      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
-      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
-      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
-      "0         FULL_Charge  1214.902838  3.404241e-224\n",
-      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
-      "8       AS_DAYM780201   717.317408  4.911002e-142\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(None, None)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# checking the transformed data\n",
-    "\n",
-    "# rescaledPredictors\n",
-    "reBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
-    "reFit = reBestFeatures.fit(rescaledPredictors, target)\n",
-    "\n",
-    "reScores = pd.DataFrame(reFit.scores_) \n",
-    "rePvalues = pd.DataFrame(reFit.pvalues_)\n",
-    "reColumns = pd.DataFrame(data.columns[0:11])\n",
-    "\n",
-    "reFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\n",
-    "reFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n",
-    "\n",
-    "\n",
-    "\n",
-    "# standardizedPredictors\n",
-    "stBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
-    "stFit = stBestFeatures.fit(standardizedPredictors, target)\n",
-    "\n",
-    "stScores = pd.DataFrame(stFit.scores_) \n",
-    "stPvalues = pd.DataFrame(stFit.pvalues_)\n",
-    "stColumns = pd.DataFrame(data.columns[0:11])\n",
-    "\n",
-    "stFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\n",
-    "stFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n",
-    "\n",
-    "print(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.08173854 0.13773548 0.0922014  0.09119859 0.04664165 0.08280402\n",
-      " 0.03303368 0.297985   0.05331243 0.03219657 0.05115262]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# using feature importance\n",
-    "from sklearn.ensemble import ExtraTreesClassifier\n",
-    "model = ExtraTreesClassifier()\n",
-    "model.fit(predictors, target)\n",
-    "print(model.feature_importances_)\n",
-    "\n",
-    "# visualising feature importance\n",
-    "importances = pd.Series(model.feature_importances_, index=data.columns[0:11])\n",
-    "importances.nlargest(10).plot(kind='barh')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Building the classification model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
-       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
-       "                   warm_start=False)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\n",
-    "from sklearn.model_selection import train_test_split # random split\n",
-    "from sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\n",
-    "p_train, p_test, t_train, t_test = train_test_split(predictors, target, \n",
-    "                                                    test_size=0.30,random_state=42)\n",
-    "\n",
-    "logit = LogisticRegression() # making instance of model\n",
-    "\n",
-    "# fitting the model on untransformed data\n",
-    "logit.fit(p_train, t_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Measuring model performance\n",
-    "\n",
-    "We can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# predict on test data\n",
-    "predictions = logit.predict(p_test) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 15.0, 'Predicted label')"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# the confusion matrix\n",
-    "from sklearn import metrics\n",
-    "cm = metrics.confusion_matrix(t_test, predictions)\n",
-    "cm\n",
-    "sb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n",
-    "          square=True, cmap='Blues') \n",
-    "plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy:  91.77631578947368\n",
-      "Precision:  92.82608695652173\n",
-      "Recall:  91.04477611940298\n",
-      "MCC:  0.8356480321235562\n"
-     ]
-    }
-   ],
-   "source": [
-    "# performance metrics\n",
-    "print(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\n",
-    "print(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\n",
-    "print(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "print('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n",
-    "                                                      # higher values are better\n",
-    "# not affected by unbalanced classes\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ROC curve of true positive rate against false positive rate\n",
-    "# shows tradeoff between sensitivy and specificity\n",
-    "\n",
-    "pred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\n",
-    "fpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\n",
-    "auc = metrics.roc_auc_score(t_test, pred_probs)\n",
-    "plt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\n",
-    "plt.legend(loc=4)\n",
-    "plt.ylabel('tpr'), plt.xlabel('fpr')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['False' 'True']\n",
-      "386\n",
-      "372\n"
-     ]
-    }
-   ],
-   "source": [
-    "# performance on new data\n",
-    "new_pred = logit.predict(new.values)\n",
-    "\n",
-    "pred_df = pd.DataFrame(new_pred) \n",
-    "pred_df.columns=[\"CLASS\"]\n",
-    "pred_df.index.name=\"Index\" \n",
-    "pred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n",
-    "\n",
-    "#csv file output\n",
-    "pred_df.to_csv(\"ilujumba.csv\") \n",
-    "print(pred_df['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(pred_df.groupby('CLASS').size()[0].sum())\n",
-    "print(pred_df.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Logistic regression on rescaled data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy:  91.66666666666666\n",
-      "Precision:  92.81045751633987\n",
-      "Recall:  90.8315565031983\n",
-      "MCC:  0.8335025900424667\n"
-     ]
-    }
-   ],
-   "source": [
-    "p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n",
-    "                                                        test_size=0.30,random_state=42)\n",
-    "\n",
-    "logit1 = LogisticRegression() # making instance of model\n",
-    "\n",
-    "# fitting the model on rescaled data\n",
-    "logit1.fit(p1_train, t1_train)\n",
-    "\n",
-    "# predict on test data\n",
-    "predictions1 = logit1.predict(p1_test)\n",
-    "\n",
-    "# performance metrics\n",
-    "print(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\n",
-    "print(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\n",
-    "print(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "print('MCC: ',matthews_corrcoef(t1_test, predictions1))\n",
-    "\n",
-    "# rescaling new data\n",
-    "newArray = new.to_numpy()\n",
-    "rescaledNew = scaler.fit_transform(newArray)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['False' 'True']\n",
-      "378\n",
-      "380\n"
-     ]
-    }
-   ],
-   "source": [
-    "# performance on new data (rescaled)\n",
-    "new_pred1 = logit1.predict(rescaledNew)\n",
-    "\n",
-    "pred_df1 = pd.DataFrame(new_pred1) \n",
-    "pred_df1.columns=[\"CLASS\"]\n",
-    "pred_df1.index.name=\"Index\" \n",
-    "pred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n",
-    "\n",
-    "#csv file output\n",
-    "pred_df1.to_csv(\"ilujumba1.csv\") \n",
-    "print(pred_df1['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(pred_df1.groupby('CLASS').size()[0].sum())\n",
-    "print(pred_df1.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Logistic regression on standardized data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy:  91.8859649122807\n",
-      "Precision:  93.21663019693655\n",
-      "Recall:  90.8315565031983\n",
-      "MCC:  0.8379994044962448\n",
-      "['False' 'True']\n",
-      "388\n",
-      "370\n"
-     ]
-    }
-   ],
-   "source": [
-    "p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n",
-    "                                                        test_size=0.30,random_state=42)\n",
-    "\n",
-    "logit2 = LogisticRegression() # making instance of model\n",
-    "\n",
-    "# fitting the model on rescaled data\n",
-    "logit2.fit(p2_train, t2_train)\n",
-    "\n",
-    "# predict on test data\n",
-    "predictions2 = logit2.predict(p2_test)\n",
-    "\n",
-    "# performance metrics\n",
-    "print(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\n",
-    "print(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\n",
-    "print(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\n",
-    "print('MCC: ',matthews_corrcoef(t2_test, predictions2))\n",
-    "\n",
-    "# standardizing new data\n",
-    "standardizedNew = scaler1.transform(newArray)\n",
-    "\n",
-    "# performance on new data (standaridized)\n",
-    "new_pred2 = logit2.predict(standardizedNew)\n",
-    "\n",
-    "pred_df2 = pd.DataFrame(new_pred2) \n",
-    "pred_df2.columns=[\"CLASS\"]\n",
-    "pred_df2.index.name=\"Index\" \n",
-    "pred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n",
-    "\n",
-    "#csv file output\n",
-    "pred_df2.to_csv(\"ilujumba2.csv\") \n",
-    "print(pred_df2['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(pred_df2.groupby('CLASS').size()[0].sum())\n",
-    "print(pred_df2.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Using selected features, rescaled data and Logistic regression\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy:  90.5701754385965\n",
-      "Precision:  91.36069114470843\n",
-      "Recall:  90.19189765458422\n",
-      "MCC:  0.8113912389992642\n",
-      "['False' 'True']\n",
-      "390\n",
-      "368\n"
-     ]
-    }
-   ],
-   "source": [
-    "p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n",
-    "                                                        test_size=0.30,random_state=42)\n",
-    "\n",
-    "logit3 = LogisticRegression() # making instance of model\n",
-    "\n",
-    "# fitting the model on rescaled data\n",
-    "logit3.fit(p3_train, t3_train)\n",
-    "\n",
-    "# predict on test data\n",
-    "predictions3 = logit3.predict(p3_test)\n",
-    "\n",
-    "# performance metrics\n",
-    "print(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\n",
-    "print(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\n",
-    "print(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "print('MCC: ',matthews_corrcoef(t3_test, predictions3))\n",
-    "\n",
-    "# rescaling new data\n",
-    "# newArray = new.to_numpy()\n",
-    "# rescaledNew = scaler.fit_transform(newArray)\n",
-    "\n",
-    "# performance on new data (rescaled)\n",
-    "new_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n",
-    "\n",
-    "pred_df3 = pd.DataFrame(new_pred3) \n",
-    "pred_df3.columns=[\"CLASS\"]\n",
-    "pred_df3.index.name=\"Index\" \n",
-    "pred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n",
-    "\n",
-    "#csv file output\n",
-    "pred_df3.to_csv(\"ilujumba3.csv\") \n",
-    "print(pred_df3['CLASS'].unique())\n",
-    "\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(pred_df3.groupby('CLASS').size()[0].sum())\n",
-    "print(pred_df3.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Using cross-validation and Logistic Regression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8387328752276078\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import KFold\n",
-    "from sklearn.model_selection import cross_val_score\n",
-    "\n",
-    "kfold = KFold(n_splits=10, random_state=42)\n",
-    "model6 = LogisticRegression()\n",
-    "model6.fit(predictors, target)\n",
-    "\n",
-    "results = cross_val_score(model6, predictors, target)\n",
-    "print(results.mean())\n",
-    "\n",
-    "model6_pred = model6.predict(rescaledNew)\n",
-    "df6 = pd.DataFrame(model6_pred)\n",
-    "df6.columns = ['CLASS']\n",
-    "df6.index.name = 'Index'\n",
-    "df6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n",
-    "\n",
-    "df6.to_csv('ilujumba7.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Naive Bayes classifier with kfold cross-validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.887773129281193\n",
-      "['True' 'False']\n",
-      "370\n",
-      "388\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.naive_bayes import GaussianNB\n",
-    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
-    "model7 = GaussianNB()\n",
-    "model7.fit(predictors, target)\n",
-    "\n",
-    "results = cross_val_score(model7, predictors, target)\n",
-    "print(results.mean())\n",
-    "\n",
-    "model7_pred = model7.predict(newArray)\n",
-    "df7 = pd.DataFrame(model7_pred)\n",
-    "df7.columns = ['CLASS']\n",
-    "df7.index.name = 'Index'\n",
-    "df7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n",
-    "\n",
-    "df7.to_csv('ilujumba7.csv')\n",
-    "print(df7['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(df7.groupby('CLASS').size()[0].sum())\n",
-    "print(df7.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Naive Bayes classifier on rescaled features\n",
-    "\n",
-    "Assumes that all features are independent of each other and each feature contributes equally to the resulting class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.887773129281193\n",
-      "['True' 'False']\n",
-      "347\n",
-      "411\n"
-     ]
-    }
-   ],
-   "source": [
-    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
-    "model8 = GaussianNB()\n",
-    "model8.fit(rescaledPredictors, target)\n",
-    "\n",
-    "results1 = cross_val_score(model8, rescaledPredictors, target)\n",
-    "print(results1.mean())\n",
-    "\n",
-    "model8_pred = model8.predict(rescaledNew)\n",
-    "df8 = pd.DataFrame(model8_pred)\n",
-    "df8.columns = ['CLASS']\n",
-    "df8.index.name = 'Index'\n",
-    "df8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n",
-    "\n",
-    "df8.to_csv('ilujumba8.csv')\n",
-    "print(df8['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(df8.groupby('CLASS').size()[0].sum())\n",
-    "print(df8.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Naive Bayes and kfold validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean for results 0.9101496004863645\n",
-      "cross-predicted accuracy  0.6405529953917051\n",
-      "['True' 'False']\n",
-      "370\n",
-      "388\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import cross_val_predict\n",
-    "from sklearn.naive_bayes import GaussianNB\n",
-    "\n",
-    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
-    "model9 = GaussianNB()\n",
-    "model9.fit(predictors, target)\n",
-    "\n",
-    "results = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\n",
-    "print('mean for results', results.mean())\n",
-    "\n",
-    "predic = cross_val_predict(model9, predictors, target, cv =10)\n",
-    "accuracy = metrics.r2_score(target, predic)\n",
-    "print('cross-predicted accuracy ', accuracy)\n",
-    "\n",
-    "model9_pred = model9.predict(newArray)\n",
-    "df9 = pd.DataFrame(model9_pred)\n",
-    "df9.columns = ['CLASS']\n",
-    "df9.index.name = 'Index'\n",
-    "df9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n",
-    "\n",
-    "df9.to_csv('ilujumba9.csv')\n",
-    "print(df9['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(df9.groupby('CLASS').size()[0].sum())\n",
-    "print(df9.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Comparing several algorithms to look at the nature of the decision boundaries created"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#importing classifiers from the sklearn library\n",
-    "\n",
-    "from matplotlib.colors import ListedColormap\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.neural_network import MLPClassifier #1\n",
-    "from sklearn.neighbors import KNeighborsClassifier #2\n",
-    "from sklearn.svm import SVC #3\n",
-    "from sklearn.gaussian_process import GaussianProcessClassifier #4\n",
-    "from sklearn.gaussian_process.kernels import RBF #5\n",
-    "from sklearn.tree import DecisionTreeClassifier #6\n",
-    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\n",
-    "from sklearn.naive_bayes import GaussianNB #9\n",
-    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\n",
-    "from sklearn.linear_model import LogisticRegression #11\n",
-    "\n",
-    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
-    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
-    "         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n",
-    "\n",
-    "classifiers = [\n",
-    "    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n",
-    "    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n",
-    "    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n",
-    "    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n",
-    "    DecisionTreeClassifier(),\n",
-    "    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n",
-    "    MLPClassifier(max_iter=1000), #iterations until converge\n",
-    "    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n",
-    "    GaussianNB(),\n",
-    "    QuadraticDiscriminantAnalysis(),\n",
-    "    LogisticRegression()]\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Dimensionality reduction\n",
-    "https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# preprocessing the dataset\n",
-    "dataArray = data.to_numpy()\n",
-    "X, y = dataArray[:,0:11], dataArray[0:,11]\n",
-    "X = StandardScaler().fit_transform(X)\n",
-    "\n",
-    "# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\n",
-    "from sklearn.decomposition import PCA\n",
-    "pca = PCA()\n",
-    "pca.fit_transform(X)\n",
-    "pca_variance = pca.explained_variance_\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\n",
-    "plt.legend()\n",
-    "plt.ylabel('Variance ratio')\n",
-    "plt.xlabel('Principal components')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3038, 8)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\n",
-    "ninety_five = pca2.fit_transform(X)\n",
-    "ninety_five.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Explained variance:  0.9528002773163116\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Eight features explain 95% of the variance in the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pca2 = PCA(3) # keeping features three principal components\n",
-    "principalComponents = pca2.fit_transform(X)\n",
-    "\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "plt.figure(figsize=(10,6))\n",
-    "ax = plt.axes(projection='3d')\n",
-    "ax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n",
-    "           linewidths=1, alpha=.5,\n",
-    "           edgecolor='k', s= 200,\n",
-    "           c=data['CLASS'])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3038, 3)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "principalComponents.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\n",
-    "finalDf = pd.concat([principalDf, data['CLASS']], axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>PC1</th>\n",
-       "      <th>PC2</th>\n",
-       "      <th>PC3</th>\n",
-       "      <th>CLASS</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-0.423532</td>\n",
-       "      <td>-0.528139</td>\n",
-       "      <td>0.010959</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-1.428729</td>\n",
-       "      <td>-1.258821</td>\n",
-       "      <td>-1.767912</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-1.211100</td>\n",
-       "      <td>2.074380</td>\n",
-       "      <td>-1.508877</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.912438</td>\n",
-       "      <td>2.516293</td>\n",
-       "      <td>-0.041182</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.947851</td>\n",
-       "      <td>2.369553</td>\n",
-       "      <td>-0.713456</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        PC1       PC2       PC3  CLASS\n",
-       "0 -0.423532 -0.528139  0.010959      1\n",
-       "1 -1.428729 -1.258821 -1.767912      1\n",
-       "2 -1.211100  2.074380 -1.508877      1\n",
-       "3 -0.912438  2.516293 -0.041182      1\n",
-       "4 -0.947851  2.369553 -0.713456      1"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "finalDf.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Variance explained by three PCs:  65.30291340147794 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Visualising the top 2 principal components"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize = (6,6))\n",
-    "ax = fig.add_subplot(111) \n",
-    "ax.set(xlim=(-10,10), ylim=(-10,10))\n",
-    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
-    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
-    "ax.set_title('top 2 components', fontsize = 20)\n",
-    "\n",
-    "targets = [0, 1]\n",
-    "colors = ['r', 'g']\n",
-    "\n",
-    "for target, color in zip(targets,colors):\n",
-    "    indices = finalDf['CLASS'] == target\n",
-    "    ax.scatter(finalDf.loc[indices, 'PC1']\n",
-    "               , finalDf.loc[indices, 'PC2']\n",
-    "               , c = color\n",
-    "               , s = 50)\n",
-    "ax.legend(targets)\n",
-    "ax.grid()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# splitting the into training and test part\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# creating mesh for the contour plot\n",
-    "\n",
-    "h = .02  # step size in the mesh\n",
-    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
-    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
-    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca4 = PCA(n_components=2)\n",
-    "\n",
-    "# applying PCA on training set\n",
-    "pca4.fit(X_train)\n",
-    "\n",
-    "#applying transform on training and testing sets\n",
-    "train_ = pca4.transform(X_train)\n",
-    "test_ = pca4.transform(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Explained variance:  0.5330557904151474\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((2126, 2), (912, 2))"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_.shape, test_.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'plt' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-f281cd67b166>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfigure\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mds_cnt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "figure = plt.figure(figsize=(27, 15))\n",
-    "i = 1\n",
-    "\n",
-    "datasets=[data]\n",
-    "for ds_cnt, ds in enumerate(datasets):\n",
-    "    # just plot the dataset first\n",
-    "    cm = plt.cm.RdBu\n",
-    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
-    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
-    "\n",
-    "    if ds_cnt == 0:\n",
-    "        ax.set_title(\"Input data\")\n",
-    "        # Plot the top 2 principal components for training data\n",
-    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n",
-    "                    edgecolors='k')\n",
-    "        # Plot the top 2 principal components for the testing data\n",
-    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
-    "                    edgecolors='k')\n",
-    "        ax.set_xlim(xx.min(), xx.max())\n",
-    "        ax.set_ylim(yy.min(), yy.max())\n",
-    "        ax.set_xticks(())\n",
-    "        ax.set_yticks(())\n",
-    "        i += 1\n",
-    "\n",
-    "        # iterate over classifiers\n",
-    "\n",
-    "    for name, clf in zip(names, classifiers):\n",
-    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
-    "        clf.fit(train_, y_train)\n",
-    "        score = clf.score(test_, y_test)\n",
-    "\n",
-    "        # Plot the decision boundary. For that, we will assign a color to each\n",
-    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
-    "\n",
-    "        if hasattr(clf, \"decision_function\"):\n",
-    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n",
-    "        else:\n",
-    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n",
-    "\n",
-    "        # Put the result into a color plot\n",
-    "        Z = Z.reshape(xx.shape)\n",
-    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
-    "\n",
-    "        # Plot the training points\n",
-    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n",
-    "        # Plot the testing points\n",
-    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.6)\n",
-    "\n",
-    "        ax.set_xlim(xx.min(), xx.max())\n",
-    "        ax.set_ylim(yy.min(), yy.max())\n",
-    "        ax.set_xticks(())\n",
-    "        ax.set_yticks(())\n",
-    "        if ds_cnt == 0:\n",
-    "            ax.set_title(name)\n",
-    "            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n",
-    "            i += 1\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 0c64fa47f1a83ddb1bf2e05958c04899fc1f8b76 Mon Sep 17 00:00:00 2001
From: Atwine <twinmugume@gmail.com>
Date: Sat, 14 Mar 2020 16:10:56 +0300
Subject: [PATCH 07/10] resolve errors as per spoken

---
 .DS_Store                                     |  Bin 6148 -> 6148 bytes
 .../Ibra Lujumba_kaggle-checkpoint.ipynb      | 1296 ++++++++++
 Assignment Colab/Ibra Lujumba_kaggle.ipynb    | 2160 ++++++++++++++++-
 Assignment Colab/ilujumba.csv                 |  759 ++++++
 Assignment Colab/ilujumba1.csv                |  759 ++++++
 Assignment Colab/ilujumba2.csv                |  759 ++++++
 Assignment Colab/ilujumba3.csv                |  759 ++++++
 Assignment Colab/ilujumba7.csv                |  759 ++++++
 Assignment Colab/ilujumba8.csv                |  759 ++++++
 Assignment Colab/ilujumba9.csv                |  759 ++++++
 10 files changed, 8768 insertions(+), 1 deletion(-)
 create mode 100644 Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb
 create mode 100644 Assignment Colab/ilujumba.csv
 create mode 100644 Assignment Colab/ilujumba1.csv
 create mode 100644 Assignment Colab/ilujumba2.csv
 create mode 100644 Assignment Colab/ilujumba3.csv
 create mode 100644 Assignment Colab/ilujumba7.csv
 create mode 100644 Assignment Colab/ilujumba8.csv
 create mode 100644 Assignment Colab/ilujumba9.csv

diff --git a/.DS_Store b/.DS_Store
index 8ce8b0c8ede95fdc0f7947e485760f506dbd85c2..13a709c66636531d05da8cadeb5ecdecdf45b010 100644
GIT binary patch
delta 12
TcmZoMXfc?ugOOq5PH%Al9!>;)

delta 12
TcmZoMXfc?ugOPFLPH%Al9#aH=

diff --git a/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb b/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb
new file mode 100644
index 0000000..bd61f51
--- /dev/null
+++ b/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb	
@@ -0,0 +1,1296 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ACE_Uganda Kaggle competition 1\n",
+    "### by Ibra Lujumba"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Exploratory Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is done to try to understand the properties of the data before any machine learning algorithm id used to make predictions about the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Importing python modules for data analysis and visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # manipulation of arrays\n",
+    "import pandas as pd # manipulating dataframes\n",
+    "import matplotlib.pyplot as plt # data visualisation\n",
+    "import seaborn as sb # data visualisation,it is based on plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ignoring warnings that may arise\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###### Importing the datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls ../input/ace-class-assignment/\n",
+    "\n",
+    "# reading in the data\n",
+    "data = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\n",
+    "new = pd.read_csv('../input/ace-class-assignment/Test.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Checking the dimensions of the data as well as the datatype of each column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking dimensions of the datasets\n",
+    "data.shape, new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the datatypes of the variables\n",
+    "data.dtypes, new.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the values in all the variables exists as either floats or integers.\n",
+    "\n",
+    "Proceeding to work with the training dataset to build the classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# getting the descriptive statistics of the train dataset such as arithmetic mean, \n",
+    "# standard deviation, quartiles and number of non-NA values in each column \n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\n",
+    "AS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\n",
+    "For all the variables, the data points are not widely spread"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the proprotions of classses\n",
+    "data.groupby('CLASS').size()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Both classes have an equal number of entries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# obtaining pairwise correlation values for the variables in the train dataset\n",
+    "# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\n",
+    "pearsoncorr = data.corr(method='pearson')\n",
+    "\n",
+    "# visualizing the correlation matrix as a heatmap to make interpretation easier\n",
+    "plt.figure(figsize=(10,10))\n",
+    "top_corr = pearsoncorr.index\n",
+    "sb.heatmap(pearsoncorr, \n",
+    "            xticklabels=pearsoncorr.columns,\n",
+    "            yticklabels=pearsoncorr.columns,\n",
+    "            cmap='RdYlGn',\n",
+    "            annot=True,\n",
+    "            linewidth=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the p-values associated with the correlation values using the code below.\n",
+    "\n",
+    "`from scipy.stats import pearsonr`\n",
+    "\n",
+    "`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n",
+    "\n",
+    "In this example, all values were too low to be informative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using a scatter plot matrix to visualise correlations\n",
+    "# plt.figure(figsize=(60,60))\n",
+    "# sb.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\n",
+    "These variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n",
+    "\n",
+    "Variables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n",
+    "\n",
+    "#this confirms that NT_EFC195 is a categorical variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# getting the associated p-values. The value of 1 at the bottom should be ignored \n",
+    "from scipy.stats import pearsonr\n",
+    "data.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the distribution and skewness of variables\n",
+    "plt.figure(figsize=(10,6))\n",
+    "data.skew().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of the variables are minimally skewed except NT_EFC195. Further checks will be done to try to understand the properties of this variable.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.groupby('NT_EFC195').size() # majority of the instances are of Class 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The skewedness in this variable can be understood by having most of its values at zeros\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n",
+    "\n",
+    "Tranformation possibilities\n",
+    "* using the minimum and maximum scaler\n",
+    "* standardisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Better performance of algorithms can be obtained if the data is transformed.\n",
+    "Some algorithms are may take features with large values as the most important features in the predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Seperating the predictor variables from the target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# converting Pandas dataframe to ndArray\n",
+    "dataArray = data.to_numpy()\n",
+    "\n",
+    "# seperating the predictor and response variables\n",
+    "target = dataArray[:,11]\n",
+    "predictors = dataArray[:,0:11]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using minMaxScaler to set all values between 0 and 1\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "rescaledPredictors = scaler.fit_transform(predictors)\n",
+    "                        \n",
+    "\n",
+    "# using StandardScaler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler1 = StandardScaler().fit(predictors)\n",
+    "standardizedPredictors = scaler1.transform(predictors)\n",
+    "                        \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n",
+    "\n",
+    "# untransformed data\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif\n",
+    "bestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "fit = bestFeatures.fit(predictors, target)\n",
+    "\n",
+    "scores = pd.DataFrame(fit.scores_) \n",
+    "pvalues = pd.DataFrame(fit.pvalues_)\n",
+    "columns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "featureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\n",
+    "featureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n",
+    "\n",
+    "print(featureValues.nlargest(7, 'score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the transformed data\n",
+    "\n",
+    "# rescaledPredictors\n",
+    "reBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "reFit = reBestFeatures.fit(rescaledPredictors, target)\n",
+    "\n",
+    "reScores = pd.DataFrame(reFit.scores_) \n",
+    "rePvalues = pd.DataFrame(reFit.pvalues_)\n",
+    "reColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "reFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\n",
+    "reFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "# standardizedPredictors\n",
+    "stBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "stFit = stBestFeatures.fit(standardizedPredictors, target)\n",
+    "\n",
+    "stScores = pd.DataFrame(stFit.scores_) \n",
+    "stPvalues = pd.DataFrame(stFit.pvalues_)\n",
+    "stColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "stFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\n",
+    "stFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n",
+    "\n",
+    "print(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using feature importance\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "model = ExtraTreesClassifier()\n",
+    "model.fit(predictors, target)\n",
+    "print(model.feature_importances_)\n",
+    "\n",
+    "# visualising feature importance\n",
+    "importances = pd.Series(model.feature_importances_, index=data.columns[0:11])\n",
+    "importances.nlargest(10).plot(kind='barh')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Building the classification model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\n",
+    "from sklearn.model_selection import train_test_split # random split\n",
+    "from sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\n",
+    "p_train, p_test, t_train, t_test = train_test_split(predictors, target, \n",
+    "                                                    test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on untransformed data\n",
+    "logit.fit(p_train, t_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Measuring model performance\n",
+    "\n",
+    "We can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict on test data\n",
+    "predictions = logit.predict(p_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the confusion matrix\n",
+    "from sklearn import metrics\n",
+    "cm = metrics.confusion_matrix(t_test, predictions)\n",
+    "cm\n",
+    "sb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n",
+    "          square=True, cmap='Blues') \n",
+    "plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n",
+    "                                                      # higher values are better\n",
+    "# not affected by unbalanced classes\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ROC curve of true positive rate against false positive rate\n",
+    "# shows tradeoff between sensitivy and specificity\n",
+    "\n",
+    "pred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\n",
+    "fpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\n",
+    "auc = metrics.roc_auc_score(t_test, pred_probs)\n",
+    "plt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('tpr'), plt.xlabel('fpr')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance on new data\n",
+    "new_pred = logit.predict(new.values)\n",
+    "\n",
+    "pred_df = pd.DataFrame(new_pred) \n",
+    "pred_df.columns=[\"CLASS\"]\n",
+    "pred_df.index.name=\"Index\" \n",
+    "pred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df.to_csv(\"ilujumba.csv\") \n",
+    "print(pred_df['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on rescaled data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit1 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit1.fit(p1_train, t1_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions1 = logit1.predict(p1_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t1_test, predictions1))\n",
+    "\n",
+    "# rescaling new data\n",
+    "newArray = new.to_numpy()\n",
+    "rescaledNew = scaler.fit_transform(newArray)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance on new data (rescaled)\n",
+    "new_pred1 = logit1.predict(rescaledNew)\n",
+    "\n",
+    "pred_df1 = pd.DataFrame(new_pred1) \n",
+    "pred_df1.columns=[\"CLASS\"]\n",
+    "pred_df1.index.name=\"Index\" \n",
+    "pred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df1.to_csv(\"ilujumba1.csv\") \n",
+    "print(pred_df1['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df1.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df1.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on standardized data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit2 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit2.fit(p2_train, t2_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions2 = logit2.predict(p2_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\n",
+    "print('MCC: ',matthews_corrcoef(t2_test, predictions2))\n",
+    "\n",
+    "# standardizing new data\n",
+    "standardizedNew = scaler1.transform(newArray)\n",
+    "\n",
+    "# performance on new data (standaridized)\n",
+    "new_pred2 = logit2.predict(standardizedNew)\n",
+    "\n",
+    "pred_df2 = pd.DataFrame(new_pred2) \n",
+    "pred_df2.columns=[\"CLASS\"]\n",
+    "pred_df2.index.name=\"Index\" \n",
+    "pred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df2.to_csv(\"ilujumba2.csv\") \n",
+    "print(pred_df2['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df2.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df2.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using selected features, rescaled data and Logistic regression\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit3 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit3.fit(p3_train, t3_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions3 = logit3.predict(p3_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t3_test, predictions3))\n",
+    "\n",
+    "# rescaling new data\n",
+    "# newArray = new.to_numpy()\n",
+    "# rescaledNew = scaler.fit_transform(newArray)\n",
+    "\n",
+    "# performance on new data (rescaled)\n",
+    "new_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n",
+    "\n",
+    "pred_df3 = pd.DataFrame(new_pred3) \n",
+    "pred_df3.columns=[\"CLASS\"]\n",
+    "pred_df3.index.name=\"Index\" \n",
+    "pred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df3.to_csv(\"ilujumba3.csv\") \n",
+    "print(pred_df3['CLASS'].unique())\n",
+    "\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df3.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df3.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using cross-validation and Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42)\n",
+    "model6 = LogisticRegression()\n",
+    "model6.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model6, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model6_pred = model6.predict(rescaledNew)\n",
+    "df6 = pd.DataFrame(model6_pred)\n",
+    "df6.columns = ['CLASS']\n",
+    "df6.index.name = 'Index'\n",
+    "df6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n",
+    "\n",
+    "df6.to_csv('ilujumba7.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier with kfold cross-validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model7 = GaussianNB()\n",
+    "model7.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model7, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model7_pred = model7.predict(newArray)\n",
+    "df7 = pd.DataFrame(model7_pred)\n",
+    "df7.columns = ['CLASS']\n",
+    "df7.index.name = 'Index'\n",
+    "df7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df7.to_csv('ilujumba7.csv')\n",
+    "print(df7['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df7.groupby('CLASS').size()[0].sum())\n",
+    "print(df7.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier on rescaled features\n",
+    "\n",
+    "Assumes that all features are independent of each other and each feature contributes equally to the resulting class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model8 = GaussianNB()\n",
+    "model8.fit(rescaledPredictors, target)\n",
+    "\n",
+    "results1 = cross_val_score(model8, rescaledPredictors, target)\n",
+    "print(results1.mean())\n",
+    "\n",
+    "model8_pred = model8.predict(rescaledNew)\n",
+    "df8 = pd.DataFrame(model8_pred)\n",
+    "df8.columns = ['CLASS']\n",
+    "df8.index.name = 'Index'\n",
+    "df8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df8.to_csv('ilujumba8.csv')\n",
+    "print(df8['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df8.groupby('CLASS').size()[0].sum())\n",
+    "print(df8.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes and kfold validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model9 = GaussianNB()\n",
+    "model9.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\n",
+    "print('mean for results', results.mean())\n",
+    "\n",
+    "predic = cross_val_predict(model9, predictors, target, cv =10)\n",
+    "accuracy = metrics.r2_score(target, predic)\n",
+    "print('cross-predicted accuracy ', accuracy)\n",
+    "\n",
+    "model9_pred = model9.predict(newArray)\n",
+    "df9 = pd.DataFrame(model9_pred)\n",
+    "df9.columns = ['CLASS']\n",
+    "df9.index.name = 'Index'\n",
+    "df9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df9.to_csv('ilujumba9.csv')\n",
+    "print(df9['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df9.groupby('CLASS').size()[0].sum())\n",
+    "print(df9.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing several algorithms to look at the nature of the decision boundaries created"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://medium.com/cascade-bio-blog/creating-visualizations-to-better-understand-your-data-and-models-part-2-28d5c46e956"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Algorithms define a st of hyperplanes that divide the datapoints to their respective classes and span the feature space trained on. Visualising enables one to understand the limitations of a given algorithm on a dataset given to it.\n",
+    "Thus decision boundaries enable one to understand to how the training data selected affects performance of the algorithm."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ten sklearn classifier algorithms were compared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing classifiers from the sklearn library\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPClassifier #1\n",
+    "from sklearn.neighbors import KNeighborsClassifier #2\n",
+    "from sklearn.svm import SVC #3\n",
+    "from sklearn.gaussian_process import GaussianProcessClassifier #4\n",
+    "from sklearn.gaussian_process.kernels import RBF #5\n",
+    "from sklearn.tree import DecisionTreeClassifier #6\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\n",
+    "from sklearn.naive_bayes import GaussianNB #9\n",
+    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\n",
+    "from sklearn.linear_model import LogisticRegression #11\n",
+    "\n",
+    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
+    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
+    "         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n",
+    "\n",
+    "classifiers = [\n",
+    "    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n",
+    "    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n",
+    "    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n",
+    "    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n",
+    "    DecisionTreeClassifier(),\n",
+    "    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n",
+    "    MLPClassifier(max_iter=1000), #iterations until converge\n",
+    "    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n",
+    "    GaussianNB(),\n",
+    "    QuadraticDiscriminantAnalysis(),\n",
+    "    LogisticRegression()]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dimensionality reduction\n",
+    "https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the data is multi-dimensional, it was reduced using Principal Component Analysis (PCA) to reduce it to two components.\n",
+    "Trial runs were done to check how much of the variation in the data is explained by the principal components.\n",
+    "\n",
+    "Another thing to keep in mind is that PCA works best on standardised/normalised data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preprocessing the dataset\n",
+    "dataArray = data.to_numpy()\n",
+    "X, y = dataArray[:,0:11], dataArray[0:,11]\n",
+    "X = StandardScaler().fit_transform(X)\n",
+    "\n",
+    "# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA()\n",
+    "pca.fit_transform(X)\n",
+    "pca_variance = pca.explained_variance_\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Variance ratio')\n",
+    "plt.xlabel('Principal components')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\n",
+    "ninety_five = pca2.fit_transform(X)\n",
+    "ninety_five.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eight features explain 95% of the variance in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca2 = PCA(3) # keeping features three principal components\n",
+    "principalComponents = pca2.fit_transform(X)\n",
+    "\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "plt.figure(figsize=(10,6))\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n",
+    "           linewidths=1, alpha=.5,\n",
+    "           edgecolor='k', s= 200,\n",
+    "           c=data['CLASS'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The three pincipal components wete visualised using a 3D plot. The figure above shows clustering of the three components. Each component is not exactly independent of the others so the clusters overlap to some extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#converting principal component ndarrays to DataFrame format\n",
+    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\n",
+    "finalDf = pd.concat([principalDf, data['CLASS']], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finalDf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualising the top 2 principal components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize = (6,6))\n",
+    "ax = fig.add_subplot(111) \n",
+    "ax.set(xlim=(-10,10), ylim=(-10,10))\n",
+    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
+    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
+    "ax.set_title('top 2 components', fontsize = 20)\n",
+    "\n",
+    "targets = [0, 1]\n",
+    "colors = ['r', 'g']\n",
+    "\n",
+    "for target, color in zip(targets,colors):\n",
+    "    indices = finalDf['CLASS'] == target\n",
+    "    ax.scatter(finalDf.loc[indices, 'PC1']\n",
+    "               , finalDf.loc[indices, 'PC2']\n",
+    "               , c = color\n",
+    "               , s = 50)\n",
+    "ax.legend(targets)\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting the into training and test part\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A mesh grid is required. This can be thought of as a matrix of coordinates upon which the model will make decisions.\n",
+    "These are then visualised to reveal decision boundaries.\n",
+    "The mesh grip was created based on the data and a step size of 0.02"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating mesh for the contour plot\n",
+    "\n",
+    "h = .02  # step size in the mesh\n",
+    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
+    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two principal components were used to enable visualisation on a scatter plot.\n",
+    "\n",
+    "The parameters for the PCA were generated on the training data and these were applied on both the training and training sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca4 = PCA(n_components=2)\n",
+    "\n",
+    "# applying PCA on training set\n",
+    "pca4.fit(X_train)\n",
+    "\n",
+    "#applying transform on training and testing sets\n",
+    "train_ = pca4.transform(X_train)\n",
+    "test_ = pca4.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_.shape, test_.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After transforming the data and the creating the meshgrid, decision boundaries for the algorithms were created by iterating over the classifiers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "figure = plt.figure(figsize=(27, 15))\n",
+    "i = 1\n",
+    "\n",
+    "datasets=[data]\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
+    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "        # Plot the top 2 principal components for training data\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n",
+    "                    edgecolors='k')\n",
+    "        # Plot the top 2 principal components for the testing data\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
+    "                    edgecolors='k')\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        i += 1\n",
+    "\n",
+    "        # iterate over classifiers\n",
+    "\n",
+    "    for name, clf in zip(names, classifiers):\n",
+    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "        clf.fit(train_, y_train)\n",
+    "        score = clf.score(test_, y_test)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n",
+    "        # Plot the testing points\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.4)\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(name)\n",
+    "            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n",
+    "            i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Accuracies of the different algorithms are indicated on the lower right corner.\n",
+    "\n",
+    "The plots show training points in solid colors and testing points semi-transparent. Contour decision boundaries were used which seperate points based on shared characteritics.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Assignment Colab/Ibra Lujumba_kaggle.ipynb b/Assignment Colab/Ibra Lujumba_kaggle.ipynb
index 600d184..89f6c90 100644
--- a/Assignment Colab/Ibra Lujumba_kaggle.ipynb	
+++ b/Assignment Colab/Ibra Lujumba_kaggle.ipynb	
@@ -1 +1,2159 @@
-{"cells":[{"metadata":{},"cell_type":"markdown","source":"## ACE_Uganda Kaggle competition 1\n### by Ibra Lujumba"},{"metadata":{},"cell_type":"markdown","source":"#### Exploratory Data Analysis "},{"metadata":{},"cell_type":"markdown","source":"This is done to try to understand the properties of the data before any machine learning algorithm id used to make predictions about the data."},{"metadata":{},"cell_type":"markdown","source":"##### Importing python modules for data analysis and visualization"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np # manipulation of arrays\nimport pandas as pd # manipulating dataframes\nimport matplotlib.pyplot as plt # data visualisation\nimport seaborn as sb # data visualisation,it is based on plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#ignoring warnings that may arise\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"###### Importing the datasets"},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input/ace-class-assignment/\n\n# reading in the data\ndata = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\nnew = pd.read_csv('../input/ace-class-assignment/Test.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"##### Checking the dimensions of the data as well as the datatype of each column"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking dimensions of the datasets\ndata.shape, new.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the datatypes of the variables\ndata.dtypes, new.dtypes","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"All the values in all the variables exists as either floats or integers.\n\nProceeding to work with the training dataset to build the classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the descriptive statistics of the train dataset such as arithmetic mean, \n# standard deviation, quartiles and number of non-NA values in each column \ndata.describe()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"raw","source":"From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\nAS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\nFor all the variables, the data points are not widely spread"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the proprotions of classses\ndata.groupby('CLASS').size()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"raw","source":"Both classes have an equal number of entries"},{"metadata":{"trusted":true},"cell_type":"code","source":"# obtaining pairwise correlation values for the variables in the train dataset\n# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\npearsoncorr = data.corr(method='pearson')\n\n# visualizing the correlation matrix as a heatmap to make interpretation easier\nplt.figure(figsize=(10,10))\ntop_corr = pearsoncorr.index\nsb.heatmap(pearsoncorr, \n            xticklabels=pearsoncorr.columns,\n            yticklabels=pearsoncorr.columns,\n            cmap='RdYlGn',\n            annot=True,\n            linewidth=0.5)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."},{"metadata":{},"cell_type":"markdown","source":"You can get the p-values associated with the correlation values using the code below.\n\n`from scipy.stats import pearsonr`\n\n`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n\nIn this example, all values were too low to be informative"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using a scatter plot matrix to visualise correlations\n# plt.figure(figsize=(60,60))\n# sb.pairplot(data)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\nThese variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n\nVariables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"},{"metadata":{"trusted":true},"cell_type":"code","source":"len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n\n#this confirms that NT_EFC195 is a categorical variable","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the associated p-values. The value of 1 at the bottom should be ignored \nfrom scipy.stats import pearsonr\ndata.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the distribution and skewness of variables\nplt.figure(figsize=(10,6))\ndata.skew().plot(kind='bar')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Most of the variables are minimally skewed except NT_EFC195. Further checks will be done to try to understand the properties of this variable.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"data.groupby('NT_EFC195').size() # majority of the instances are of Class 0.","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The skewedness in this variable can be understood by having most of its values at zeros\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n\nTranformation possibilities\n* using the minimum and maximum scaler\n* standardisation"},{"metadata":{},"cell_type":"markdown","source":"#### Data transformation"},{"metadata":{},"cell_type":"markdown","source":"Better performance of algorithms can be obtained if the data is transformed.\nSome algorithms are may take features with large values as the most important features in the predictions"},{"metadata":{},"cell_type":"markdown","source":"Seperating the predictor variables from the target variable"},{"metadata":{"trusted":true},"cell_type":"code","source":"# converting Pandas dataframe to ndArray\ndataArray = data.to_numpy()\n\n# seperating the predictor and response variables\ntarget = dataArray[:,11]\npredictors = dataArray[:,0:11]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using minMaxScaler to set all values between 0 and 1\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler(feature_range=(0,1))\nrescaledPredictors = scaler.fit_transform(predictors)\n                        \n\n# using StandardScaler\nfrom sklearn.preprocessing import StandardScaler\nscaler1 = StandardScaler().fit(predictors)\nstandardizedPredictors = scaler1.transform(predictors)\n                        \n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Feature selection"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n\n# untransformed data\nfrom sklearn.feature_selection import SelectKBest, f_classif\nbestFeatures = SelectKBest(score_func=f_classif, k=7)\nfit = bestFeatures.fit(predictors, target)\n\nscores = pd.DataFrame(fit.scores_) \npvalues = pd.DataFrame(fit.pvalues_)\ncolumns = pd.DataFrame(data.columns[0:11])\n\nfeatureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\nfeatureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n\nprint(featureValues.nlargest(7, 'score'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the transformed data\n\n# rescaledPredictors\nreBestFeatures = SelectKBest(score_func=f_classif, k=7)\nreFit = reBestFeatures.fit(rescaledPredictors, target)\n\nreScores = pd.DataFrame(reFit.scores_) \nrePvalues = pd.DataFrame(reFit.pvalues_)\nreColumns = pd.DataFrame(data.columns[0:11])\n\nreFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\nreFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n\n\n\n# standardizedPredictors\nstBestFeatures = SelectKBest(score_func=f_classif, k=7)\nstFit = stBestFeatures.fit(standardizedPredictors, target)\n\nstScores = pd.DataFrame(stFit.scores_) \nstPvalues = pd.DataFrame(stFit.pvalues_)\nstColumns = pd.DataFrame(data.columns[0:11])\n\nstFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\nstFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n\nprint(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using feature importance\nfrom sklearn.ensemble import ExtraTreesClassifier\nmodel = ExtraTreesClassifier()\nmodel.fit(predictors, target)\nprint(model.feature_importances_)\n\n# visualising feature importance\nimportances = pd.Series(model.feature_importances_, index=data.columns[0:11])\nimportances.nlargest(10).plot(kind='barh')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"#### Building the classification model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\nfrom sklearn.model_selection import train_test_split # random split\nfrom sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\np_train, p_test, t_train, t_test = train_test_split(predictors, target, \n                                                    test_size=0.30,random_state=42)\n\nlogit = LogisticRegression() # making instance of model\n\n# fitting the model on untransformed data\nlogit.fit(p_train, t_train)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Measuring model performance\n\nWe can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# predict on test data\npredictions = logit.predict(p_test) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# the confusion matrix\nfrom sklearn import metrics\ncm = metrics.confusion_matrix(t_test, predictions)\ncm\nsb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n          square=True, cmap='Blues') \nplt.ylabel('Actual label'); plt.xlabel('Predicted label')\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\nprint(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\nprint(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n                                                      # higher values are better\n# not affected by unbalanced classes\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ROC curve of true positive rate against false positive rate\n# shows tradeoff between sensitivy and specificity\n\npred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\nfpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\nauc = metrics.roc_auc_score(t_test, pred_probs)\nplt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\nplt.legend(loc=4)\nplt.ylabel('tpr'), plt.xlabel('fpr')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data\nnew_pred = logit.predict(new.values)\n\npred_df = pd.DataFrame(new_pred) \npred_df.columns=[\"CLASS\"]\npred_df.index.name=\"Index\" \npred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df.to_csv(\"ilujumba.csv\") \nprint(pred_df['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df.groupby('CLASS').size()[0].sum())\nprint(pred_df.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on rescaled data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit1 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit1.fit(p1_train, t1_train)\n\n# predict on test data\npredictions1 = logit1.predict(p1_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\nprint(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\nprint(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t1_test, predictions1))\n\n# rescaling new data\nnewArray = new.to_numpy()\nrescaledNew = scaler.fit_transform(newArray)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data (rescaled)\nnew_pred1 = logit1.predict(rescaledNew)\n\npred_df1 = pd.DataFrame(new_pred1) \npred_df1.columns=[\"CLASS\"]\npred_df1.index.name=\"Index\" \npred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df1.to_csv(\"ilujumba1.csv\") \nprint(pred_df1['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df1.groupby('CLASS').size()[0].sum())\nprint(pred_df1.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on standardized data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit2 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit2.fit(p2_train, t2_train)\n\n# predict on test data\npredictions2 = logit2.predict(p2_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\nprint(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\nprint(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\nprint('MCC: ',matthews_corrcoef(t2_test, predictions2))\n\n# standardizing new data\nstandardizedNew = scaler1.transform(newArray)\n\n# performance on new data (standaridized)\nnew_pred2 = logit2.predict(standardizedNew)\n\npred_df2 = pd.DataFrame(new_pred2) \npred_df2.columns=[\"CLASS\"]\npred_df2.index.name=\"Index\" \npred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df2.to_csv(\"ilujumba2.csv\") \nprint(pred_df2['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df2.groupby('CLASS').size()[0].sum())\nprint(pred_df2.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Using selected features, rescaled data and Logistic regression\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n                                                        test_size=0.30,random_state=42)\n\nlogit3 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit3.fit(p3_train, t3_train)\n\n# predict on test data\npredictions3 = logit3.predict(p3_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\nprint(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\nprint(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t3_test, predictions3))\n\n# rescaling new data\n# newArray = new.to_numpy()\n# rescaledNew = scaler.fit_transform(newArray)\n\n# performance on new data (rescaled)\nnew_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n\npred_df3 = pd.DataFrame(new_pred3) \npred_df3.columns=[\"CLASS\"]\npred_df3.index.name=\"Index\" \npred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df3.to_csv(\"ilujumba3.csv\") \nprint(pred_df3['CLASS'].unique())\n\n\n#printing the numbers of False and True\nprint(pred_df3.groupby('CLASS').size()[0].sum())\nprint(pred_df3.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Using cross-validation and Logistic Regression"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\n\nkfold = KFold(n_splits=10, random_state=42)\nmodel6 = LogisticRegression()\nmodel6.fit(predictors, target)\n\nresults = cross_val_score(model6, predictors, target)\nprint(results.mean())\n\nmodel6_pred = model6.predict(rescaledNew)\ndf6 = pd.DataFrame(model6_pred)\ndf6.columns = ['CLASS']\ndf6.index.name = 'Index'\ndf6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n\ndf6.to_csv('ilujumba7.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier with kfold cross-validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel7 = GaussianNB()\nmodel7.fit(predictors, target)\n\nresults = cross_val_score(model7, predictors, target)\nprint(results.mean())\n\nmodel7_pred = model7.predict(newArray)\ndf7 = pd.DataFrame(model7_pred)\ndf7.columns = ['CLASS']\ndf7.index.name = 'Index'\ndf7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf7.to_csv('ilujumba7.csv')\nprint(df7['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df7.groupby('CLASS').size()[0].sum())\nprint(df7.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier on rescaled features\n\nAssumes that all features are independent of each other and each feature contributes equally to the resulting class"},{"metadata":{"trusted":true},"cell_type":"code","source":"kfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel8 = GaussianNB()\nmodel8.fit(rescaledPredictors, target)\n\nresults1 = cross_val_score(model8, rescaledPredictors, target)\nprint(results1.mean())\n\nmodel8_pred = model8.predict(rescaledNew)\ndf8 = pd.DataFrame(model8_pred)\ndf8.columns = ['CLASS']\ndf8.index.name = 'Index'\ndf8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf8.to_csv('ilujumba8.csv')\nprint(df8['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df8.groupby('CLASS').size()[0].sum())\nprint(df8.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes and kfold validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import cross_val_predict\nfrom sklearn.naive_bayes import GaussianNB\n\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel9 = GaussianNB()\nmodel9.fit(predictors, target)\n\nresults = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\nprint('mean for results', results.mean())\n\npredic = cross_val_predict(model9, predictors, target, cv =10)\naccuracy = metrics.r2_score(target, predic)\nprint('cross-predicted accuracy ', accuracy)\n\nmodel9_pred = model9.predict(newArray)\ndf9 = pd.DataFrame(model9_pred)\ndf9.columns = ['CLASS']\ndf9.index.name = 'Index'\ndf9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf9.to_csv('ilujumba9.csv')\nprint(df9['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df9.groupby('CLASS').size()[0].sum())\nprint(df9.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Comparing several algorithms to look at the nature of the decision boundaries created"},{"metadata":{},"cell_type":"markdown","source":"https://medium.com/cascade-bio-blog/creating-visualizations-to-better-understand-your-data-and-models-part-2-28d5c46e956"},{"metadata":{},"cell_type":"markdown","source":"Algorithms define a st of hyperplanes that divide the datapoints to their respective classes and span the feature space trained on. Visualising enables one to understand the limitations of a given algorithm on a dataset given to it.\nThus decision boundaries enable one to understand to how the training data selected affects performance of the algorithm."},{"metadata":{},"cell_type":"markdown","source":"Ten sklearn classifier algorithms were compared"},{"metadata":{"trusted":true},"cell_type":"code","source":"#importing classifiers from the sklearn library\n\nfrom matplotlib.colors import ListedColormap\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neural_network import MLPClassifier #1\nfrom sklearn.neighbors import KNeighborsClassifier #2\nfrom sklearn.svm import SVC #3\nfrom sklearn.gaussian_process import GaussianProcessClassifier #4\nfrom sklearn.gaussian_process.kernels import RBF #5\nfrom sklearn.tree import DecisionTreeClassifier #6\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\nfrom sklearn.naive_bayes import GaussianNB #9\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\nfrom sklearn.linear_model import LogisticRegression #11\n\nnames = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n\nclassifiers = [\n    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n    DecisionTreeClassifier(),\n    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n    MLPClassifier(max_iter=1000), #iterations until converge\n    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n    GaussianNB(),\n    QuadraticDiscriminantAnalysis(),\n    LogisticRegression()]\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Dimensionality reduction\nhttps://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"},{"metadata":{},"cell_type":"markdown","source":"Since the data is multi-dimensional, it was reduced using Principal Component Analysis (PCA) to reduce it to two components.\nTrial runs were done to check how much of the variation in the data is explained by the principal components.\n\nAnother thing to keep in mind is that PCA works best on standardised/normalised data"},{"metadata":{"trusted":true},"cell_type":"code","source":"# preprocessing the dataset\ndataArray = data.to_numpy()\nX, y = dataArray[:,0:11], dataArray[0:,11]\nX = StandardScaler().fit_transform(X)\n\n# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\nfrom sklearn.decomposition import PCA\npca = PCA()\npca.fit_transform(X)\npca_variance = pca.explained_variance_\nplt.figure(figsize=(8, 6))\nplt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\nplt.legend()\nplt.ylabel('Variance ratio')\nplt.xlabel('Principal components')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\nninety_five = pca2.fit_transform(X)\nninety_five.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Eight features explain 95% of the variance in the dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"pca2 = PCA(3) # keeping features three principal components\nprincipalComponents = pca2.fit_transform(X)\n\nfrom mpl_toolkits.mplot3d import Axes3D\nplt.figure(figsize=(10,6))\nax = plt.axes(projection='3d')\nax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n           linewidths=1, alpha=.5,\n           edgecolor='k', s= 200,\n           c=data['CLASS'])\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The three pincipal components wete visualised using a 3D plot. The figure above shows clustering of the three components. Each component is not exactly independent of the others so the clusters overlap to some extent"},{"metadata":{"trusted":true},"cell_type":"code","source":"#converting principal component ndarrays to DataFrame format\nprincipalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\nfinalDf = pd.concat([principalDf, data['CLASS']], axis = 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"finalDf.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Visualising the top 2 principal components"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = plt.figure(figsize = (6,6))\nax = fig.add_subplot(111) \nax.set(xlim=(-10,10), ylim=(-10,10))\nax.set_xlabel('Principal Component 1', fontsize = 15)\nax.set_ylabel('Principal Component 2', fontsize = 15)\nax.set_title('top 2 components', fontsize = 20)\n\ntargets = [0, 1]\ncolors = ['r', 'g']\n\nfor target, color in zip(targets,colors):\n    indices = finalDf['CLASS'] == target\n    ax.scatter(finalDf.loc[indices, 'PC1']\n               , finalDf.loc[indices, 'PC2']\n               , c = color\n               , s = 50)\nax.legend(targets)\nax.grid()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# splitting the into training and test part\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"A mesh grid is required. This can be thought of as a matrix of coordinates upon which the model will make decisions.\nThese are then visualised to reveal decision boundaries.\nThe mesh grip was created based on the data and a step size of 0.02"},{"metadata":{"trusted":true},"cell_type":"code","source":"# creating mesh for the contour plot\n\nh = .02  # step size in the mesh\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Two principal components were used to enable visualisation on a scatter plot.\n\nThe parameters for the PCA were generated on the training data and these were applied on both the training and training sets"},{"metadata":{"trusted":true},"cell_type":"code","source":"pca4 = PCA(n_components=2)\n\n# applying PCA on training set\npca4.fit(X_train)\n\n#applying transform on training and testing sets\ntrain_ = pca4.transform(X_train)\ntest_ = pca4.transform(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_.shape, test_.shape","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"After transforming the data and the creating the meshgrid, decision boundaries for the algorithms were created by iterating over the classifiers."},{"metadata":{"trusted":true},"cell_type":"code","source":"figure = plt.figure(figsize=(27, 15))\ni = 1\n\ndatasets=[data]\nfor ds_cnt, ds in enumerate(datasets):\n    # just plot the dataset first\n    cm = plt.cm.RdBu\n    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n\n    if ds_cnt == 0:\n        ax.set_title(\"Input data\")\n        # Plot the top 2 principal components for training data\n        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n                    edgecolors='k')\n        # Plot the top 2 principal components for the testing data\n        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n                    edgecolors='k')\n        ax.set_xlim(xx.min(), xx.max())\n        ax.set_ylim(yy.min(), yy.max())\n        ax.set_xticks(())\n        ax.set_yticks(())\n        i += 1\n\n        # iterate over classifiers\n\n    for name, clf in zip(names, classifiers):\n        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n        clf.fit(train_, y_train)\n        score = clf.score(test_, y_test)\n\n        # Plot the decision boundary. For that, we will assign a color to each\n        # point in the mesh [x_min, x_max]x[y_min, y_max].\n\n        if hasattr(clf, \"decision_function\"):\n            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n        else:\n            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n\n        # Put the result into a color plot\n        Z = Z.reshape(xx.shape)\n        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n\n        # Plot the training points\n        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n        # Plot the testing points\n        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.4)\n\n        ax.set_xlim(xx.min(), xx.max())\n        ax.set_ylim(yy.min(), yy.max())\n        ax.set_xticks(())\n        ax.set_yticks(())\n        if ds_cnt == 0:\n            ax.set_title(name)\n            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n            i += 1\n\nplt.tight_layout()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Accuracies of the different algorithms are indicated on the lower right corner.\n\nThe plots show training points in solid colors and testing points semi-transparent. Contour decision boundaries were used which seperate points based on shared characteritics.\n"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ACE_Uganda Kaggle competition 1\n",
+    "### by Ibra Lujumba"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Exploratory Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is done to try to understand the properties of the data before any machine learning algorithm id used to make predictions about the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Importing python modules for data analysis and visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # manipulation of arrays\n",
+    "import pandas as pd # manipulating dataframes\n",
+    "import matplotlib.pyplot as plt # data visualisation\n",
+    "import seaborn as sb # data visualisation,it is based on plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ignoring warnings that may arise\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###### Importing the datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ls: ../input/ace-class-assignment/: No such file or directory\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls ../input/ace-class-assignment/\n",
+    "\n",
+    "# reading in the data\n",
+    "data = pd.read_csv('../AMP Data Sets/AMP_TrainSet.csv')\n",
+    "new = pd.read_csv('../AMP Data Sets/Test.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Checking the dimensions of the data as well as the datatype of each column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((3038, 12), (758, 11))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking dimensions of the datasets\n",
+    "data.shape, new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(FULL_Charge           float64\n",
+       " FULL_AcidicMolPerc    float64\n",
+       " FULL_AURR980107       float64\n",
+       " FULL_DAYM780201       float64\n",
+       " FULL_GEOR030101       float64\n",
+       " FULL_OOBM850104       float64\n",
+       " NT_EFC195               int64\n",
+       " AS_MeanAmphiMoment    float64\n",
+       " AS_DAYM780201         float64\n",
+       " AS_FUKS010112         float64\n",
+       " CT_RACS820104         float64\n",
+       " CLASS                   int64\n",
+       " dtype: object, FULL_Charge           float64\n",
+       " FULL_AcidicMolPerc    float64\n",
+       " FULL_AURR980107       float64\n",
+       " FULL_DAYM780201       float64\n",
+       " FULL_GEOR030101       float64\n",
+       " FULL_OOBM850104       float64\n",
+       " NT_EFC195               int64\n",
+       " AS_MeanAmphiMoment    float64\n",
+       " AS_DAYM780201         float64\n",
+       " AS_FUKS010112         float64\n",
+       " CT_RACS820104         float64\n",
+       " dtype: object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the datatypes of the variables\n",
+    "data.dtypes, new.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the values in all the variables exists as either floats or integers.\n",
+    "\n",
+    "Proceeding to work with the training dataset to build the classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FULL_Charge</th>\n",
+       "      <th>FULL_AcidicMolPerc</th>\n",
+       "      <th>FULL_AURR980107</th>\n",
+       "      <th>FULL_DAYM780201</th>\n",
+       "      <th>FULL_GEOR030101</th>\n",
+       "      <th>FULL_OOBM850104</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "      <th>AS_MeanAmphiMoment</th>\n",
+       "      <th>AS_DAYM780201</th>\n",
+       "      <th>AS_FUKS010112</th>\n",
+       "      <th>CT_RACS820104</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "      <td>3038.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.060237</td>\n",
+       "      <td>8.521520</td>\n",
+       "      <td>0.971410</td>\n",
+       "      <td>73.668760</td>\n",
+       "      <td>0.994007</td>\n",
+       "      <td>-2.432927</td>\n",
+       "      <td>0.088545</td>\n",
+       "      <td>15.683233</td>\n",
+       "      <td>73.650828</td>\n",
+       "      <td>5.911361</td>\n",
+       "      <td>1.235255</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.819929</td>\n",
+       "      <td>7.586652</td>\n",
+       "      <td>0.107413</td>\n",
+       "      <td>8.527489</td>\n",
+       "      <td>0.031333</td>\n",
+       "      <td>1.707223</td>\n",
+       "      <td>0.284133</td>\n",
+       "      <td>11.575665</td>\n",
+       "      <td>9.166092</td>\n",
+       "      <td>0.693689</td>\n",
+       "      <td>0.210012</td>\n",
+       "      <td>0.500082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-16.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.684000</td>\n",
+       "      <td>42.750000</td>\n",
+       "      <td>0.866000</td>\n",
+       "      <td>-10.432000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.041000</td>\n",
+       "      <td>42.778000</td>\n",
+       "      <td>3.533000</td>\n",
+       "      <td>0.785000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.516000</td>\n",
+       "      <td>0.895000</td>\n",
+       "      <td>68.294000</td>\n",
+       "      <td>0.974000</td>\n",
+       "      <td>-3.606000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.587500</td>\n",
+       "      <td>67.556000</td>\n",
+       "      <td>5.459250</td>\n",
+       "      <td>1.082000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.143000</td>\n",
+       "      <td>0.963000</td>\n",
+       "      <td>74.059500</td>\n",
+       "      <td>0.994000</td>\n",
+       "      <td>-2.296500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.988500</td>\n",
+       "      <td>73.697000</td>\n",
+       "      <td>5.925500</td>\n",
+       "      <td>1.184000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>13.158000</td>\n",
+       "      <td>1.041000</td>\n",
+       "      <td>79.343750</td>\n",
+       "      <td>1.011000</td>\n",
+       "      <td>-1.283250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>26.807750</td>\n",
+       "      <td>79.778000</td>\n",
+       "      <td>6.382000</td>\n",
+       "      <td>1.351000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>46.667000</td>\n",
+       "      <td>1.451000</td>\n",
+       "      <td>101.682000</td>\n",
+       "      <td>1.196000</td>\n",
+       "      <td>3.576000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>51.280000</td>\n",
+       "      <td>103.167000</td>\n",
+       "      <td>8.662000</td>\n",
+       "      <td>2.192000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
+       "count  3038.000000         3038.000000      3038.000000      3038.000000   \n",
+       "mean      2.060237            8.521520         0.971410        73.668760   \n",
+       "std       3.819929            7.586652         0.107413         8.527489   \n",
+       "min     -16.000000            0.000000         0.684000        42.750000   \n",
+       "25%       0.000000            2.516000         0.895000        68.294000   \n",
+       "50%       2.000000            7.143000         0.963000        74.059500   \n",
+       "75%       4.000000           13.158000         1.041000        79.343750   \n",
+       "max      30.000000           46.667000         1.451000       101.682000   \n",
+       "\n",
+       "       FULL_GEOR030101  FULL_OOBM850104    NT_EFC195  AS_MeanAmphiMoment  \\\n",
+       "count      3038.000000      3038.000000  3038.000000         3038.000000   \n",
+       "mean          0.994007        -2.432927     0.088545           15.683233   \n",
+       "std           0.031333         1.707223     0.284133           11.575665   \n",
+       "min           0.866000       -10.432000     0.000000            0.041000   \n",
+       "25%           0.974000        -3.606000     0.000000            5.587500   \n",
+       "50%           0.994000        -2.296500     0.000000           14.988500   \n",
+       "75%           1.011000        -1.283250     0.000000           26.807750   \n",
+       "max           1.196000         3.576000     1.000000           51.280000   \n",
+       "\n",
+       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104        CLASS  \n",
+       "count    3038.000000    3038.000000    3038.000000  3038.000000  \n",
+       "mean       73.650828       5.911361       1.235255     0.500000  \n",
+       "std         9.166092       0.693689       0.210012     0.500082  \n",
+       "min        42.778000       3.533000       0.785000     0.000000  \n",
+       "25%        67.556000       5.459250       1.082000     0.000000  \n",
+       "50%        73.697000       5.925500       1.184000     0.500000  \n",
+       "75%        79.778000       6.382000       1.351000     1.000000  \n",
+       "max       103.167000       8.662000       2.192000     1.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# getting the descriptive statistics of the train dataset such as arithmetic mean, \n",
+    "# standard deviation, quartiles and number of non-NA values in each column \n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\n",
+    "AS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\n",
+    "For all the variables, the data points are not widely spread"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CLASS\n",
+       "0    1519\n",
+       "1    1519\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the proprotions of classses\n",
+    "data.groupby('CLASS').size()"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Both classes have an equal number of entries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1182df550>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# obtaining pairwise correlation values for the variables in the train dataset\n",
+    "# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\n",
+    "pearsoncorr = data.corr(method='pearson')\n",
+    "\n",
+    "# visualizing the correlation matrix as a heatmap to make interpretation easier\n",
+    "plt.figure(figsize=(10,10))\n",
+    "top_corr = pearsoncorr.index\n",
+    "sb.heatmap(pearsoncorr, \n",
+    "            xticklabels=pearsoncorr.columns,\n",
+    "            yticklabels=pearsoncorr.columns,\n",
+    "            cmap='RdYlGn',\n",
+    "            annot=True,\n",
+    "            linewidth=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the p-values associated with the correlation values using the code below.\n",
+    "\n",
+    "`from scipy.stats import pearsonr`\n",
+    "\n",
+    "`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n",
+    "\n",
+    "In this example, all values were too low to be informative\n",
+    "\n",
+    "<div class = 'alert alert-info'>\n",
+    " Why do we need to know about he p-values?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using a scatter plot matrix to visualise correlations\n",
+    "# plt.figure(figsize=(60,60))\n",
+    "# sb.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\n",
+    "These variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n",
+    "\n",
+    "Variables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2830, array([0, 1]))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n",
+    "\n",
+    "#this confirms that NT_EFC195 is a categorical variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CLASS</th>\n",
+       "      <th>NT_EFC195</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   CLASS  NT_EFC195\n",
+       "0      1          0\n",
+       "1      1          1\n",
+       "2      1          0\n",
+       "3      1          0\n",
+       "4      1          0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FULL_Charge           3.404241e-224\n",
+       "FULL_AcidicMolPerc    4.220004e-295\n",
+       "FULL_AURR980107       1.887905e-277\n",
+       "FULL_DAYM780201       6.997934e-245\n",
+       "FULL_GEOR030101        2.669597e-48\n",
+       "FULL_OOBM850104       7.441087e-154\n",
+       "NT_EFC195              2.189203e-48\n",
+       "AS_MeanAmphiMoment     0.000000e+00\n",
+       "AS_DAYM780201         4.911002e-142\n",
+       "AS_FUKS010112          6.540694e-02\n",
+       "CT_RACS820104          5.329249e-51\n",
+       "CLASS                  1.000000e+00\n",
+       "Name: CLASS, dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# getting the associated p-values. The value of 1 at the bottom should be ignored \n",
+    "from scipy.stats import pearsonr\n",
+    "data.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x113287b90>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# checking the distribution and skewness of variables\n",
+    "plt.figure(figsize=(10,6))\n",
+    "data.skew().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of the variables are minimally skewed except NT_EFC195. Further checks will be done to try to understand the properties of this variable.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NT_EFC195\n",
+       "0    2769\n",
+       "1     269\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.groupby('NT_EFC195').size() # majority of the instances are of Class 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The skewedness in this variable can be understood by having most of its values at zeros\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10baf1f90>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10ba8f9d0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10bb1d2d0>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11870c6d0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118742d50>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118780f50>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1187b7a90>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1187f6150>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1187f6c90>],\n",
+       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1188394d0>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1188a5510>,\n",
+       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1188dbb90>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n",
+    "\n",
+    "Tranformation possibilities\n",
+    "* using the minimum and maximum scaler\n",
+    "* standardisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Better performance of algorithms can be obtained if the data is transformed.\n",
+    "Some algorithms are may take features with large values as the most important features in the predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Seperating the predictor variables from the target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# converting Pandas dataframe to ndArray\n",
+    "dataArray = data.to_numpy()\n",
+    "\n",
+    "# seperating the predictor and response variables\n",
+    "target = dataArray[:,11]\n",
+    "predictors = dataArray[:,0:11]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using minMaxScaler to set all values between 0 and 1\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "rescaledPredictors = scaler.fit_transform(predictors)\n",
+    "                        \n",
+    "\n",
+    "# using StandardScaler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler1 = StandardScaler().fit(predictors)\n",
+    "standardizedPredictors = scaler1.transform(predictors)\n",
+    "                        \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            predictor        score         pvalue\n",
+      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
+      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
+      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
+      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
+      "0         FULL_Charge  1214.902838  3.404241e-224\n",
+      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
+      "8       AS_DAYM780201   717.317408  4.911002e-142\n"
+     ]
+    }
+   ],
+   "source": [
+    "# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n",
+    "\n",
+    "# untransformed data\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif\n",
+    "bestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "fit = bestFeatures.fit(predictors, target)\n",
+    "\n",
+    "scores = pd.DataFrame(fit.scores_) \n",
+    "pvalues = pd.DataFrame(fit.pvalues_)\n",
+    "columns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "featureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\n",
+    "featureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n",
+    "\n",
+    "print(featureValues.nlargest(7, 'score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         re_predictor     re_score      re_pvalue\n",
+      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
+      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
+      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
+      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
+      "0         FULL_Charge  1214.902838  3.404241e-224\n",
+      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
+      "8       AS_DAYM780201   717.317408  4.911002e-142\n",
+      "         st_predictor     st_score      st_pvalue\n",
+      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
+      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
+      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
+      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
+      "0         FULL_Charge  1214.902838  3.404241e-224\n",
+      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
+      "8       AS_DAYM780201   717.317408  4.911002e-142\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(None, None)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the transformed data\n",
+    "\n",
+    "# rescaledPredictors\n",
+    "reBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "reFit = reBestFeatures.fit(rescaledPredictors, target)\n",
+    "\n",
+    "reScores = pd.DataFrame(reFit.scores_) \n",
+    "rePvalues = pd.DataFrame(reFit.pvalues_)\n",
+    "reColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "reFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\n",
+    "reFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "# standardizedPredictors\n",
+    "stBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "stFit = stBestFeatures.fit(standardizedPredictors, target)\n",
+    "\n",
+    "stScores = pd.DataFrame(stFit.scores_) \n",
+    "stPvalues = pd.DataFrame(stFit.pvalues_)\n",
+    "stColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "stFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\n",
+    "stFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n",
+    "\n",
+    "print(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.08556225 0.1431365  0.09581152 0.08379914 0.05094201 0.07798315\n",
+      " 0.03644825 0.28815319 0.05496085 0.03261207 0.05059108]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# using feature importance\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "model = ExtraTreesClassifier()\n",
+    "model.fit(predictors, target)\n",
+    "print(model.feature_importances_)\n",
+    "\n",
+    "# visualising feature importance\n",
+    "importances = pd.Series(model.feature_importances_, index=data.columns[0:11])\n",
+    "importances.nlargest(10).plot(kind='barh')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Building the classification model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
+       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
+       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
+       "                   warm_start=False)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\n",
+    "from sklearn.model_selection import train_test_split # random split\n",
+    "from sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\n",
+    "p_train, p_test, t_train, t_test = train_test_split(predictors, target, \n",
+    "                                                    test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on untransformed data\n",
+    "logit.fit(p_train, t_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Measuring model performance\n",
+    "\n",
+    "We can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict on test data\n",
+    "predictions = logit.predict(p_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 15.0, 'Predicted label')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the confusion matrix\n",
+    "from sklearn import metrics\n",
+    "cm = metrics.confusion_matrix(t_test, predictions)\n",
+    "cm\n",
+    "sb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n",
+    "          square=True, cmap='Blues') \n",
+    "plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.77631578947368\n",
+      "Precision:  92.82608695652173\n",
+      "Recall:  91.04477611940298\n",
+      "MCC:  0.8356480321235562\n"
+     ]
+    }
+   ],
+   "source": [
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n",
+    "                                                      # higher values are better\n",
+    "# not affected by unbalanced classes\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ROC curve of true positive rate against false positive rate\n",
+    "# shows tradeoff between sensitivy and specificity\n",
+    "\n",
+    "pred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\n",
+    "fpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\n",
+    "auc = metrics.roc_auc_score(t_test, pred_probs)\n",
+    "plt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('tpr'), plt.xlabel('fpr')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['False' 'True']\n",
+      "386\n",
+      "372\n"
+     ]
+    }
+   ],
+   "source": [
+    "# performance on new data\n",
+    "new_pred = logit.predict(new.values)\n",
+    "\n",
+    "pred_df = pd.DataFrame(new_pred) \n",
+    "pred_df.columns=[\"CLASS\"]\n",
+    "pred_df.index.name=\"Index\" \n",
+    "pred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df.to_csv(\"ilujumba.csv\") \n",
+    "print(pred_df['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on rescaled data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.66666666666666\n",
+      "Precision:  92.81045751633987\n",
+      "Recall:  90.8315565031983\n",
+      "MCC:  0.8335025900424667\n"
+     ]
+    }
+   ],
+   "source": [
+    "p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit1 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit1.fit(p1_train, t1_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions1 = logit1.predict(p1_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t1_test, predictions1))\n",
+    "\n",
+    "# rescaling new data\n",
+    "newArray = new.to_numpy()\n",
+    "rescaledNew = scaler.fit_transform(newArray)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['False' 'True']\n",
+      "378\n",
+      "380\n"
+     ]
+    }
+   ],
+   "source": [
+    "# performance on new data (rescaled)\n",
+    "new_pred1 = logit1.predict(rescaledNew)\n",
+    "\n",
+    "pred_df1 = pd.DataFrame(new_pred1) \n",
+    "pred_df1.columns=[\"CLASS\"]\n",
+    "pred_df1.index.name=\"Index\" \n",
+    "pred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df1.to_csv(\"ilujumba1.csv\") \n",
+    "print(pred_df1['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df1.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df1.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on standardized data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  91.8859649122807\n",
+      "Precision:  93.21663019693655\n",
+      "Recall:  90.8315565031983\n",
+      "MCC:  0.8379994044962448\n",
+      "['False' 'True']\n",
+      "388\n",
+      "370\n"
+     ]
+    }
+   ],
+   "source": [
+    "p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit2 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit2.fit(p2_train, t2_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions2 = logit2.predict(p2_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\n",
+    "print('MCC: ',matthews_corrcoef(t2_test, predictions2))\n",
+    "\n",
+    "# standardizing new data\n",
+    "standardizedNew = scaler1.transform(newArray)\n",
+    "\n",
+    "# performance on new data (standaridized)\n",
+    "new_pred2 = logit2.predict(standardizedNew)\n",
+    "\n",
+    "pred_df2 = pd.DataFrame(new_pred2) \n",
+    "pred_df2.columns=[\"CLASS\"]\n",
+    "pred_df2.index.name=\"Index\" \n",
+    "pred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df2.to_csv(\"ilujumba2.csv\") \n",
+    "print(pred_df2['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df2.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df2.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using selected features, rescaled data and Logistic regression\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  90.5701754385965\n",
+      "Precision:  91.36069114470843\n",
+      "Recall:  90.19189765458422\n",
+      "MCC:  0.8113912389992642\n",
+      "['False' 'True']\n",
+      "390\n",
+      "368\n"
+     ]
+    }
+   ],
+   "source": [
+    "p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit3 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit3.fit(p3_train, t3_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions3 = logit3.predict(p3_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t3_test, predictions3))\n",
+    "\n",
+    "# rescaling new data\n",
+    "# newArray = new.to_numpy()\n",
+    "# rescaledNew = scaler.fit_transform(newArray)\n",
+    "\n",
+    "# performance on new data (rescaled)\n",
+    "new_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n",
+    "\n",
+    "pred_df3 = pd.DataFrame(new_pred3) \n",
+    "pred_df3.columns=[\"CLASS\"]\n",
+    "pred_df3.index.name=\"Index\" \n",
+    "pred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df3.to_csv(\"ilujumba3.csv\") \n",
+    "print(pred_df3['CLASS'].unique())\n",
+    "\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df3.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df3.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using cross-validation and Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8393913118876268\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42)\n",
+    "model6 = LogisticRegression()\n",
+    "model6.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model6, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model6_pred = model6.predict(rescaledNew)\n",
+    "df6 = pd.DataFrame(model6_pred)\n",
+    "df6.columns = ['CLASS']\n",
+    "df6.index.name = 'Index'\n",
+    "df6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n",
+    "\n",
+    "df6.to_csv('ilujumba7.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier with kfold cross-validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.887773129281193\n",
+      "['True' 'False']\n",
+      "370\n",
+      "388\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model7 = GaussianNB()\n",
+    "model7.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model7, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model7_pred = model7.predict(newArray)\n",
+    "df7 = pd.DataFrame(model7_pred)\n",
+    "df7.columns = ['CLASS']\n",
+    "df7.index.name = 'Index'\n",
+    "df7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df7.to_csv('ilujumba7.csv')\n",
+    "print(df7['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df7.groupby('CLASS').size()[0].sum())\n",
+    "print(df7.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier on rescaled features\n",
+    "\n",
+    "Assumes that all features are independent of each other and each feature contributes equally to the resulting class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.887773129281193\n",
+      "['True' 'False']\n",
+      "347\n",
+      "411\n"
+     ]
+    }
+   ],
+   "source": [
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model8 = GaussianNB()\n",
+    "model8.fit(rescaledPredictors, target)\n",
+    "\n",
+    "results1 = cross_val_score(model8, rescaledPredictors, target)\n",
+    "print(results1.mean())\n",
+    "\n",
+    "model8_pred = model8.predict(rescaledNew)\n",
+    "df8 = pd.DataFrame(model8_pred)\n",
+    "df8.columns = ['CLASS']\n",
+    "df8.index.name = 'Index'\n",
+    "df8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df8.to_csv('ilujumba8.csv')\n",
+    "print(df8['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df8.groupby('CLASS').size()[0].sum())\n",
+    "print(df8.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes and kfold validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean for results 0.9101496004863645\n",
+      "cross-predicted accuracy  0.6405529953917051\n",
+      "['True' 'False']\n",
+      "370\n",
+      "388\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model9 = GaussianNB()\n",
+    "model9.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\n",
+    "print('mean for results', results.mean())\n",
+    "\n",
+    "predic = cross_val_predict(model9, predictors, target, cv =10)\n",
+    "accuracy = metrics.r2_score(target, predic)\n",
+    "print('cross-predicted accuracy ', accuracy)\n",
+    "\n",
+    "model9_pred = model9.predict(newArray)\n",
+    "df9 = pd.DataFrame(model9_pred)\n",
+    "df9.columns = ['CLASS']\n",
+    "df9.index.name = 'Index'\n",
+    "df9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df9.to_csv('ilujumba9.csv')\n",
+    "print(df9['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df9.groupby('CLASS').size()[0].sum())\n",
+    "print(df9.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing several algorithms to look at the nature of the decision boundaries created"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://medium.com/cascade-bio-blog/creating-visualizations-to-better-understand-your-data-and-models-part-2-28d5c46e956"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Algorithms define a st of hyperplanes that divide the datapoints to their respective classes and span the feature space trained on. Visualising enables one to understand the limitations of a given algorithm on a dataset given to it.\n",
+    "Thus decision boundaries enable one to understand to how the training data selected affects performance of the algorithm."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ten sklearn classifier algorithms were compared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing classifiers from the sklearn library\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPClassifier #1\n",
+    "from sklearn.neighbors import KNeighborsClassifier #2\n",
+    "from sklearn.svm import SVC #3\n",
+    "from sklearn.gaussian_process import GaussianProcessClassifier #4\n",
+    "from sklearn.gaussian_process.kernels import RBF #5\n",
+    "from sklearn.tree import DecisionTreeClassifier #6\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\n",
+    "from sklearn.naive_bayes import GaussianNB #9\n",
+    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\n",
+    "from sklearn.linear_model import LogisticRegression #11\n",
+    "\n",
+    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
+    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
+    "         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n",
+    "\n",
+    "classifiers = [\n",
+    "    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n",
+    "    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n",
+    "    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n",
+    "    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n",
+    "    DecisionTreeClassifier(),\n",
+    "    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n",
+    "    MLPClassifier(max_iter=1000), #iterations until converge\n",
+    "    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n",
+    "    GaussianNB(),\n",
+    "    QuadraticDiscriminantAnalysis(),\n",
+    "    LogisticRegression()]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dimensionality reduction\n",
+    "https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the data is multi-dimensional, it was reduced using Principal Component Analysis (PCA) to reduce it to two components.\n",
+    "Trial runs were done to check how much of the variation in the data is explained by the principal components.\n",
+    "\n",
+    "Another thing to keep in mind is that PCA works best on standardised/normalised data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6Vf1Ja1VJkqQJ9fMq0s8Cvw28kt4bzGbTO6UuSZIGqJ8Q37Wq/hp4oKrOAl4D7N9uWZIkaSL9hPj694b/MsmewEzg2e2VJEmS+tHPNfElSbYDPgAsBbYF/rrVqiRJ0oTGDfEkWwH3VdU9wHeA52+WqiRJ0oTGPZ1eVY8Cf7WZapEkSRuhn2viFyf5yyQ7JXnW+q/WK5MkSePq55r4Uc3344e1FZ5alyRpoCYM8aqa8I1lkiRp8+vndLokSZqEDHFJkjrKEJckqaMmDPH0vDHJic38zkn2a780SZI0nn6OxP8BOAA4ppn/FXBaaxVJkqS+9POI2f5VNT/JVQBVdU+S32q5LkmSNIG+XoCSZBq9Z8NJMgt4tNWqJEnShPoJ8VOBLwPPTvIh4N+A/9VqVZIkaUL9DPZydpLlwCuAAK+tqhtar0ySJI1rwhBPsgC4rqpOa+afkWT/qrqy9eokSdKY+jmd/o/A/cPm72/aJEnSAPUT4qmqWj/TvJ60n7vaJUlSi/oJ8ZuSvCPJ9ObrncBNbRcmSZLG10+Ivx14GXA7sBrYH1jcZlGSJGli/dydfidw9GaoRZIkbYR+7k6fBbwNmDO8f1X9SXtlSZKkifRzg9pXge8CFwOPtFuOJEnqVz8hvk1V/c+N3XCSnYDPAM+hN2Trkqr6xIg+AT4BvBr4NXBsVa3Y2M+SJGlL1M+NbRckefUmbHsd8O6qejGwADg+yYtH9HkVsFvztRifP5ckqW/9hPg76QX5g0nuS/KrJPdNtFJV3bH+qLqqfgXcAOw4otthwGeq5wrgmUmeu5E/gyRJW6R+7k5/+hP9kCRzgH2AkUO17gjcNmx+ddN2x4j1F9M81rbzzjs/0XIkSZoS+hp5Lcl29E55z1jfVlXf6XPdbYEvAX9eVRMewY+mqpYASwCGhoZqgu6SJG0R+nnE7K30TqnPBq6md337cuCgPtadTi/Az66q80fpcjuw07D52U2bJEmaQL/XxF8K3FpVv0/vtPgvJ1qpufP808ANVfXRMbotBf44PQuAe6vqjjH6SpKkYfo5nf5QVT2UhCRPqapVSV7Ux3oHAm8Crk1yddP2fmBngKo6HbiQ3uNlN9J7xOwtG/0TSJK0heonxFcneSbwFeCiJPcAt060UlX9G5AJ+hRwfD+FSpKkx+rn7vTDm8kPJrkUmAl8o9WqJEnShMYM8STPqKr7kjxrWPO1zfdtgbtbrUySJI1rvCPxzwOHAsvpDZuaEd+f33p1kiRpTGOGeFUd2txh/ntV9e+bsSZJktSHcR8xa248+3+bqRZJkrQR+nlOfEWSl7ZeiSRJ2ij9PGK2P/CGJLcCD9BcE6+qvVqtTJIkjaufEH9l61VIkqSN1s9z4rcCJHk2w16AIkmSBmvCa+JJFiX5KXAz8G3gFuDrLdclSZIm0M/p9L+l9+ayi6tqnyS/D7yx3bL0sYt+MpDPfdfBLxzI50qSNl4/d6c/XFVrga2SbFVVlwJDLdclSZIm0M+R+C+TbAt8Bzg7yZ307lKXJEkD1M+R+GHAg8C76L345GfAH7ZZlCRJmth4L0A5Dfh8VX1vWPNZ7ZckSZL6Md6R+E+AU5LckuTvk+yzuYqSJEkTGzPEq+oTVXUA8HvAWuCMJKuSnJTEW5glSRqwCa+JV9WtVfWRqtoHOAZ4LXBD65VJkqRx9TPYy9ZJ/jDJ2fQGefkx8LrWK5MkSeMa78a2g+kdeb8a+AHwRWBxVfl4mSRJk8B4z4m/D/g88O6qumcz1SNJkvo0ZohX1UGbsxBJkrRx+hnsRZIkTUKGuCRJHWWIS5LUUYa4JEkdZYhLktRRhrgkSR1liEuS1FGthXiSM5LcmeRHYyxfmOTeJFc3Xye2VYskSVPReCO2PVFnAp8EPjNOn+9W1aEt1iBJ0pTV2pF4VX0HuLut7UuStKUb9DXxA5Jck+TrSfYYcC2SJHVKm6fTJ7ICeF5V3Z/k1cBXgN1G65hkMbAYYOedd958FUqSNIkN7Ei8qu6rqvub6QuB6Ul2GKPvkqoaqqqhWbNmbdY6JUmarAYW4kl+O0ma6f2aWtYOqh5JkrqmtdPpSb4ALAR2SLIaOAmYDlBVpwNHAsclWQc8CBxdVdVWPZIkTTWthXhVHTPB8k/SewRNkiRtgkHfnS5JkjaRIS5JUkcZ4pIkdZQhLklSRxnikiR1lCEuSVJHGeKSJHWUIS5JUkcN8gUo6qCPXfSTgXzuuw5+4UA+V5ImM4/EJUnqKENckqSOMsQlSeooQ1ySpI4yxCVJ6ihDXJKkjjLEJUnqKENckqSOMsQlSeooQ1ySpI4yxCVJ6ihDXJKkjjLEJUnqKENckqSOMsQlSeooQ1ySpI4yxCVJ6ihDXJKkjjLEJUnqKENckqSOMsQlSeqo1kI8yRlJ7kzyozGWJ8mpSW5MsjLJ/LZqkSRpKmrzSPxM4JBxlr8K2K35Wgz8Y4u1SJI05bQW4lX1HeDucbocBnymeq4AnpnkuW3VI0nSVDPIa+I7ArcNm1/dtD1OksVJliVZtmbNms1SnCRJk10nbmyrqiVVNVRVQ7NmzRp0OZIkTQqDDPHbgZ2Gzc9u2iRJUh8GGeJLgT9u7lJfANxbVXcMsB5Jkjpl67Y2nOQLwEJghySrgZOA6QBVdTpwIfBq4Ebg18Bb2qpFkqSpqLUQr6pjJlhewPFtfb4kSVNdJ25skyRJj2eIS5LUUYa4JEkd1do1cWlz+dhFPxnI577r4BcO5HMlaT2PxCVJ6ihDXJKkjjLEJUnqKENckqSOMsQlSeooQ1ySpI4yxCVJ6iifE5da4vPrktrmkbgkSR1liEuS1FGGuCRJHWWIS5LUUYa4JEkdZYhLktRRhrgkSR3lc+LSFsRn16WpxSNxSZI6yhCXJKmjDHFJkjrKEJckqaMMcUmSOsoQlySpowxxSZI6yhCXJKmjDHFJkjqq1RBPckiSHye5Mcl7R1l+bJI1Sa5uvt7aZj2SJE0lrQ27mmQacBpwMLAa+GGSpVV1/Yiu51TVCW3VIUnSVNXm2On7ATdW1U0ASb4IHAaMDHFJWzjHdJc2TZun03cEbhs2v7ppG+mIJCuTnJdkp9E2lGRxkmVJlq1Zs6aNWiVJ6pxB39j2NWBOVe0FXAScNVqnqlpSVUNVNTRr1qzNWqAkSZNVmyF+OzD8yHp207ZBVa2tqt80s58C9m2xHkmSppQ2Q/yHwG5JdknyW8DRwNLhHZI8d9jsIuCGFuuRJGlKae3Gtqpal+QE4F+BacAZVXVdkpOBZVW1FHhHkkXAOuBu4Ni26pEkaapp8+50qupC4MIRbScOm34f8L42a5AkaapqNcQlqct89E2T3aDvTpckSZvIEJckqaMMcUmSOsoQlySpowxxSZI6yhCXJKmjDHFJkjrKEJckqaMMcUmSOsoR2ySpQxxFTsN5JC5JUkcZ4pIkdZQhLklSRxnikiR1lCEuSVJHGeKSJHWUIS5JUkcZ4pIkdZQhLklSRxnikiR1lCEuSVJHGeKSJHWUL0CRJD1hvphlMDwSlySpowxxSZI6ytPpkqQpaUs4xe+RuCRJHWWIS5LUUa2GeJJDkvw4yY1J3jvK8qckOadZfmWSOW3WI0nSVNJaiCeZBpwGvAp4MXBMkheP6PanwD1VtSvwMeAjbdUjSdJU0+aR+H7AjVV1U1X9J/BF4LARfQ4DzmqmzwNekSQt1iRJ0pTRZojvCNw2bH510zZqn6paB9wLbN9iTZIkTRmpqnY2nBwJHFJVb23m3wTsX1UnDOvzo6bP6mb+Z02fu0ZsazGwuJl9EfDjVoreNDsAd03YS+C+2hjuq/65r/rnvurfZNtXz6uqWSMb23xO/HZgp2Hzs5u20fqsTrI1MBNYO3JDVbUEWNJSnU9IkmVVNTToOrrAfdU/91X/3Ff9c1/1ryv7qs3T6T8EdkuyS5LfAo4Glo7osxR4czN9JHBJtXVqQJKkKaa1I/GqWpfkBOBfgWnAGVV1XZKTgWVVtRT4NPDZJDcCd9MLekmS1IdWh12tqguBC0e0nThs+iHg9W3WsBlMytP8k5T7qn/uq/65r/rnvupfJ/ZVaze2SZKkdjnsqiRJHWWIb6KJhpRVT5Kdklya5Pok1yV556BrmuySTEtyVZILBl3LZJfkmUnOS7IqyQ1JDhh0TZNVknc1/w3+KMkXkswYdE2TRZIzktzZPPa8vu1ZSS5K8tPm+3aDrHEshvgm6HNIWfWsA95dVS8GFgDHu68m9E7ghkEX0RGfAL5RVXOBebjfRpVkR+AdwFBV7UnvZmNvJP4vZwKHjGh7L/CtqtoN+FYzP+kY4pumnyFlBVTVHVW1opn+Fb3/yY4cuU+NJLOB1wCfGnQtk12SmcDv0nvKhar6z6r65WCrmtS2Bp7ajMmxDfAfA65n0qiq79B7Qmq44cOCnwW8drMW1SdDfNP0M6SsRmjeUrcPcOVgK5nUPg78FfDooAvpgF2ANcC/NJcfPpXkaYMuajKqqtuBU4B/B+4A7q2qbw62qknvOVV1RzP9c+A5gyxmLIa4Nosk2wJfAv68qu4bdD2TUZJDgTuravmga+mIrYH5wD9W1T7AA0zSU56D1lzPPYzeHz6/AzwtyRsHW1V3NIOQTcpHuQzxTdPPkLJqJJlOL8DPrqrzB13PJHYgsCjJLfQu0RyU5HODLWlSWw2srqr1Z3bOoxfqerz/BtxcVWuq6mHgfOBlA65psvtFkucCNN/vHHA9ozLEN00/Q8oKaF4t+2nghqr66KDrmcyq6n1VNbuq5tD7nbqkqjxaGkNV/Ry4LcmLmqZXANcPsKTJ7N+BBUm2af6bfAXeBDiR4cOCvxn46gBrGVOrI7ZNVWMNKTvgsiarA4E3Adcmubppe38zmp/0RP0ZcHbzx/RNwFsGXM+kVFVXJjkPWEHviZGr6MiIZJtDki8AC4EdkqwGTgI+DJyb5E+BW4H/PrgKx+aIbZIkdZSn0yVJ6ihDXJKkjjLEJUnqKENckqSOMsQlSeooQ1xqSZJHklzdvDXq/ybZZox+FyZ55iZs/3eax4Y2tb5bkuywqet3RZJjk/zOoOuQ2mCIS+15sKr2bt4a9Z/A24cvTM9WVfXqTXlxR1X9R1Ud+WQVO4UdS2+oUWnKMcSlzeO7wK5J5jTvof8M8CNgp/VHxM2yG5L8c/Pe528meSpAkl2TXJzkmiQrkryg6f+jZvmxSb6a5LLm/ccnrf/gJF9JsrzZ5uKJCk1ySPMZ1yT5VtP2rGY7K5NckWSvpv2DSc5K8t0ktyZ5XZK/T3Jtkm80Q+6uP+pf3/6DJLs27XOSXNJs91tJdm7az0xyapLvJ7kpyZHD6ntPkh826/zNsO08bt816w3RGxDm6qbtw+m9335lklOehH9baWAMcallzasfXwVc2zTtBvxDVe1RVbeO6L4bcFpV7QH8EjiiaT+7aZ9Hb8zrO3i8/Zr+ewGvTzLUtP9JVe1LL8zekWT7cWqdBfwzcETzWa9vFv0NcFVV7QW8H/jMsNVeABwELAI+B1xaVS8BHqT3WtX17m3aP0nvbW0A/wc4q9nu2cCpw/o/F3g5cCi90bNI8gfNPtoP2BvYN8nvjrXvquo8YBnwhqram94rOA8H9mg+8+/G2hdSFxjiUnue2gw1u4ze2NWfbtpvraorxljn5qpaPzztcmBOkqcDO1bVlwGq6qGq+vUo615UVWur6kF6L7h4edP+jiTXAFfQe3HPbuPUvAD4TlXd3HzW+ncsvxz4bNN2CbB9kmc0y77evFTjWnrDEH+jab8WmDNs218Y9v2AZvoA4PPN9GeH1Qzwlap6tKqu579eA/kHzddV9IYQnTvs53ncvhvl57sXeAj4dJLXAaPtR6kzHDtdas+DzdHfBr13T/DAOOv8Ztj0I8BTN+LzRo6hXEkW0nuD1QFV9esklwEzNmKb/fgNQFU9muTh+q+xnB/lsf+PqTGmx91uI8O+/++q+qfhHdN7V/2E+65578F+9F4AciRwAr2zCFIneSQuTXJV9StgdZLXAiR5yhh3uh/cXLt+KvBa4HvATOCeJsDn0jvSHs8VwO8m2aX5rGc17d8F3tC0LQTu2oT3wh817PvlzfT36b2xjWb7351gG/8K/El676cnyY5JnrGTFOAAAADsSURBVD3BOr8Cnt703xaY2byA513AvI36CaRJxiNxqRveBPxTkpOBh+ldq350RJ8f0Htv+2zgc1W1LMm1wNuT3AD8mF5Ij6mq1jQ3v52fZCt671A+GPggcEaSlfROQb957K2Mabtm/d8AxzRtfwb8S5L3AGuY4C1kVfXNJLsDlzdnNe4H3kjvyHssZwKnJ3mQ3r0JX00yg95R/V9sws8hTRq+xUyaApIcCwxV1QmDrmU0SW6hV99dg65Fmko8nS5JUkd5JC5JUkd5JC5JUkcZ4pIkdZQhLklSRxnikiR1lCEuSVJHGeKSJHXU/wc/SFGWfTWZsgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# preprocessing the dataset\n",
+    "dataArray = data.to_numpy()\n",
+    "X, y = dataArray[:,0:11], dataArray[0:,11]\n",
+    "X = StandardScaler().fit_transform(X)\n",
+    "\n",
+    "# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA()\n",
+    "pca.fit_transform(X)\n",
+    "pca_variance = pca.explained_variance_\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Variance ratio')\n",
+    "plt.xlabel('Principal components')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3038, 8)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\n",
+    "ninety_five = pca2.fit_transform(X)\n",
+    "ninety_five.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Explained variance:  0.9528002773163117\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eight features explain 95% of the variance in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca2 = PCA(3) # keeping features three principal components\n",
+    "principalComponents = pca2.fit_transform(X)\n",
+    "\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "plt.figure(figsize=(10,6))\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n",
+    "           linewidths=1, alpha=.5,\n",
+    "           edgecolor='k', s= 200,\n",
+    "           c=data['CLASS'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The three pincipal components wete visualised using a 3D plot. The figure above shows clustering of the three components. Each component is not exactly independent of the others so the clusters overlap to some extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#converting principal component ndarrays to DataFrame format\n",
+    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\n",
+    "finalDf = pd.concat([principalDf, data['CLASS']], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>PC3</th>\n",
+       "      <th>CLASS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.423532</td>\n",
+       "      <td>-0.528139</td>\n",
+       "      <td>0.010959</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.428729</td>\n",
+       "      <td>-1.258821</td>\n",
+       "      <td>-1.767912</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.211100</td>\n",
+       "      <td>2.074380</td>\n",
+       "      <td>-1.508877</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.912438</td>\n",
+       "      <td>2.516293</td>\n",
+       "      <td>-0.041182</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.947851</td>\n",
+       "      <td>2.369553</td>\n",
+       "      <td>-0.713456</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        PC1       PC2       PC3  CLASS\n",
+       "0 -0.423532 -0.528139  0.010959      1\n",
+       "1 -1.428729 -1.258821 -1.767912      1\n",
+       "2 -1.211100  2.074380 -1.508877      1\n",
+       "3 -0.912438  2.516293 -0.041182      1\n",
+       "4 -0.947851  2.369553 -0.713456      1"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finalDf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variance explained by three PCs:  65.30291340147798 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualising the top 2 principal components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize = (6,6))\n",
+    "ax = fig.add_subplot(111) \n",
+    "ax.set(xlim=(-10,10), ylim=(-10,10))\n",
+    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
+    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
+    "ax.set_title('top 2 components', fontsize = 20)\n",
+    "\n",
+    "targets = [0, 1]\n",
+    "colors = ['r', 'g']\n",
+    "\n",
+    "for target, color in zip(targets,colors):\n",
+    "    indices = finalDf['CLASS'] == target\n",
+    "    ax.scatter(finalDf.loc[indices, 'PC1']\n",
+    "               , finalDf.loc[indices, 'PC2']\n",
+    "               , c = color\n",
+    "               , s = 50, alpha = 0.4)\n",
+    "ax.legend(targets)\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting the into training and test part\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A mesh grid is required. This can be thought of as a matrix of coordinates upon which the model will make decisions.\n",
+    "These are then visualised to reveal decision boundaries.\n",
+    "The mesh grip was created based on the data and a step size of 0.02"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating mesh for the contour plot\n",
+    "\n",
+    "h = .02  # step size in the mesh\n",
+    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
+    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two principal components were used to enable visualisation on a scatter plot.\n",
+    "\n",
+    "The parameters for the PCA were generated on the training data and these were applied on both the training and training sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca4 = PCA(n_components=2)\n",
+    "\n",
+    "# applying PCA on training set\n",
+    "pca4.fit(X_train)\n",
+    "\n",
+    "#applying transform on training and testing sets\n",
+    "train_ = pca4.transform(X_train)\n",
+    "test_ = pca4.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Explained variance:  0.5330557904151471\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((2126, 2), (912, 2))"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_.shape, test_.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After transforming the data and the creating the meshgrid, decision boundaries for the algorithms were created by iterating over the classifiers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "figure = plt.figure(figsize=(27, 15))\n",
+    "i = 1\n",
+    "\n",
+    "datasets=[data]\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
+    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "        # Plot the top 2 principal components for training data\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n",
+    "                    edgecolors='k')\n",
+    "        # Plot the top 2 principal components for the testing data\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
+    "                    edgecolors='k')\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        i += 1\n",
+    "\n",
+    "        # iterate over classifiers\n",
+    "\n",
+    "    for name, clf in zip(names, classifiers):\n",
+    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "        clf.fit(train_, y_train)\n",
+    "        score = clf.score(test_, y_test)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n",
+    "        # Plot the testing points\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.4)\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(name)\n",
+    "            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n",
+    "            i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Accuracies of the different algorithms are indicated on the lower right corner.\n",
+    "\n",
+    "The plots show training points in solid colors and testing points semi-transparent. Contour decision boundaries were used which seperate points based on shared characteritics.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
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+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Assignment Colab/ilujumba.csv b/Assignment Colab/ilujumba.csv
new file mode 100644
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--- /dev/null
+++ b/Assignment Colab/ilujumba.csv	
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diff --git a/Assignment Colab/ilujumba1.csv b/Assignment Colab/ilujumba1.csv
new file mode 100644
index 0000000..6545f6a
--- /dev/null
+++ b/Assignment Colab/ilujumba1.csv	
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diff --git a/Assignment Colab/ilujumba2.csv b/Assignment Colab/ilujumba2.csv
new file mode 100644
index 0000000..529b97a
--- /dev/null
+++ b/Assignment Colab/ilujumba2.csv	
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diff --git a/Assignment Colab/ilujumba3.csv b/Assignment Colab/ilujumba3.csv
new file mode 100644
index 0000000..085569c
--- /dev/null
+++ b/Assignment Colab/ilujumba3.csv	
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diff --git a/Assignment Colab/ilujumba7.csv b/Assignment Colab/ilujumba7.csv
new file mode 100644
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--- /dev/null
+++ b/Assignment Colab/ilujumba7.csv	
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diff --git a/Assignment Colab/ilujumba8.csv b/Assignment Colab/ilujumba8.csv
new file mode 100644
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--- /dev/null
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diff --git a/Assignment Colab/ilujumba9.csv b/Assignment Colab/ilujumba9.csv
new file mode 100644
index 0000000..d5d0b48
--- /dev/null
+++ b/Assignment Colab/ilujumba9.csv	
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From 8af52ebcc443e4ad1f291f7934fa726eb03da9bc Mon Sep 17 00:00:00 2001
From: Ibra Lujumba <ibra.lujumba@gmail.com>
Date: Mon, 16 Mar 2020 15:52:54 +0300
Subject: [PATCH 08/10] New submission

Made suggested changes, left out graphics using bokeh because my notebook restarts everytime I run the cell with bokeh commands.
---
 .../Ibra Lujumba_kaggle-checkpoint.ipynb      | 1439 ++++++++++++++++
 Assignment Colab/Ibra Lujumba_kaggle.ipynb    | 1440 ++++++++++++++++-
 Thur 20 Feb/ACE_ClassML Pipeline.ipynb        |   18 +-
 3 files changed, 2885 insertions(+), 12 deletions(-)
 create mode 100644 Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb

diff --git a/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb b/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb
new file mode 100644
index 0000000..4c4273f
--- /dev/null
+++ b/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb	
@@ -0,0 +1,1439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ACE-Uganda Kaggle competition\n",
+    "### by Ibra Lujumba\n",
+    "#### 2019/HD07/27842U"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Contents\n",
+    "\n",
+    "- Exploratory Data Analysis\n",
+    "\n",
+    "-  Data transformation\n",
+    "\n",
+    "- Feature selection\n",
+    "\n",
+    "- Building a machine learning classifier\n",
+    "\n",
+    "- Measuring performance of a classifier\n",
+    "\n",
+    "- Machine learning is an iterative process\n",
+    "\n",
+    "- Visualising decision boundaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Exploratory Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exploratory data analysis (EDA) is the first step towards building a model that is capable of making predictions about the data. This is done to try to understand the properties of the data before any machine learning algorithm is used to make predictions using the data.\n",
+    "\n",
+    "The go-to libraries for manipulating data in python are **numpy**, and **pandas**. **matplotlib** and **seaborn** are used for data visualisation. These lbraries are imported using the **import** keyword."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Importing python modules for data analysis and visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # manipulation of arrays\n",
+    "import pandas as pd # manipulating dataframes\n",
+    "import matplotlib.pyplot as plt # data visualisation\n",
+    "import seaborn as sb # data visualisation,it is based on plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is necessary to ignore verbose warnings from functions in python. The data being manipulated may not necessarily meet the description of the input arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ignoring warnings that may arise\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Importing the datasets\n",
+    "The pandas library has the functionality to read data in delimited text files. The delimiter may be a tab(\\t), comma(,), semi-colon(;) or another metacharacter which is specified using the 'sep=' argument.\n",
+    "For comma-seperated files (csv), pd.read_csv() function is used. The default delimiter is a comma so there is no need to explicitly specify it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls ../input/ace-class-assignment/ # '!' specifies that this is not a python command \n",
+    "                                    # and should be executed outside the notebook\n",
+    "\n",
+    "# reading in the data\n",
+    "data = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\n",
+    "new = pd.read_csv('../input/ace-class-assignment/Test.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Checking the dimensions of the data as well as the datatype of each column\n",
+    "Dimensions of the dataframe are the number of rows and columns represented in the format (rows, columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking dimensions of the datasets\n",
+    "data.shape, new.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is important to check the type of data stored in the dataframe. Machine learning algorithms utilise numeric data to make predictions.\n",
+    "In cases where, there exists a non-numeric data type, it should be converted to numeric values through binarisation or numeric coding for different classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the datatypes of the variables\n",
+    "data.dtypes, new.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the values in all the variables exists as either floats or integers.\n",
+    "\n",
+    "##### Proceeding to work with the training dataset to build the classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Descriptive statistics of the train dataset such as arithmetic mean, standard deviation, quartiles and number of non-NA values in each column. These values inform what the next steps should be. \n",
+    "These steps include:\n",
+    "- cleaning of the dataset to remove rows that don't meet preset criteria\n",
+    "- taking care of skewed distributions\n",
+    "- taking care of missing values problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\n",
+    "AS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\n",
+    "For all the variables, the data points are not widely spread"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the proprotions of classses\n",
+    "data.groupby('CLASS').size()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both classes have an equal number of entries\n",
+    "\n",
+    "Obtaining pairwise correlation values for the variables in the train dataset to check if variables are independent of each other or multicollinearity exists within data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\n",
+    "pearsoncorr = data.corr(method='pearson')\n",
+    "\n",
+    "# visualizing the correlation matrix as a heatmap to make interpretation easier\n",
+    "plt.figure(figsize=(10,10))\n",
+    "top_corr = pearsoncorr.index\n",
+    "sb.heatmap(pearsoncorr, \n",
+    "            xticklabels=pearsoncorr.columns,\n",
+    "            yticklabels=pearsoncorr.columns,\n",
+    "            cmap='RdYlGn',\n",
+    "            annot=True,\n",
+    "            linewidth=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the p-values associated with the correlation values using the code below.\n",
+    "\n",
+    "`from scipy.stats import pearsonr`\n",
+    "\n",
+    "`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n",
+    "\n",
+    "Getting the *p-value* associated with a correlation value is necessary since it acknowledges whether the observed correlation between variables is significant or not. Normally, significance is confirmed if the *p-value* is below a threshold.\n",
+    "\n",
+    "In this examples, all the p-values for the observed correlations were significant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using a scatter plot matrix to visualise correlations\n",
+    "plt.figure(figsize=(40,40))\n",
+    "sb.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\n",
+    "These variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n",
+    "\n",
+    "Variables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This confirms that NT_EFC195 is a categorical variable with two classes 0 and 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[['CLASS','NT_EFC195']].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NT_EFC195 assumes both values irrespective of class\n",
+    "\n",
+    "Getting the associated p-values to check the correlation of features with the class. \n",
+    "The value of 1 at the bottom should be ignored since these values are obtained from the last column of a matrix of pairwise correlations therefore it corresponds to self correlation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "data.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the distribution and skewness of variables\n",
+    "plt.figure(figsize=(10,6))\n",
+    "data.skew().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of the variables are minimally skewed except NT_EFC195. Further checks will be done to try to understand the properties of this variable.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.groupby('NT_EFC195').size() # majority of the instances are of Class 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The skewedness in this variable can be understood by having most of its values at zeros\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n",
+    "\n",
+    "Tranformation possibilities\n",
+    "* using the minimum and maximum scaler\n",
+    "* standardisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Data transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Better performance of algorithms can be obtained if the data is transformed.\n",
+    "Some algorithms are may take features with large values as the most important features in the predictions which creates bias within predictions since predictions are majorly based on that variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Seperating the predictor variables from the target variable. Transformation should not be applied on the target variable.\n",
+    "\n",
+    "Operations on data are carried out on numpy ndArrays. The pandas dataframe is first converted to an ndArray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataArray = data.to_numpy()\n",
+    "\n",
+    "# seperating the predictor and response variables\n",
+    "target = dataArray[:,11]\n",
+    "predictors = dataArray[:,0:11]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numeric data may be rescaled (all values are put between a specified range) or standardized to have a mean of zero for normally distributed data.\n",
+    "Transformations have the advantage of improving performance of machine learning classifiers.\n",
+    "\n",
+    "Rescaling is done using the MinMaxScaler while standardising is done using the StandardScaler() functions. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using minMaxScaler to set all values between 0 and 1\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "rescaledPredictors = scaler.fit_transform(predictors)\n",
+    "                        \n",
+    "\n",
+    "# using StandardScaler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler1 = StandardScaler().fit(predictors)\n",
+    "standardizedPredictors = scaler1.transform(predictors)\n",
+    "                        \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Feature selection\n",
+    "This is done to be able to know which features contribute significantly to prediction of class. Once these features are known, they can be used to predict class. This has the advantage of reducing training time, preventing overfitting as well as improving accuracy of the model since redundant features are not used in the prediction process.\n",
+    "\n",
+    "On this dataset,univariate statistics such as F-test was used as an alternative to chi-squared test (some values after transformation are zero and chi2 returns an error)\n",
+    "\n",
+    "The F-test and feature selection were done on both the original and transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# untransformed data\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif\n",
+    "bestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "fit = bestFeatures.fit(predictors, target)\n",
+    "\n",
+    "scores = pd.DataFrame(fit.scores_) \n",
+    "pvalues = pd.DataFrame(fit.pvalues_)\n",
+    "columns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "featureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\n",
+    "featureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n",
+    "\n",
+    "print(featureValues.nlargest(7, 'score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the transformed data\n",
+    "\n",
+    "# rescaledPredictors\n",
+    "reBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "reFit = reBestFeatures.fit(rescaledPredictors, target)\n",
+    "\n",
+    "reScores = pd.DataFrame(reFit.scores_) \n",
+    "rePvalues = pd.DataFrame(reFit.pvalues_)\n",
+    "reColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "reFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\n",
+    "reFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "# standardizedPredictors\n",
+    "stBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "stFit = stBestFeatures.fit(standardizedPredictors, target)\n",
+    "\n",
+    "stScores = pd.DataFrame(stFit.scores_) \n",
+    "stPvalues = pd.DataFrame(stFit.pvalues_)\n",
+    "stColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "stFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\n",
+    "stFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n",
+    "\n",
+    "print(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The importance of different features can be ranked using the ExtraTressClassifier which is also known as the Extremely Randomized Trees. In this Extra Trees classifier, the features and splits are selected at random; hence, “Extremely Randomized Tree”. Since splits are chosen at random for each feature in the Extra Trees Classifier, it’s less computationally expensive than a Random Forest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using feature importance\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "model = ExtraTreesClassifier()\n",
+    "model.fit(predictors, target)\n",
+    "print(model.feature_importances_)\n",
+    "\n",
+    "# visualising feature importance\n",
+    "importances = pd.Series(model.feature_importances_, index=data.columns[0:11])\n",
+    "importances.nlargest(10).plot(kind='barh')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Building the classification model\n",
+    "Finding te right model for a given dataset is an iterative methods. It is advisable to pursue all combinations of data and algorithms to find the best algorithm that is able to offer satisfactory results on a given dataset.\n",
+    "It cannot be expected that an algorithm that performs well on a particular dataset will perform the same eay in another dataset.\n",
+    "Therefore, an exhaustive approach is necessary to find the best performing algorithm for a given dataset.\n",
+    "\n",
+    "Some of these combinations are (for original and transformed data):\n",
+    "- model + train/test sets\n",
+    "- model + kfold crossvalidation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\n",
+    "from sklearn.model_selection import train_test_split # random split\n",
+    "from sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\n",
+    "p_train, p_test, t_train, t_test = train_test_split(predictors, target, \n",
+    "                                                    test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on untransformed data\n",
+    "logit.fit(p_train, t_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Measuring model performance\n",
+    "Logistic regression is a supervised learning algorithm. Performance of the algorithm is measured as the number of correct predictions made by the algorithm out the the number of true class values.\n",
+    "We can measure the performance of a classification problem using precison, F1 Score, Receiver Operating Curve (ROC) curve, accuracy and Matthews Correlation Coefficient.\n",
+    "\n",
+    "Other performance metric are;\n",
+    "- True Positives (TP) / True Positive Rate (TPR): Number of correct positive predictions / Probability of predicting positive given that the actual class is positive\n",
+    "- False Negatives (FN) / False Negative Rate (FNR): Number of wrong negative predictions / Probability of predicting negative given that the actual class is positive\n",
+    "- True Negatives (TN) / True Negative Rate (TNR): Number of correct negative predictions / Probability of predicting negative given that the actual class is negative\n",
+    "- False Positives (FP) / False Positive Rate (FPR): Number of wrong positive predictions / Probability of predicting positive given that the actual class is negative\n",
+    "- Precision (P): Proportion of predicted positives that are correct\n",
+    "- Recall (R): Proportion of actual positives captured"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict on test data\n",
+    "predictions = logit.predict(p_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the confusion matrix\n",
+    "from sklearn import metrics\n",
+    "cm = metrics.confusion_matrix(t_test, predictions)\n",
+    "cm\n",
+    "sb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n",
+    "          square=True, cmap='Blues') \n",
+    "plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n",
+    "                                                      # higher values are better\n",
+    "# not affected by unbalanced classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Performance metrics are inter-related and can be visualised using an ROC curve where the true positive rate is plotted against the false positive rate. Essentially, the curve shows the tradeoff between sensitivity and specificity.\n",
+    "The maximum area under the curve (AUC) is one which represents 100% accuracy therefore higher values of AUC are desired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\n",
+    "fpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\n",
+    "auc = metrics.roc_auc_score(t_test, pred_probs)\n",
+    "plt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('tpr'), plt.xlabel('fpr')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The confusion matrix above shows the number of true positives, true negatives, false positives as well as false negatives.\n",
+    "The model made a total of 75 wrong class predictions. It can be seen that logistic regression has a good performance of the original dataset where all features are utilised in the prediction of the class.\n",
+    "\n",
+    "Something to keep in mind is that a good performance of the training set by an algorithm is not an indicator of how the model will performance when new data is presented to it. However, a good performance is desirable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance on new data\n",
+    "new_pred = logit.predict(new.values)\n",
+    "\n",
+    "pred_df = pd.DataFrame(new_pred) \n",
+    "pred_df.columns=[\"CLASS\"]\n",
+    "pred_df.index.name=\"Index\" \n",
+    "pred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df.to_csv(\"ilujumba.csv\") \n",
+    "print(pred_df['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the csv file was submitted to the competition, the model had an 83.33% accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Machine learning is an iterative process\n",
+    "\n",
+    "Different combinations of logistic regression with the data were tried.\n",
+    "- train_test split\n",
+    "\n",
+    "    model + rescaled data\n",
+    "    \n",
+    "    model + standardised data\n",
+    "\n",
+    "\n",
+    "- kfold crossvalidation\n",
+    "\n",
+    "    model + rescaled data\n",
+    "    \n",
+    "    model + standardised data\n",
+    "\n",
+    "#### Logistic regression on rescaled data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit1 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit1.fit(p1_train, t1_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions1 = logit1.predict(p1_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t1_test, predictions1))\n",
+    "\n",
+    "# rescaling new data\n",
+    "newArray = new.to_numpy()\n",
+    "rescaledNew = scaler.fit_transform(newArray)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance on new data (rescaled)\n",
+    "new_pred1 = logit1.predict(rescaledNew)\n",
+    "\n",
+    "pred_df1 = pd.DataFrame(new_pred1) \n",
+    "pred_df1.columns=[\"CLASS\"]\n",
+    "pred_df1.index.name=\"Index\" \n",
+    "pred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df1.to_csv(\"ilujumba1.csv\") \n",
+    "print(pred_df1['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df1.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df1.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This combination returned an 88.11% accuracy after submission."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on standardized data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit2 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit2.fit(p2_train, t2_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions2 = logit2.predict(p2_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\n",
+    "print('MCC: ',matthews_corrcoef(t2_test, predictions2))\n",
+    "\n",
+    "# standardizing new data\n",
+    "standardizedNew = scaler1.transform(newArray)\n",
+    "\n",
+    "# performance on new data (standaridized)\n",
+    "new_pred2 = logit2.predict(standardizedNew)\n",
+    "\n",
+    "pred_df2 = pd.DataFrame(new_pred2) \n",
+    "pred_df2.columns=[\"CLASS\"]\n",
+    "pred_df2.index.name=\"Index\" \n",
+    "pred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df2.to_csv(\"ilujumba2.csv\") \n",
+    "print(pred_df2['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df2.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df2.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model streategy returned an 86.03% accuracy after submission which is better than when the original data is used but slightly lower than when rescaled features are used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using selected features, rescaled data and Logistic regression\n",
+    "\n",
+    "An attempt was made to manually select for features that showed the highest importance in the prediction of class. All further strategies on data using Logistic Regression were done using rescaled data with the assumption that it could provide better model performance results when compared to other transformations of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit3 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit3.fit(p3_train, t3_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions3 = logit3.predict(p3_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t3_test, predictions3))\n",
+    "\n",
+    "# rescaling new data\n",
+    "# newArray = new.to_numpy()\n",
+    "# rescaledNew = scaler.fit_transform(newArray)\n",
+    "\n",
+    "# performance on new data (rescaled)\n",
+    "new_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n",
+    "\n",
+    "pred_df3 = pd.DataFrame(new_pred3) \n",
+    "pred_df3.columns=[\"CLASS\"]\n",
+    "pred_df3.index.name=\"Index\" \n",
+    "pred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df3.to_csv(\"ilujumba3.csv\") \n",
+    "print(pred_df3['CLASS'].unique())\n",
+    "\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df3.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df3.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using cross-validation and Logistic Regression\n",
+    "\n",
+    "During cross-validation, the data is split into kfolds. k-1 folds are used in the training are used while the kth fold is used for testing. The process is repeated for k times until each fold has been used as a testing set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42)\n",
+    "model6 = LogisticRegression()\n",
+    "model6.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model6, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model6_pred = model6.predict(rescaledNew)\n",
+    "df6 = pd.DataFrame(model6_pred)\n",
+    "df6.columns = ['CLASS']\n",
+    "df6.index.name = 'Index'\n",
+    "df6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n",
+    "\n",
+    "df6.to_csv('ilujumba7.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another thing to keep in mind is to exhaustive use of simple models on the data. The reason for this is that they have a good reputation in terms of performance on different types of data and their method of prediction is well understood. \n",
+    "\n",
+    "\n",
+    "Another simple algorithm for classification is the Naive Bayes classifier. This algorithm assumes that all features are independent of each other and each feature contributes equally to the resulting class. For this reason, it is called 'naive'.\n",
+    "\n",
+    "#### Naive Bayes classifier with kfold cross-validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model7 = GaussianNB()\n",
+    "model7.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model7, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model7_pred = model7.predict(newArray)\n",
+    "df7 = pd.DataFrame(model7_pred)\n",
+    "df7.columns = ['CLASS']\n",
+    "df7.index.name = 'Index'\n",
+    "df7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df7.to_csv('ilujumba7.csv')\n",
+    "print(df7['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df7.groupby('CLASS').size()[0].sum())\n",
+    "print(df7.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier on rescaled features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model8 = GaussianNB()\n",
+    "model8.fit(rescaledPredictors, target)\n",
+    "\n",
+    "results1 = cross_val_score(model8, rescaledPredictors, target)\n",
+    "print(results1.mean())\n",
+    "\n",
+    "model8_pred = model8.predict(rescaledNew)\n",
+    "df8 = pd.DataFrame(model8_pred)\n",
+    "df8.columns = ['CLASS']\n",
+    "df8.index.name = 'Index'\n",
+    "df8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df8.to_csv('ilujumba8.csv')\n",
+    "print(df8['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df8.groupby('CLASS').size()[0].sum())\n",
+    "print(df8.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes and kfold validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model9 = GaussianNB()\n",
+    "model9.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\n",
+    "print('mean for results', results.mean())\n",
+    "\n",
+    "predic = cross_val_predict(model9, predictors, target, cv =10)\n",
+    "accuracy = metrics.r2_score(target, predic)\n",
+    "print('cross-predicted accuracy ', accuracy)\n",
+    "\n",
+    "model9_pred = model9.predict(newArray)\n",
+    "df9 = pd.DataFrame(model9_pred)\n",
+    "df9.columns = ['CLASS']\n",
+    "df9.index.name = 'Index'\n",
+    "df9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df9.to_csv('ilujumba9.csv')\n",
+    "print(df9['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df9.groupby('CLASS').size()[0].sum())\n",
+    "print(df9.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model strategy returned the highest score after submission with a 99.599% accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing several algorithms to look at the nature of the decision boundaries created"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://medium.com/cascade-bio-blog/creating-visualizations-to-better-understand-your-data-and-models-part-2-28d5c46e956"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Algorithms define a set of hyperplanes that divide the datapoints to their respective classes and span the feature space trained on. Visualising enables one to understand the limitations of a given algorithm on a dataset given to it.\n",
+    "Thus decision boundaries enable one to understand to how the training data selected affects performance of the algorithm."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ten sklearn non-linear classifier algorithms were compared. KNearest Neighbors, Support Vector Machines (Linear and RBF kernels), Gaussian Process Classifier, Decision Trees, Random Forest, Neural Networks, AdaBoost, Naive Bayes, \"Quadratic Discriminant Analysis, Logistic Regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing classifiers from the sklearn library\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPClassifier #1\n",
+    "from sklearn.neighbors import KNeighborsClassifier #2\n",
+    "from sklearn.svm import SVC #3\n",
+    "from sklearn.gaussian_process import GaussianProcessClassifier #4\n",
+    "from sklearn.gaussian_process.kernels import RBF #5\n",
+    "from sklearn.tree import DecisionTreeClassifier #6\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\n",
+    "from sklearn.naive_bayes import GaussianNB #9\n",
+    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\n",
+    "from sklearn.linear_model import LogisticRegression #11\n",
+    "\n",
+    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
+    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
+    "         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n",
+    "\n",
+    "classifiers = [\n",
+    "    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n",
+    "    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n",
+    "    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n",
+    "    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n",
+    "    DecisionTreeClassifier(),\n",
+    "    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n",
+    "    MLPClassifier(max_iter=1000), #iterations until converge\n",
+    "    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n",
+    "    GaussianNB(), #NaiveBayes\n",
+    "    QuadraticDiscriminantAnalysis(),\n",
+    "    LogisticRegression()]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dimensionality reduction\n",
+    "\n",
+    "https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/\n",
+    "\n",
+    "https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the data is multi-dimensional, it was reduced using Principal Component Analysis (PCA) to reduce it to two components.\n",
+    "Trial runs were done to check how much of the variation in the data is explained by the principal components.\n",
+    "\n",
+    "Another thing to keep in mind is that PCA works best on standardised/normalised data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preprocessing the dataset\n",
+    "dataArray = data.to_numpy()\n",
+    "X, y = dataArray[:,0:11], dataArray[0:,11]\n",
+    "X = StandardScaler().fit_transform(X)\n",
+    "\n",
+    "# reducing dimensions of the dataset using PCA  \n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA()\n",
+    "pca.fit_transform(X)\n",
+    "pca_variance = pca.explained_variance_\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Variance ratio')\n",
+    "plt.xlabel('Principal components')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\n",
+    "ninety_five = pca2.fit_transform(X)\n",
+    "ninety_five.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eight features explain 95% of the variance in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca2 = PCA(3) # keeping features three principal components\n",
+    "principalComponents = pca2.fit_transform(X)\n",
+    "\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "plt.figure(figsize=(10,6))\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n",
+    "           linewidths=1, alpha=.5,\n",
+    "           edgecolor='k', s= 200,\n",
+    "           c=data['CLASS'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The three pincipal components wete visualised using a 3D plot. The figure above shows clustering of the three components. Each component is not exactly independent of the others so the clusters overlap to some extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#converting principal component ndarrays to DataFrame format\n",
+    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\n",
+    "finalDf = pd.concat([principalDf, data['CLASS']], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finalDf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualising the top 2 principal components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize = (6,6))\n",
+    "ax = fig.add_subplot(111) \n",
+    "ax.set(xlim=(-10,10), ylim=(-10,10))\n",
+    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
+    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
+    "ax.set_title('top 2 components', fontsize = 20)\n",
+    "\n",
+    "targets = [0, 1]\n",
+    "colors = ['r', 'g']\n",
+    "\n",
+    "for target, color in zip(targets,colors):\n",
+    "    indices = finalDf['CLASS'] == target\n",
+    "    ax.scatter(finalDf.loc[indices, 'PC1']\n",
+    "               , finalDf.loc[indices, 'PC2']\n",
+    "               , c = color\n",
+    "               , s = 50, alpha = 0.4)\n",
+    "ax.legend(targets)\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting the into training and test part\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A mesh grid is required. This can be thought of as a matrix of coordinates upon which the model will make decisions.\n",
+    "These are then visualised to reveal decision boundaries.\n",
+    "The mesh grip was created based on the data and a step size of 0.02"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating mesh for the contour plot\n",
+    "\n",
+    "h = .02  # step size in the mesh\n",
+    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
+    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two principal components were used to enable visualisation on a scatter plot.\n",
+    "\n",
+    "The parameters for the PCA were generated on the training data and these were applied on both the training and training sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca4 = PCA(n_components=2)\n",
+    "\n",
+    "# applying PCA on training set\n",
+    "pca4.fit(X_train)\n",
+    "\n",
+    "#applying transform on training and testing sets\n",
+    "train_ = pca4.transform(X_train)\n",
+    "test_ = pca4.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_.shape, test_.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After transforming the data and the creating the meshgrid, decision boundaries for the algorithms were created by iterating over the classifiers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "figure = plt.figure(figsize=(27, 15))\n",
+    "i = 1\n",
+    "\n",
+    "datasets=[data]\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
+    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "        # Plot the top 2 principal components for training data\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n",
+    "                    edgecolors='k')\n",
+    "        # Plot the top 2 principal components for the testing data\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
+    "                    edgecolors='k')\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        i += 1\n",
+    "\n",
+    "        # iterate over classifiers\n",
+    "\n",
+    "    for name, clf in zip(names, classifiers):\n",
+    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "        clf.fit(train_, y_train)\n",
+    "        score = clf.score(test_, y_test)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n",
+    "        # Plot the testing points\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.4)\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(name)\n",
+    "            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n",
+    "            i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Accuracies of the different algorithms are indicated on the lower right corner.\n",
+    "\n",
+    "The plots show training points in solid colors and testing points semi-transparent.Decision boundaries for GaussianProcessClassifier, RandomForest and AdaBoost are complicated while the decison boundaries for LogisticRegression, NaiveBayes, NeuralNetwork, and Linear SVM are simpler. GaussianProcess and RBF SVM have contoured decision boundaries which seperate points with similar characteristics.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Assignment Colab/Ibra Lujumba_kaggle.ipynb b/Assignment Colab/Ibra Lujumba_kaggle.ipynb
index 600d184..4c4273f 100644
--- a/Assignment Colab/Ibra Lujumba_kaggle.ipynb	
+++ b/Assignment Colab/Ibra Lujumba_kaggle.ipynb	
@@ -1 +1,1439 @@
-{"cells":[{"metadata":{},"cell_type":"markdown","source":"## ACE_Uganda Kaggle competition 1\n### by Ibra Lujumba"},{"metadata":{},"cell_type":"markdown","source":"#### Exploratory Data Analysis "},{"metadata":{},"cell_type":"markdown","source":"This is done to try to understand the properties of the data before any machine learning algorithm id used to make predictions about the data."},{"metadata":{},"cell_type":"markdown","source":"##### Importing python modules for data analysis and visualization"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np # manipulation of arrays\nimport pandas as pd # manipulating dataframes\nimport matplotlib.pyplot as plt # data visualisation\nimport seaborn as sb # data visualisation,it is based on plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#ignoring warnings that may arise\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"###### Importing the datasets"},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input/ace-class-assignment/\n\n# reading in the data\ndata = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\nnew = pd.read_csv('../input/ace-class-assignment/Test.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"##### Checking the dimensions of the data as well as the datatype of each column"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking dimensions of the datasets\ndata.shape, new.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the datatypes of the variables\ndata.dtypes, new.dtypes","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"All the values in all the variables exists as either floats or integers.\n\nProceeding to work with the training dataset to build the classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the descriptive statistics of the train dataset such as arithmetic mean, \n# standard deviation, quartiles and number of non-NA values in each column \ndata.describe()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"raw","source":"From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\nAS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\nFor all the variables, the data points are not widely spread"},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the proprotions of classses\ndata.groupby('CLASS').size()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"raw","source":"Both classes have an equal number of entries"},{"metadata":{"trusted":true},"cell_type":"code","source":"# obtaining pairwise correlation values for the variables in the train dataset\n# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\npearsoncorr = data.corr(method='pearson')\n\n# visualizing the correlation matrix as a heatmap to make interpretation easier\nplt.figure(figsize=(10,10))\ntop_corr = pearsoncorr.index\nsb.heatmap(pearsoncorr, \n            xticklabels=pearsoncorr.columns,\n            yticklabels=pearsoncorr.columns,\n            cmap='RdYlGn',\n            annot=True,\n            linewidth=0.5)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."},{"metadata":{},"cell_type":"markdown","source":"You can get the p-values associated with the correlation values using the code below.\n\n`from scipy.stats import pearsonr`\n\n`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n\nIn this example, all values were too low to be informative"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using a scatter plot matrix to visualise correlations\n# plt.figure(figsize=(60,60))\n# sb.pairplot(data)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\nThese variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n\nVariables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"},{"metadata":{"trusted":true},"cell_type":"code","source":"len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n\n#this confirms that NT_EFC195 is a categorical variable","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# getting the associated p-values. The value of 1 at the bottom should be ignored \nfrom scipy.stats import pearsonr\ndata.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the distribution and skewness of variables\nplt.figure(figsize=(10,6))\ndata.skew().plot(kind='bar')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Most of the variables are minimally skewed except NT_EFC195. Further checks will be done to try to understand the properties of this variable.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"data.groupby('NT_EFC195').size() # majority of the instances are of Class 0.","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The skewedness in this variable can be understood by having most of its values at zeros\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n\nTranformation possibilities\n* using the minimum and maximum scaler\n* standardisation"},{"metadata":{},"cell_type":"markdown","source":"#### Data transformation"},{"metadata":{},"cell_type":"markdown","source":"Better performance of algorithms can be obtained if the data is transformed.\nSome algorithms are may take features with large values as the most important features in the predictions"},{"metadata":{},"cell_type":"markdown","source":"Seperating the predictor variables from the target variable"},{"metadata":{"trusted":true},"cell_type":"code","source":"# converting Pandas dataframe to ndArray\ndataArray = data.to_numpy()\n\n# seperating the predictor and response variables\ntarget = dataArray[:,11]\npredictors = dataArray[:,0:11]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using minMaxScaler to set all values between 0 and 1\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler(feature_range=(0,1))\nrescaledPredictors = scaler.fit_transform(predictors)\n                        \n\n# using StandardScaler\nfrom sklearn.preprocessing import StandardScaler\nscaler1 = StandardScaler().fit(predictors)\nstandardizedPredictors = scaler1.transform(predictors)\n                        \n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Feature selection"},{"metadata":{"trusted":true},"cell_type":"code","source":"# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n\n# untransformed data\nfrom sklearn.feature_selection import SelectKBest, f_classif\nbestFeatures = SelectKBest(score_func=f_classif, k=7)\nfit = bestFeatures.fit(predictors, target)\n\nscores = pd.DataFrame(fit.scores_) \npvalues = pd.DataFrame(fit.pvalues_)\ncolumns = pd.DataFrame(data.columns[0:11])\n\nfeatureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\nfeatureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n\nprint(featureValues.nlargest(7, 'score'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the transformed data\n\n# rescaledPredictors\nreBestFeatures = SelectKBest(score_func=f_classif, k=7)\nreFit = reBestFeatures.fit(rescaledPredictors, target)\n\nreScores = pd.DataFrame(reFit.scores_) \nrePvalues = pd.DataFrame(reFit.pvalues_)\nreColumns = pd.DataFrame(data.columns[0:11])\n\nreFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\nreFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n\n\n\n# standardizedPredictors\nstBestFeatures = SelectKBest(score_func=f_classif, k=7)\nstFit = stBestFeatures.fit(standardizedPredictors, target)\n\nstScores = pd.DataFrame(stFit.scores_) \nstPvalues = pd.DataFrame(stFit.pvalues_)\nstColumns = pd.DataFrame(data.columns[0:11])\n\nstFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\nstFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n\nprint(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using feature importance\nfrom sklearn.ensemble import ExtraTreesClassifier\nmodel = ExtraTreesClassifier()\nmodel.fit(predictors, target)\nprint(model.feature_importances_)\n\n# visualising feature importance\nimportances = pd.Series(model.feature_importances_, index=data.columns[0:11])\nimportances.nlargest(10).plot(kind='barh')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"#### Building the classification model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\nfrom sklearn.model_selection import train_test_split # random split\nfrom sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\np_train, p_test, t_train, t_test = train_test_split(predictors, target, \n                                                    test_size=0.30,random_state=42)\n\nlogit = LogisticRegression() # making instance of model\n\n# fitting the model on untransformed data\nlogit.fit(p_train, t_train)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Measuring model performance\n\nWe can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# predict on test data\npredictions = logit.predict(p_test) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# the confusion matrix\nfrom sklearn import metrics\ncm = metrics.confusion_matrix(t_test, predictions)\ncm\nsb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n          square=True, cmap='Blues') \nplt.ylabel('Actual label'); plt.xlabel('Predicted label')\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\nprint(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\nprint(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n                                                      # higher values are better\n# not affected by unbalanced classes\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ROC curve of true positive rate against false positive rate\n# shows tradeoff between sensitivy and specificity\n\npred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\nfpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\nauc = metrics.roc_auc_score(t_test, pred_probs)\nplt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\nplt.legend(loc=4)\nplt.ylabel('tpr'), plt.xlabel('fpr')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data\nnew_pred = logit.predict(new.values)\n\npred_df = pd.DataFrame(new_pred) \npred_df.columns=[\"CLASS\"]\npred_df.index.name=\"Index\" \npred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df.to_csv(\"ilujumba.csv\") \nprint(pred_df['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df.groupby('CLASS').size()[0].sum())\nprint(pred_df.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on rescaled data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit1 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit1.fit(p1_train, t1_train)\n\n# predict on test data\npredictions1 = logit1.predict(p1_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\nprint(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\nprint(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t1_test, predictions1))\n\n# rescaling new data\nnewArray = new.to_numpy()\nrescaledNew = scaler.fit_transform(newArray)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# performance on new data (rescaled)\nnew_pred1 = logit1.predict(rescaledNew)\n\npred_df1 = pd.DataFrame(new_pred1) \npred_df1.columns=[\"CLASS\"]\npred_df1.index.name=\"Index\" \npred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df1.to_csv(\"ilujumba1.csv\") \nprint(pred_df1['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df1.groupby('CLASS').size()[0].sum())\nprint(pred_df1.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Logistic regression on standardized data"},{"metadata":{"trusted":true},"cell_type":"code","source":"p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n                                                        test_size=0.30,random_state=42)\n\nlogit2 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit2.fit(p2_train, t2_train)\n\n# predict on test data\npredictions2 = logit2.predict(p2_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\nprint(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\nprint(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\nprint('MCC: ',matthews_corrcoef(t2_test, predictions2))\n\n# standardizing new data\nstandardizedNew = scaler1.transform(newArray)\n\n# performance on new data (standaridized)\nnew_pred2 = logit2.predict(standardizedNew)\n\npred_df2 = pd.DataFrame(new_pred2) \npred_df2.columns=[\"CLASS\"]\npred_df2.index.name=\"Index\" \npred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df2.to_csv(\"ilujumba2.csv\") \nprint(pred_df2['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(pred_df2.groupby('CLASS').size()[0].sum())\nprint(pred_df2.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Using selected features, rescaled data and Logistic regression\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n                                                        test_size=0.30,random_state=42)\n\nlogit3 = LogisticRegression() # making instance of model\n\n# fitting the model on rescaled data\nlogit3.fit(p3_train, t3_train)\n\n# predict on test data\npredictions3 = logit3.predict(p3_test)\n\n# performance metrics\nprint(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\nprint(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\nprint(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n\nfrom sklearn.metrics import matthews_corrcoef\nprint('MCC: ',matthews_corrcoef(t3_test, predictions3))\n\n# rescaling new data\n# newArray = new.to_numpy()\n# rescaledNew = scaler.fit_transform(newArray)\n\n# performance on new data (rescaled)\nnew_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n\npred_df3 = pd.DataFrame(new_pred3) \npred_df3.columns=[\"CLASS\"]\npred_df3.index.name=\"Index\" \npred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n\n#csv file output\npred_df3.to_csv(\"ilujumba3.csv\") \nprint(pred_df3['CLASS'].unique())\n\n\n#printing the numbers of False and True\nprint(pred_df3.groupby('CLASS').size()[0].sum())\nprint(pred_df3.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Using cross-validation and Logistic Regression"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\n\nkfold = KFold(n_splits=10, random_state=42)\nmodel6 = LogisticRegression()\nmodel6.fit(predictors, target)\n\nresults = cross_val_score(model6, predictors, target)\nprint(results.mean())\n\nmodel6_pred = model6.predict(rescaledNew)\ndf6 = pd.DataFrame(model6_pred)\ndf6.columns = ['CLASS']\ndf6.index.name = 'Index'\ndf6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n\ndf6.to_csv('ilujumba7.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier with kfold cross-validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel7 = GaussianNB()\nmodel7.fit(predictors, target)\n\nresults = cross_val_score(model7, predictors, target)\nprint(results.mean())\n\nmodel7_pred = model7.predict(newArray)\ndf7 = pd.DataFrame(model7_pred)\ndf7.columns = ['CLASS']\ndf7.index.name = 'Index'\ndf7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf7.to_csv('ilujumba7.csv')\nprint(df7['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df7.groupby('CLASS').size()[0].sum())\nprint(df7.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes classifier on rescaled features\n\nAssumes that all features are independent of each other and each feature contributes equally to the resulting class"},{"metadata":{"trusted":true},"cell_type":"code","source":"kfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel8 = GaussianNB()\nmodel8.fit(rescaledPredictors, target)\n\nresults1 = cross_val_score(model8, rescaledPredictors, target)\nprint(results1.mean())\n\nmodel8_pred = model8.predict(rescaledNew)\ndf8 = pd.DataFrame(model8_pred)\ndf8.columns = ['CLASS']\ndf8.index.name = 'Index'\ndf8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf8.to_csv('ilujumba8.csv')\nprint(df8['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df8.groupby('CLASS').size()[0].sum())\nprint(df8.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Naive Bayes and kfold validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import cross_val_predict\nfrom sklearn.naive_bayes import GaussianNB\n\nkfold = KFold(n_splits=10, random_state=42, shuffle=True)\nmodel9 = GaussianNB()\nmodel9.fit(predictors, target)\n\nresults = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\nprint('mean for results', results.mean())\n\npredic = cross_val_predict(model9, predictors, target, cv =10)\naccuracy = metrics.r2_score(target, predic)\nprint('cross-predicted accuracy ', accuracy)\n\nmodel9_pred = model9.predict(newArray)\ndf9 = pd.DataFrame(model9_pred)\ndf9.columns = ['CLASS']\ndf9.index.name = 'Index'\ndf9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n\ndf9.to_csv('ilujumba9.csv')\nprint(df9['CLASS'].unique())\n\n#printing the numbers of False and True\nprint(df9.groupby('CLASS').size()[0].sum())\nprint(df9.groupby('CLASS').size()[1].sum())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Comparing several algorithms to look at the nature of the decision boundaries created"},{"metadata":{},"cell_type":"markdown","source":"https://medium.com/cascade-bio-blog/creating-visualizations-to-better-understand-your-data-and-models-part-2-28d5c46e956"},{"metadata":{},"cell_type":"markdown","source":"Algorithms define a st of hyperplanes that divide the datapoints to their respective classes and span the feature space trained on. Visualising enables one to understand the limitations of a given algorithm on a dataset given to it.\nThus decision boundaries enable one to understand to how the training data selected affects performance of the algorithm."},{"metadata":{},"cell_type":"markdown","source":"Ten sklearn classifier algorithms were compared"},{"metadata":{"trusted":true},"cell_type":"code","source":"#importing classifiers from the sklearn library\n\nfrom matplotlib.colors import ListedColormap\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neural_network import MLPClassifier #1\nfrom sklearn.neighbors import KNeighborsClassifier #2\nfrom sklearn.svm import SVC #3\nfrom sklearn.gaussian_process import GaussianProcessClassifier #4\nfrom sklearn.gaussian_process.kernels import RBF #5\nfrom sklearn.tree import DecisionTreeClassifier #6\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\nfrom sklearn.naive_bayes import GaussianNB #9\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\nfrom sklearn.linear_model import LogisticRegression #11\n\nnames = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n\nclassifiers = [\n    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n    DecisionTreeClassifier(),\n    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n    MLPClassifier(max_iter=1000), #iterations until converge\n    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n    GaussianNB(),\n    QuadraticDiscriminantAnalysis(),\n    LogisticRegression()]\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Dimensionality reduction\nhttps://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"},{"metadata":{},"cell_type":"markdown","source":"Since the data is multi-dimensional, it was reduced using Principal Component Analysis (PCA) to reduce it to two components.\nTrial runs were done to check how much of the variation in the data is explained by the principal components.\n\nAnother thing to keep in mind is that PCA works best on standardised/normalised data"},{"metadata":{"trusted":true},"cell_type":"code","source":"# preprocessing the dataset\ndataArray = data.to_numpy()\nX, y = dataArray[:,0:11], dataArray[0:,11]\nX = StandardScaler().fit_transform(X)\n\n# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\nfrom sklearn.decomposition import PCA\npca = PCA()\npca.fit_transform(X)\npca_variance = pca.explained_variance_\nplt.figure(figsize=(8, 6))\nplt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\nplt.legend()\nplt.ylabel('Variance ratio')\nplt.xlabel('Principal components')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\nninety_five = pca2.fit_transform(X)\nninety_five.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Eight features explain 95% of the variance in the dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"pca2 = PCA(3) # keeping features three principal components\nprincipalComponents = pca2.fit_transform(X)\n\nfrom mpl_toolkits.mplot3d import Axes3D\nplt.figure(figsize=(10,6))\nax = plt.axes(projection='3d')\nax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n           linewidths=1, alpha=.5,\n           edgecolor='k', s= 200,\n           c=data['CLASS'])\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The three pincipal components wete visualised using a 3D plot. The figure above shows clustering of the three components. Each component is not exactly independent of the others so the clusters overlap to some extent"},{"metadata":{"trusted":true},"cell_type":"code","source":"#converting principal component ndarrays to DataFrame format\nprincipalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\nfinalDf = pd.concat([principalDf, data['CLASS']], axis = 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"finalDf.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Visualising the top 2 principal components"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = plt.figure(figsize = (6,6))\nax = fig.add_subplot(111) \nax.set(xlim=(-10,10), ylim=(-10,10))\nax.set_xlabel('Principal Component 1', fontsize = 15)\nax.set_ylabel('Principal Component 2', fontsize = 15)\nax.set_title('top 2 components', fontsize = 20)\n\ntargets = [0, 1]\ncolors = ['r', 'g']\n\nfor target, color in zip(targets,colors):\n    indices = finalDf['CLASS'] == target\n    ax.scatter(finalDf.loc[indices, 'PC1']\n               , finalDf.loc[indices, 'PC2']\n               , c = color\n               , s = 50)\nax.legend(targets)\nax.grid()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# splitting the into training and test part\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"A mesh grid is required. This can be thought of as a matrix of coordinates upon which the model will make decisions.\nThese are then visualised to reveal decision boundaries.\nThe mesh grip was created based on the data and a step size of 0.02"},{"metadata":{"trusted":true},"cell_type":"code","source":"# creating mesh for the contour plot\n\nh = .02  # step size in the mesh\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Two principal components were used to enable visualisation on a scatter plot.\n\nThe parameters for the PCA were generated on the training data and these were applied on both the training and training sets"},{"metadata":{"trusted":true},"cell_type":"code","source":"pca4 = PCA(n_components=2)\n\n# applying PCA on training set\npca4.fit(X_train)\n\n#applying transform on training and testing sets\ntrain_ = pca4.transform(X_train)\ntest_ = pca4.transform(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_.shape, test_.shape","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"After transforming the data and the creating the meshgrid, decision boundaries for the algorithms were created by iterating over the classifiers."},{"metadata":{"trusted":true},"cell_type":"code","source":"figure = plt.figure(figsize=(27, 15))\ni = 1\n\ndatasets=[data]\nfor ds_cnt, ds in enumerate(datasets):\n    # just plot the dataset first\n    cm = plt.cm.RdBu\n    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n\n    if ds_cnt == 0:\n        ax.set_title(\"Input data\")\n        # Plot the top 2 principal components for training data\n        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n                    edgecolors='k')\n        # Plot the top 2 principal components for the testing data\n        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n                    edgecolors='k')\n        ax.set_xlim(xx.min(), xx.max())\n        ax.set_ylim(yy.min(), yy.max())\n        ax.set_xticks(())\n        ax.set_yticks(())\n        i += 1\n\n        # iterate over classifiers\n\n    for name, clf in zip(names, classifiers):\n        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n        clf.fit(train_, y_train)\n        score = clf.score(test_, y_test)\n\n        # Plot the decision boundary. For that, we will assign a color to each\n        # point in the mesh [x_min, x_max]x[y_min, y_max].\n\n        if hasattr(clf, \"decision_function\"):\n            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n        else:\n            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n\n        # Put the result into a color plot\n        Z = Z.reshape(xx.shape)\n        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n\n        # Plot the training points\n        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n        # Plot the testing points\n        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.4)\n\n        ax.set_xlim(xx.min(), xx.max())\n        ax.set_ylim(yy.min(), yy.max())\n        ax.set_xticks(())\n        ax.set_yticks(())\n        if ds_cnt == 0:\n            ax.set_title(name)\n            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n            i += 1\n\nplt.tight_layout()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Accuracies of the different algorithms are indicated on the lower right corner.\n\nThe plots show training points in solid colors and testing points semi-transparent. Contour decision boundaries were used which seperate points based on shared characteritics.\n"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ACE-Uganda Kaggle competition\n",
+    "### by Ibra Lujumba\n",
+    "#### 2019/HD07/27842U"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Contents\n",
+    "\n",
+    "- Exploratory Data Analysis\n",
+    "\n",
+    "-  Data transformation\n",
+    "\n",
+    "- Feature selection\n",
+    "\n",
+    "- Building a machine learning classifier\n",
+    "\n",
+    "- Measuring performance of a classifier\n",
+    "\n",
+    "- Machine learning is an iterative process\n",
+    "\n",
+    "- Visualising decision boundaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Exploratory Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exploratory data analysis (EDA) is the first step towards building a model that is capable of making predictions about the data. This is done to try to understand the properties of the data before any machine learning algorithm is used to make predictions using the data.\n",
+    "\n",
+    "The go-to libraries for manipulating data in python are **numpy**, and **pandas**. **matplotlib** and **seaborn** are used for data visualisation. These lbraries are imported using the **import** keyword."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Importing python modules for data analysis and visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # manipulation of arrays\n",
+    "import pandas as pd # manipulating dataframes\n",
+    "import matplotlib.pyplot as plt # data visualisation\n",
+    "import seaborn as sb # data visualisation,it is based on plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is necessary to ignore verbose warnings from functions in python. The data being manipulated may not necessarily meet the description of the input arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ignoring warnings that may arise\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Importing the datasets\n",
+    "The pandas library has the functionality to read data in delimited text files. The delimiter may be a tab(\\t), comma(,), semi-colon(;) or another metacharacter which is specified using the 'sep=' argument.\n",
+    "For comma-seperated files (csv), pd.read_csv() function is used. The default delimiter is a comma so there is no need to explicitly specify it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls ../input/ace-class-assignment/ # '!' specifies that this is not a python command \n",
+    "                                    # and should be executed outside the notebook\n",
+    "\n",
+    "# reading in the data\n",
+    "data = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\n",
+    "new = pd.read_csv('../input/ace-class-assignment/Test.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Checking the dimensions of the data as well as the datatype of each column\n",
+    "Dimensions of the dataframe are the number of rows and columns represented in the format (rows, columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking dimensions of the datasets\n",
+    "data.shape, new.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is important to check the type of data stored in the dataframe. Machine learning algorithms utilise numeric data to make predictions.\n",
+    "In cases where, there exists a non-numeric data type, it should be converted to numeric values through binarisation or numeric coding for different classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the datatypes of the variables\n",
+    "data.dtypes, new.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the values in all the variables exists as either floats or integers.\n",
+    "\n",
+    "##### Proceeding to work with the training dataset to build the classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Descriptive statistics of the train dataset such as arithmetic mean, standard deviation, quartiles and number of non-NA values in each column. These values inform what the next steps should be. \n",
+    "These steps include:\n",
+    "- cleaning of the dataset to remove rows that don't meet preset criteria\n",
+    "- taking care of skewed distributions\n",
+    "- taking care of missing values problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\n",
+    "AS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\n",
+    "For all the variables, the data points are not widely spread"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the proprotions of classses\n",
+    "data.groupby('CLASS').size()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both classes have an equal number of entries\n",
+    "\n",
+    "Obtaining pairwise correlation values for the variables in the train dataset to check if variables are independent of each other or multicollinearity exists within data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\n",
+    "pearsoncorr = data.corr(method='pearson')\n",
+    "\n",
+    "# visualizing the correlation matrix as a heatmap to make interpretation easier\n",
+    "plt.figure(figsize=(10,10))\n",
+    "top_corr = pearsoncorr.index\n",
+    "sb.heatmap(pearsoncorr, \n",
+    "            xticklabels=pearsoncorr.columns,\n",
+    "            yticklabels=pearsoncorr.columns,\n",
+    "            cmap='RdYlGn',\n",
+    "            annot=True,\n",
+    "            linewidth=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the p-values associated with the correlation values using the code below.\n",
+    "\n",
+    "`from scipy.stats import pearsonr`\n",
+    "\n",
+    "`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n",
+    "\n",
+    "Getting the *p-value* associated with a correlation value is necessary since it acknowledges whether the observed correlation between variables is significant or not. Normally, significance is confirmed if the *p-value* is below a threshold.\n",
+    "\n",
+    "In this examples, all the p-values for the observed correlations were significant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using a scatter plot matrix to visualise correlations\n",
+    "plt.figure(figsize=(40,40))\n",
+    "sb.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\n",
+    "These variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n",
+    "\n",
+    "Variables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This confirms that NT_EFC195 is a categorical variable with two classes 0 and 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[['CLASS','NT_EFC195']].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NT_EFC195 assumes both values irrespective of class\n",
+    "\n",
+    "Getting the associated p-values to check the correlation of features with the class. \n",
+    "The value of 1 at the bottom should be ignored since these values are obtained from the last column of a matrix of pairwise correlations therefore it corresponds to self correlation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "data.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the distribution and skewness of variables\n",
+    "plt.figure(figsize=(10,6))\n",
+    "data.skew().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of the variables are minimally skewed except NT_EFC195. Further checks will be done to try to understand the properties of this variable.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.groupby('NT_EFC195').size() # majority of the instances are of Class 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The skewedness in this variable can be understood by having most of its values at zeros\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n",
+    "\n",
+    "Tranformation possibilities\n",
+    "* using the minimum and maximum scaler\n",
+    "* standardisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Data transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Better performance of algorithms can be obtained if the data is transformed.\n",
+    "Some algorithms are may take features with large values as the most important features in the predictions which creates bias within predictions since predictions are majorly based on that variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Seperating the predictor variables from the target variable. Transformation should not be applied on the target variable.\n",
+    "\n",
+    "Operations on data are carried out on numpy ndArrays. The pandas dataframe is first converted to an ndArray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataArray = data.to_numpy()\n",
+    "\n",
+    "# seperating the predictor and response variables\n",
+    "target = dataArray[:,11]\n",
+    "predictors = dataArray[:,0:11]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numeric data may be rescaled (all values are put between a specified range) or standardized to have a mean of zero for normally distributed data.\n",
+    "Transformations have the advantage of improving performance of machine learning classifiers.\n",
+    "\n",
+    "Rescaling is done using the MinMaxScaler while standardising is done using the StandardScaler() functions. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using minMaxScaler to set all values between 0 and 1\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "rescaledPredictors = scaler.fit_transform(predictors)\n",
+    "                        \n",
+    "\n",
+    "# using StandardScaler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler1 = StandardScaler().fit(predictors)\n",
+    "standardizedPredictors = scaler1.transform(predictors)\n",
+    "                        \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Feature selection\n",
+    "This is done to be able to know which features contribute significantly to prediction of class. Once these features are known, they can be used to predict class. This has the advantage of reducing training time, preventing overfitting as well as improving accuracy of the model since redundant features are not used in the prediction process.\n",
+    "\n",
+    "On this dataset,univariate statistics such as F-test was used as an alternative to chi-squared test (some values after transformation are zero and chi2 returns an error)\n",
+    "\n",
+    "The F-test and feature selection were done on both the original and transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# untransformed data\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif\n",
+    "bestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "fit = bestFeatures.fit(predictors, target)\n",
+    "\n",
+    "scores = pd.DataFrame(fit.scores_) \n",
+    "pvalues = pd.DataFrame(fit.pvalues_)\n",
+    "columns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "featureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\n",
+    "featureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n",
+    "\n",
+    "print(featureValues.nlargest(7, 'score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the transformed data\n",
+    "\n",
+    "# rescaledPredictors\n",
+    "reBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "reFit = reBestFeatures.fit(rescaledPredictors, target)\n",
+    "\n",
+    "reScores = pd.DataFrame(reFit.scores_) \n",
+    "rePvalues = pd.DataFrame(reFit.pvalues_)\n",
+    "reColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "reFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\n",
+    "reFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "# standardizedPredictors\n",
+    "stBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "stFit = stBestFeatures.fit(standardizedPredictors, target)\n",
+    "\n",
+    "stScores = pd.DataFrame(stFit.scores_) \n",
+    "stPvalues = pd.DataFrame(stFit.pvalues_)\n",
+    "stColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "stFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\n",
+    "stFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n",
+    "\n",
+    "print(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The importance of different features can be ranked using the ExtraTressClassifier which is also known as the Extremely Randomized Trees. In this Extra Trees classifier, the features and splits are selected at random; hence, “Extremely Randomized Tree”. Since splits are chosen at random for each feature in the Extra Trees Classifier, it’s less computationally expensive than a Random Forest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using feature importance\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "model = ExtraTreesClassifier()\n",
+    "model.fit(predictors, target)\n",
+    "print(model.feature_importances_)\n",
+    "\n",
+    "# visualising feature importance\n",
+    "importances = pd.Series(model.feature_importances_, index=data.columns[0:11])\n",
+    "importances.nlargest(10).plot(kind='barh')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Building the classification model\n",
+    "Finding te right model for a given dataset is an iterative methods. It is advisable to pursue all combinations of data and algorithms to find the best algorithm that is able to offer satisfactory results on a given dataset.\n",
+    "It cannot be expected that an algorithm that performs well on a particular dataset will perform the same eay in another dataset.\n",
+    "Therefore, an exhaustive approach is necessary to find the best performing algorithm for a given dataset.\n",
+    "\n",
+    "Some of these combinations are (for original and transformed data):\n",
+    "- model + train/test sets\n",
+    "- model + kfold crossvalidation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\n",
+    "from sklearn.model_selection import train_test_split # random split\n",
+    "from sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\n",
+    "p_train, p_test, t_train, t_test = train_test_split(predictors, target, \n",
+    "                                                    test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on untransformed data\n",
+    "logit.fit(p_train, t_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Measuring model performance\n",
+    "Logistic regression is a supervised learning algorithm. Performance of the algorithm is measured as the number of correct predictions made by the algorithm out the the number of true class values.\n",
+    "We can measure the performance of a classification problem using precison, F1 Score, Receiver Operating Curve (ROC) curve, accuracy and Matthews Correlation Coefficient.\n",
+    "\n",
+    "Other performance metric are;\n",
+    "- True Positives (TP) / True Positive Rate (TPR): Number of correct positive predictions / Probability of predicting positive given that the actual class is positive\n",
+    "- False Negatives (FN) / False Negative Rate (FNR): Number of wrong negative predictions / Probability of predicting negative given that the actual class is positive\n",
+    "- True Negatives (TN) / True Negative Rate (TNR): Number of correct negative predictions / Probability of predicting negative given that the actual class is negative\n",
+    "- False Positives (FP) / False Positive Rate (FPR): Number of wrong positive predictions / Probability of predicting positive given that the actual class is negative\n",
+    "- Precision (P): Proportion of predicted positives that are correct\n",
+    "- Recall (R): Proportion of actual positives captured"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict on test data\n",
+    "predictions = logit.predict(p_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the confusion matrix\n",
+    "from sklearn import metrics\n",
+    "cm = metrics.confusion_matrix(t_test, predictions)\n",
+    "cm\n",
+    "sb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n",
+    "          square=True, cmap='Blues') \n",
+    "plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n",
+    "                                                      # higher values are better\n",
+    "# not affected by unbalanced classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Performance metrics are inter-related and can be visualised using an ROC curve where the true positive rate is plotted against the false positive rate. Essentially, the curve shows the tradeoff between sensitivity and specificity.\n",
+    "The maximum area under the curve (AUC) is one which represents 100% accuracy therefore higher values of AUC are desired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\n",
+    "fpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\n",
+    "auc = metrics.roc_auc_score(t_test, pred_probs)\n",
+    "plt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('tpr'), plt.xlabel('fpr')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The confusion matrix above shows the number of true positives, true negatives, false positives as well as false negatives.\n",
+    "The model made a total of 75 wrong class predictions. It can be seen that logistic regression has a good performance of the original dataset where all features are utilised in the prediction of the class.\n",
+    "\n",
+    "Something to keep in mind is that a good performance of the training set by an algorithm is not an indicator of how the model will performance when new data is presented to it. However, a good performance is desirable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance on new data\n",
+    "new_pred = logit.predict(new.values)\n",
+    "\n",
+    "pred_df = pd.DataFrame(new_pred) \n",
+    "pred_df.columns=[\"CLASS\"]\n",
+    "pred_df.index.name=\"Index\" \n",
+    "pred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df.to_csv(\"ilujumba.csv\") \n",
+    "print(pred_df['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the csv file was submitted to the competition, the model had an 83.33% accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Machine learning is an iterative process\n",
+    "\n",
+    "Different combinations of logistic regression with the data were tried.\n",
+    "- train_test split\n",
+    "\n",
+    "    model + rescaled data\n",
+    "    \n",
+    "    model + standardised data\n",
+    "\n",
+    "\n",
+    "- kfold crossvalidation\n",
+    "\n",
+    "    model + rescaled data\n",
+    "    \n",
+    "    model + standardised data\n",
+    "\n",
+    "#### Logistic regression on rescaled data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit1 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit1.fit(p1_train, t1_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions1 = logit1.predict(p1_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t1_test, predictions1))\n",
+    "\n",
+    "# rescaling new data\n",
+    "newArray = new.to_numpy()\n",
+    "rescaledNew = scaler.fit_transform(newArray)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance on new data (rescaled)\n",
+    "new_pred1 = logit1.predict(rescaledNew)\n",
+    "\n",
+    "pred_df1 = pd.DataFrame(new_pred1) \n",
+    "pred_df1.columns=[\"CLASS\"]\n",
+    "pred_df1.index.name=\"Index\" \n",
+    "pred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df1.to_csv(\"ilujumba1.csv\") \n",
+    "print(pred_df1['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df1.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df1.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This combination returned an 88.11% accuracy after submission."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on standardized data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit2 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit2.fit(p2_train, t2_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions2 = logit2.predict(p2_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\n",
+    "print('MCC: ',matthews_corrcoef(t2_test, predictions2))\n",
+    "\n",
+    "# standardizing new data\n",
+    "standardizedNew = scaler1.transform(newArray)\n",
+    "\n",
+    "# performance on new data (standaridized)\n",
+    "new_pred2 = logit2.predict(standardizedNew)\n",
+    "\n",
+    "pred_df2 = pd.DataFrame(new_pred2) \n",
+    "pred_df2.columns=[\"CLASS\"]\n",
+    "pred_df2.index.name=\"Index\" \n",
+    "pred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df2.to_csv(\"ilujumba2.csv\") \n",
+    "print(pred_df2['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df2.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df2.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model streategy returned an 86.03% accuracy after submission which is better than when the original data is used but slightly lower than when rescaled features are used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using selected features, rescaled data and Logistic regression\n",
+    "\n",
+    "An attempt was made to manually select for features that showed the highest importance in the prediction of class. All further strategies on data using Logistic Regression were done using rescaled data with the assumption that it could provide better model performance results when compared to other transformations of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit3 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit3.fit(p3_train, t3_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions3 = logit3.predict(p3_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t3_test, predictions3))\n",
+    "\n",
+    "# rescaling new data\n",
+    "# newArray = new.to_numpy()\n",
+    "# rescaledNew = scaler.fit_transform(newArray)\n",
+    "\n",
+    "# performance on new data (rescaled)\n",
+    "new_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n",
+    "\n",
+    "pred_df3 = pd.DataFrame(new_pred3) \n",
+    "pred_df3.columns=[\"CLASS\"]\n",
+    "pred_df3.index.name=\"Index\" \n",
+    "pred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df3.to_csv(\"ilujumba3.csv\") \n",
+    "print(pred_df3['CLASS'].unique())\n",
+    "\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df3.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df3.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using cross-validation and Logistic Regression\n",
+    "\n",
+    "During cross-validation, the data is split into kfolds. k-1 folds are used in the training are used while the kth fold is used for testing. The process is repeated for k times until each fold has been used as a testing set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42)\n",
+    "model6 = LogisticRegression()\n",
+    "model6.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model6, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model6_pred = model6.predict(rescaledNew)\n",
+    "df6 = pd.DataFrame(model6_pred)\n",
+    "df6.columns = ['CLASS']\n",
+    "df6.index.name = 'Index'\n",
+    "df6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n",
+    "\n",
+    "df6.to_csv('ilujumba7.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another thing to keep in mind is to exhaustive use of simple models on the data. The reason for this is that they have a good reputation in terms of performance on different types of data and their method of prediction is well understood. \n",
+    "\n",
+    "\n",
+    "Another simple algorithm for classification is the Naive Bayes classifier. This algorithm assumes that all features are independent of each other and each feature contributes equally to the resulting class. For this reason, it is called 'naive'.\n",
+    "\n",
+    "#### Naive Bayes classifier with kfold cross-validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model7 = GaussianNB()\n",
+    "model7.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model7, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model7_pred = model7.predict(newArray)\n",
+    "df7 = pd.DataFrame(model7_pred)\n",
+    "df7.columns = ['CLASS']\n",
+    "df7.index.name = 'Index'\n",
+    "df7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df7.to_csv('ilujumba7.csv')\n",
+    "print(df7['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df7.groupby('CLASS').size()[0].sum())\n",
+    "print(df7.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier on rescaled features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model8 = GaussianNB()\n",
+    "model8.fit(rescaledPredictors, target)\n",
+    "\n",
+    "results1 = cross_val_score(model8, rescaledPredictors, target)\n",
+    "print(results1.mean())\n",
+    "\n",
+    "model8_pred = model8.predict(rescaledNew)\n",
+    "df8 = pd.DataFrame(model8_pred)\n",
+    "df8.columns = ['CLASS']\n",
+    "df8.index.name = 'Index'\n",
+    "df8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df8.to_csv('ilujumba8.csv')\n",
+    "print(df8['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df8.groupby('CLASS').size()[0].sum())\n",
+    "print(df8.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes and kfold validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model9 = GaussianNB()\n",
+    "model9.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\n",
+    "print('mean for results', results.mean())\n",
+    "\n",
+    "predic = cross_val_predict(model9, predictors, target, cv =10)\n",
+    "accuracy = metrics.r2_score(target, predic)\n",
+    "print('cross-predicted accuracy ', accuracy)\n",
+    "\n",
+    "model9_pred = model9.predict(newArray)\n",
+    "df9 = pd.DataFrame(model9_pred)\n",
+    "df9.columns = ['CLASS']\n",
+    "df9.index.name = 'Index'\n",
+    "df9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df9.to_csv('ilujumba9.csv')\n",
+    "print(df9['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df9.groupby('CLASS').size()[0].sum())\n",
+    "print(df9.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model strategy returned the highest score after submission with a 99.599% accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing several algorithms to look at the nature of the decision boundaries created"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://medium.com/cascade-bio-blog/creating-visualizations-to-better-understand-your-data-and-models-part-2-28d5c46e956"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Algorithms define a set of hyperplanes that divide the datapoints to their respective classes and span the feature space trained on. Visualising enables one to understand the limitations of a given algorithm on a dataset given to it.\n",
+    "Thus decision boundaries enable one to understand to how the training data selected affects performance of the algorithm."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ten sklearn non-linear classifier algorithms were compared. KNearest Neighbors, Support Vector Machines (Linear and RBF kernels), Gaussian Process Classifier, Decision Trees, Random Forest, Neural Networks, AdaBoost, Naive Bayes, \"Quadratic Discriminant Analysis, Logistic Regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing classifiers from the sklearn library\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPClassifier #1\n",
+    "from sklearn.neighbors import KNeighborsClassifier #2\n",
+    "from sklearn.svm import SVC #3\n",
+    "from sklearn.gaussian_process import GaussianProcessClassifier #4\n",
+    "from sklearn.gaussian_process.kernels import RBF #5\n",
+    "from sklearn.tree import DecisionTreeClassifier #6\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\n",
+    "from sklearn.naive_bayes import GaussianNB #9\n",
+    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\n",
+    "from sklearn.linear_model import LogisticRegression #11\n",
+    "\n",
+    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
+    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
+    "         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n",
+    "\n",
+    "classifiers = [\n",
+    "    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n",
+    "    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n",
+    "    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n",
+    "    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n",
+    "    DecisionTreeClassifier(),\n",
+    "    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n",
+    "    MLPClassifier(max_iter=1000), #iterations until converge\n",
+    "    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n",
+    "    GaussianNB(), #NaiveBayes\n",
+    "    QuadraticDiscriminantAnalysis(),\n",
+    "    LogisticRegression()]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dimensionality reduction\n",
+    "\n",
+    "https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/\n",
+    "\n",
+    "https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the data is multi-dimensional, it was reduced using Principal Component Analysis (PCA) to reduce it to two components.\n",
+    "Trial runs were done to check how much of the variation in the data is explained by the principal components.\n",
+    "\n",
+    "Another thing to keep in mind is that PCA works best on standardised/normalised data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preprocessing the dataset\n",
+    "dataArray = data.to_numpy()\n",
+    "X, y = dataArray[:,0:11], dataArray[0:,11]\n",
+    "X = StandardScaler().fit_transform(X)\n",
+    "\n",
+    "# reducing dimensions of the dataset using PCA  \n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA()\n",
+    "pca.fit_transform(X)\n",
+    "pca_variance = pca.explained_variance_\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Variance ratio')\n",
+    "plt.xlabel('Principal components')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\n",
+    "ninety_five = pca2.fit_transform(X)\n",
+    "ninety_five.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eight features explain 95% of the variance in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca2 = PCA(3) # keeping features three principal components\n",
+    "principalComponents = pca2.fit_transform(X)\n",
+    "\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "plt.figure(figsize=(10,6))\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n",
+    "           linewidths=1, alpha=.5,\n",
+    "           edgecolor='k', s= 200,\n",
+    "           c=data['CLASS'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The three pincipal components wete visualised using a 3D plot. The figure above shows clustering of the three components. Each component is not exactly independent of the others so the clusters overlap to some extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#converting principal component ndarrays to DataFrame format\n",
+    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\n",
+    "finalDf = pd.concat([principalDf, data['CLASS']], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finalDf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualising the top 2 principal components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize = (6,6))\n",
+    "ax = fig.add_subplot(111) \n",
+    "ax.set(xlim=(-10,10), ylim=(-10,10))\n",
+    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
+    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
+    "ax.set_title('top 2 components', fontsize = 20)\n",
+    "\n",
+    "targets = [0, 1]\n",
+    "colors = ['r', 'g']\n",
+    "\n",
+    "for target, color in zip(targets,colors):\n",
+    "    indices = finalDf['CLASS'] == target\n",
+    "    ax.scatter(finalDf.loc[indices, 'PC1']\n",
+    "               , finalDf.loc[indices, 'PC2']\n",
+    "               , c = color\n",
+    "               , s = 50, alpha = 0.4)\n",
+    "ax.legend(targets)\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting the into training and test part\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A mesh grid is required. This can be thought of as a matrix of coordinates upon which the model will make decisions.\n",
+    "These are then visualised to reveal decision boundaries.\n",
+    "The mesh grip was created based on the data and a step size of 0.02"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating mesh for the contour plot\n",
+    "\n",
+    "h = .02  # step size in the mesh\n",
+    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
+    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two principal components were used to enable visualisation on a scatter plot.\n",
+    "\n",
+    "The parameters for the PCA were generated on the training data and these were applied on both the training and training sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca4 = PCA(n_components=2)\n",
+    "\n",
+    "# applying PCA on training set\n",
+    "pca4.fit(X_train)\n",
+    "\n",
+    "#applying transform on training and testing sets\n",
+    "train_ = pca4.transform(X_train)\n",
+    "test_ = pca4.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_.shape, test_.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After transforming the data and the creating the meshgrid, decision boundaries for the algorithms were created by iterating over the classifiers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "figure = plt.figure(figsize=(27, 15))\n",
+    "i = 1\n",
+    "\n",
+    "datasets=[data]\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
+    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "        # Plot the top 2 principal components for training data\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n",
+    "                    edgecolors='k')\n",
+    "        # Plot the top 2 principal components for the testing data\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
+    "                    edgecolors='k')\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        i += 1\n",
+    "\n",
+    "        # iterate over classifiers\n",
+    "\n",
+    "    for name, clf in zip(names, classifiers):\n",
+    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "        clf.fit(train_, y_train)\n",
+    "        score = clf.score(test_, y_test)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n",
+    "        # Plot the testing points\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.4)\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(name)\n",
+    "            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n",
+    "            i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Accuracies of the different algorithms are indicated on the lower right corner.\n",
+    "\n",
+    "The plots show training points in solid colors and testing points semi-transparent.Decision boundaries for GaussianProcessClassifier, RandomForest and AdaBoost are complicated while the decison boundaries for LogisticRegression, NaiveBayes, NeuralNetwork, and Linear SVM are simpler. GaussianProcess and RBF SVM have contoured decision boundaries which seperate points with similar characteristics.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Thur 20 Feb/ACE_ClassML Pipeline.ipynb b/Thur 20 Feb/ACE_ClassML Pipeline.ipynb
index e55fe6d..0f344f5 100644
--- a/Thur 20 Feb/ACE_ClassML Pipeline.ipynb	
+++ b/Thur 20 Feb/ACE_ClassML Pipeline.ipynb	
@@ -48,7 +48,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Users/drjamesmugume/Desktop/ACE CLASS NOTES/Thur 20 Feb\r\n"
+      "/Users/drjamesmugume/Desktop/ACE CLASS NOTES/Thur 20 Feb\n"
      ]
     }
    ],
@@ -65,8 +65,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ACE_ClassML Pipeline.ipynb \u001b[31mhousing.csv\u001b[m\u001b[m\r\n",
-      "\u001b[31mdiabetes.csv\u001b[m\u001b[m\r\n"
+      "ACE_ClassML Pipeline.ipynb \u001b[31mhousing.csv\u001b[m\u001b[m\n",
+      "\u001b[31mdiabetes.csv\u001b[m\u001b[m\n"
      ]
     }
    ],
@@ -768,9 +768,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2890,9 +2888,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3865,7 +3861,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   },
   "varInspector": {
    "cols": {
@@ -3898,5 +3894,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From 00d885b01b4e823208b29abc6c170118ecd7b914 Mon Sep 17 00:00:00 2001
From: Ibra Lujumba <ibra.lujumba@gmail.com>
Date: Mon, 16 Mar 2020 16:02:55 +0300
Subject: [PATCH 09/10] Conflicts

Resolved
---
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 Assignment Colab/.ipynb_checkpoints/.DS_Store |  Bin 0 -> 6148 bytes
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 Assignment Colab/ilujumba7.csv                |  759 ------
 Assignment Colab/ilujumba8.csv                |  759 ------
 Assignment Colab/ilujumba9.csv                |  759 ------
 11 files changed, 7392 deletions(-)
 create mode 100644 Assignment Colab/.ipynb_checkpoints/.DS_Store
 delete mode 100644 Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle_final-checkpoint.ipynb
 delete mode 100644 Assignment Colab/ilujumba.csv
 delete mode 100644 Assignment Colab/ilujumba1.csv
 delete mode 100644 Assignment Colab/ilujumba2.csv
 delete mode 100644 Assignment Colab/ilujumba3.csv
 delete mode 100644 Assignment Colab/ilujumba7.csv
 delete mode 100644 Assignment Colab/ilujumba8.csv
 delete mode 100644 Assignment Colab/ilujumba9.csv

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diff --git a/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle_final-checkpoint.ipynb b/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle_final-checkpoint.ipynb
deleted file mode 100644
index 6d19baa..0000000
--- a/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle_final-checkpoint.ipynb	
+++ /dev/null
@@ -1,2079 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ACE_Uganda Kaggle competition 1\n",
-    "### by Ibra Lujumba"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Exploratory Data Analysis "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Importing python modules for data analysis and visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np # manipulation of arrays\n",
-    "import pandas as pd # manipulating dataframes\n",
-    "import matplotlib.pyplot as plt # data visualisation\n",
-    "import seaborn as sb # data visualisation,it is based on plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#ignoring warnings that may arise\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###### Importing the datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AMP_TrainSet.csv  Test.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls ../input/ace-class-assignment/\n",
-    "\n",
-    "# reading in the data\n",
-    "data = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\n",
-    "new = pd.read_csv('../input/ace-class-assignment/Test.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Checking the dimensions of the data as well as the datatype of each column"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((3038, 12), (758, 11))"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# checking dimensions of the datasets\n",
-    "data.shape, new.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(FULL_Charge           float64\n",
-       " FULL_AcidicMolPerc    float64\n",
-       " FULL_AURR980107       float64\n",
-       " FULL_DAYM780201       float64\n",
-       " FULL_GEOR030101       float64\n",
-       " FULL_OOBM850104       float64\n",
-       " NT_EFC195               int64\n",
-       " AS_MeanAmphiMoment    float64\n",
-       " AS_DAYM780201         float64\n",
-       " AS_FUKS010112         float64\n",
-       " CT_RACS820104         float64\n",
-       " CLASS                   int64\n",
-       " dtype: object,\n",
-       " FULL_Charge           float64\n",
-       " FULL_AcidicMolPerc    float64\n",
-       " FULL_AURR980107       float64\n",
-       " FULL_DAYM780201       float64\n",
-       " FULL_GEOR030101       float64\n",
-       " FULL_OOBM850104       float64\n",
-       " NT_EFC195               int64\n",
-       " AS_MeanAmphiMoment    float64\n",
-       " AS_DAYM780201         float64\n",
-       " AS_FUKS010112         float64\n",
-       " CT_RACS820104         float64\n",
-       " dtype: object)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# checking the datatypes of the variables\n",
-    "data.dtypes, new.dtypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "All the values in all the variables exists as either floats or integers.\n",
-    "\n",
-    "Proceeding to work with the training dataset to build the classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>FULL_Charge</th>\n",
-       "      <th>FULL_AcidicMolPerc</th>\n",
-       "      <th>FULL_AURR980107</th>\n",
-       "      <th>FULL_DAYM780201</th>\n",
-       "      <th>FULL_GEOR030101</th>\n",
-       "      <th>FULL_OOBM850104</th>\n",
-       "      <th>NT_EFC195</th>\n",
-       "      <th>AS_MeanAmphiMoment</th>\n",
-       "      <th>AS_DAYM780201</th>\n",
-       "      <th>AS_FUKS010112</th>\n",
-       "      <th>CT_RACS820104</th>\n",
-       "      <th>CLASS</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "      <td>3038.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.060237</td>\n",
-       "      <td>8.521520</td>\n",
-       "      <td>0.971410</td>\n",
-       "      <td>73.668760</td>\n",
-       "      <td>0.994007</td>\n",
-       "      <td>-2.432927</td>\n",
-       "      <td>0.088545</td>\n",
-       "      <td>15.683233</td>\n",
-       "      <td>73.650828</td>\n",
-       "      <td>5.911361</td>\n",
-       "      <td>1.235255</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>3.819929</td>\n",
-       "      <td>7.586652</td>\n",
-       "      <td>0.107413</td>\n",
-       "      <td>8.527489</td>\n",
-       "      <td>0.031333</td>\n",
-       "      <td>1.707223</td>\n",
-       "      <td>0.284133</td>\n",
-       "      <td>11.575665</td>\n",
-       "      <td>9.166092</td>\n",
-       "      <td>0.693689</td>\n",
-       "      <td>0.210012</td>\n",
-       "      <td>0.500082</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-16.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.684000</td>\n",
-       "      <td>42.750000</td>\n",
-       "      <td>0.866000</td>\n",
-       "      <td>-10.432000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.041000</td>\n",
-       "      <td>42.778000</td>\n",
-       "      <td>3.533000</td>\n",
-       "      <td>0.785000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.516000</td>\n",
-       "      <td>0.895000</td>\n",
-       "      <td>68.294000</td>\n",
-       "      <td>0.974000</td>\n",
-       "      <td>-3.606000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.587500</td>\n",
-       "      <td>67.556000</td>\n",
-       "      <td>5.459250</td>\n",
-       "      <td>1.082000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>7.143000</td>\n",
-       "      <td>0.963000</td>\n",
-       "      <td>74.059500</td>\n",
-       "      <td>0.994000</td>\n",
-       "      <td>-2.296500</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>14.988500</td>\n",
-       "      <td>73.697000</td>\n",
-       "      <td>5.925500</td>\n",
-       "      <td>1.184000</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>13.158000</td>\n",
-       "      <td>1.041000</td>\n",
-       "      <td>79.343750</td>\n",
-       "      <td>1.011000</td>\n",
-       "      <td>-1.283250</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>26.807750</td>\n",
-       "      <td>79.778000</td>\n",
-       "      <td>6.382000</td>\n",
-       "      <td>1.351000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>30.000000</td>\n",
-       "      <td>46.667000</td>\n",
-       "      <td>1.451000</td>\n",
-       "      <td>101.682000</td>\n",
-       "      <td>1.196000</td>\n",
-       "      <td>3.576000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>51.280000</td>\n",
-       "      <td>103.167000</td>\n",
-       "      <td>8.662000</td>\n",
-       "      <td>2.192000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       FULL_Charge  FULL_AcidicMolPerc  FULL_AURR980107  FULL_DAYM780201  \\\n",
-       "count  3038.000000         3038.000000      3038.000000      3038.000000   \n",
-       "mean      2.060237            8.521520         0.971410        73.668760   \n",
-       "std       3.819929            7.586652         0.107413         8.527489   \n",
-       "min     -16.000000            0.000000         0.684000        42.750000   \n",
-       "25%       0.000000            2.516000         0.895000        68.294000   \n",
-       "50%       2.000000            7.143000         0.963000        74.059500   \n",
-       "75%       4.000000           13.158000         1.041000        79.343750   \n",
-       "max      30.000000           46.667000         1.451000       101.682000   \n",
-       "\n",
-       "       FULL_GEOR030101  FULL_OOBM850104    NT_EFC195  AS_MeanAmphiMoment  \\\n",
-       "count      3038.000000      3038.000000  3038.000000         3038.000000   \n",
-       "mean          0.994007        -2.432927     0.088545           15.683233   \n",
-       "std           0.031333         1.707223     0.284133           11.575665   \n",
-       "min           0.866000       -10.432000     0.000000            0.041000   \n",
-       "25%           0.974000        -3.606000     0.000000            5.587500   \n",
-       "50%           0.994000        -2.296500     0.000000           14.988500   \n",
-       "75%           1.011000        -1.283250     0.000000           26.807750   \n",
-       "max           1.196000         3.576000     1.000000           51.280000   \n",
-       "\n",
-       "       AS_DAYM780201  AS_FUKS010112  CT_RACS820104        CLASS  \n",
-       "count    3038.000000    3038.000000    3038.000000  3038.000000  \n",
-       "mean       73.650828       5.911361       1.235255     0.500000  \n",
-       "std         9.166092       0.693689       0.210012     0.500082  \n",
-       "min        42.778000       3.533000       0.785000     0.000000  \n",
-       "25%        67.556000       5.459250       1.082000     0.000000  \n",
-       "50%        73.697000       5.925500       1.184000     0.500000  \n",
-       "75%        79.778000       6.382000       1.351000     1.000000  \n",
-       "max       103.167000       8.662000       2.192000     1.000000  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# getting the descriptive statistics of the train dataset such as arithmetic mean, \n",
-    "# standard deviation, quartiles and number of non-NA values in each column \n",
-    "data.describe()"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\n",
-    "AS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\n",
-    "For all the variables, the data points are not widely spread"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CLASS\n",
-       "0    1519\n",
-       "1    1519\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# checking the proprotions of classses\n",
-    "data.groupby('CLASS').size()"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "Both classes have an equal number of entries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f817a5bfd68>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# obtaining pairwise correlation values for the variables in the train dataset\n",
-    "# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\n",
-    "pearsoncorr = data.corr(method='pearson')\n",
-    "\n",
-    "# visualizing the correlation matrix as a heatmap to make interpretation easier\n",
-    "plt.figure(figsize=(10,10))\n",
-    "top_corr = pearsoncorr.index\n",
-    "sb.heatmap(pearsoncorr, \n",
-    "            xticklabels=pearsoncorr.columns,\n",
-    "            yticklabels=pearsoncorr.columns,\n",
-    "            cmap='RdYlGn',\n",
-    "            annot=True,\n",
-    "            linewidth=0.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can get the p-values associated with the correlation values using the code below.\n",
-    "\n",
-    "`from scipy.stats import pearsonr`\n",
-    "\n",
-    "`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n",
-    "\n",
-    "In this example, all values were too low to be informative"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# using a scatter plot matrix to visualise correlations\n",
-    "# plt.figure(figsize=(60,60))\n",
-    "# sb.pairplot(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\n",
-    "These variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n",
-    "\n",
-    "Variables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2830, array([0, 1]))"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()\n",
-    "\n",
-    "#this confirms that NT_EFC195 is a categorical variable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CLASS</th>\n",
-       "      <th>NT_EFC195</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   CLASS  NT_EFC195\n",
-       "0      1          0\n",
-       "1      1          1\n",
-       "2      1          0\n",
-       "3      1          0\n",
-       "4      1          0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[['CLASS','NT_EFC195']].head() #NT_EFC195 assumes both values irrespective of class\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "FULL_Charge           3.404241e-224\n",
-       "FULL_AcidicMolPerc    4.220004e-295\n",
-       "FULL_AURR980107       1.887905e-277\n",
-       "FULL_DAYM780201       6.997934e-245\n",
-       "FULL_GEOR030101        2.669597e-48\n",
-       "FULL_OOBM850104       7.441087e-154\n",
-       "NT_EFC195              2.189203e-48\n",
-       "AS_MeanAmphiMoment     0.000000e+00\n",
-       "AS_DAYM780201         4.911002e-142\n",
-       "AS_FUKS010112          6.540694e-02\n",
-       "CT_RACS820104          5.329249e-51\n",
-       "CLASS                  1.000000e+00\n",
-       "Name: CLASS, dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# getting the associated p-values. The value of 1 at the bottom should be ignored \n",
-    "from scipy.stats import pearsonr\n",
-    "data.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f817a0b9048>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# checking the distribution and skewness of variables\n",
-    "plt.figure(figsize=(10,6))\n",
-    "data.skew().plot(kind='bar')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "NT_EFC195\n",
-       "0    2769\n",
-       "1     269\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.groupby('NT_EFC195').size() # majority of the instances are of Class 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a3d90f0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a3754e0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a39c940>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a344da0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2f6240>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a31e6a0>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2c7b00>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a270f98>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a270fd0>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f817a2a2860>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a08ecc0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f817a041160>]],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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tqWanT2GZmZnk5+fHO4y46dD78fBuqxnU7YS0KujiWzrNeRjs6eAIbxSjSiyxLBO+aW2qfD+m+b40y1EpJ5rNoK2ug2iMKfPta/yx+lzgTWNMpW//m8BEoCSK8UaVf3mp7Aw7mYEBBm2vTUhLS2PQoEERjU2p9urQ+/Gvl8NRY2Dnasg/CSY/A1++BoungiML7t4GDh0Np1rmqxhYBQwBnjTGpETbtbaeqGDRbAYNZx3EDp2bTOsg1vgmwM1Od5CZbv3aa3WAgerM3PVW7Vl6V6j3VY584Zvo2F0LZf8Xv9hU0jDGeIwxRVgrgpwkIqOC9yfTfUKp5kQzWQtnHcQOnWuMedoYU2yMKc7Ly2tTcLF2pGbNEeiz5tRkTXVmnnqwp0F6tm++NWDvl5B/IiCwY1Vcw1PJxRhzAHgPqxUmeHvS3CeCaZ81FSyayVo46yBG49yE5O+zFtwM2p4BBkqlDI8T7BmQkQP11dbo0MqvrabR3kNhx+p4R6gSnIjkiUgP3+Ms4GxgY3yjUiryopmshbMOYnNeB84RkZ4i0hM4x7ctafnXAc3OcJBmF+w2adcAA6VShsdlDSRIz7GaQWsqoO4g5A6Bo0bBniRf+lTFQj/gXRH5FOue86Yx5t9xjikitM+aCha1AQbGGLeI+NdBtAPP+tdBBEqNMUtE5ETgJaAncL6I3GeMGWmMqRSR32MVPoDf+QcbJKtqf81augMRIdNha/c8a0qlBLfTGkCQkQPOKmv5KYDcwVbS9vm/fMfoqFAVmjHmU+D4eMcRDdoMqoJFdVJcY8xSYGmjbb8NevwJVhNnqHOfBZ6NZnyxVO3rs9Ylw2oCzUq36wAD1XkZY61cEKhZq7aaQAF6DYa6Q4CBym+gz7C4hqqUUvEW1Ulx1RHVQaNBATIcdl1uSnVennrreyBZOwwVm0Hs0PMYq3YNrG1KdUJas6aC6XJTMVJT7yEzzYbdZg10zUyzabKmOq/gZM1mt9YG/Xa9lajZ0zRZU52WIBiM9llTDWiyFiNVTjc5GUd+3Vnpdu2zpjovty9Zc2T4lpwCdq2Dvr4psjK7Q3YfqNgUn/iUUiqBaDNojNQ43XRJP5KsZWozqOrMAjVraZDV03p8eNeRGjWwRoVWbIl9bEoplWA0WYuRKqeH7KCatcw0TdZUJ+ZxWt/tGZDT98j2XsHJ2mCtWVOdljaDqmCarMVITb2bbN8C7mD1WavVZlDVWXlc1ndHBnQ96sj23oVHHucOgeq9UHsgtrEpFUciVr9mHWCggmmyFiPVTjddGtWs6XJTqtNy+2vW0qBrvyPbjxp95HHuEOu7f0oPpToBQZM11ZQmazFSXe8hJyO4Zk2bQVUnFtwMmpEDg06Do8dBdu8jx/hr2So0WVNKdW46GjRGmgwwSLNR59ZmUNVJ+ZtB7WnW9ysWWxPlButZYI0U1ek7VCekfdZUME3WYqTx1B06GlR1av5mUP9SUjZ702McGdBjIOzTQQaq89FmUBVMm0FjwBhDTb2HLulNm0H105PqlAI1a+ktH5c7RGvWVKfi77OmVLCwkjURWSQiPxARTe7awen24vaaBlN3ZKXb8Rqo92hTaDKaPHkyr776Kl6v/v3aJdBnLZxk7WvQ33PK0/tMQ/pBXgULt1D8DfgxsElEZoiIrqzcBjX1VnNn8NQdGQ7rV6+rGCSnG264geeff57CwkLuvvtuNm7cGNZ5IjJRRL4Ukc0icneI/RkissC3f6WIFDTaP1BEqkTkjoi8kHgJXm6qJX1GgKsa9n8T/ZhUvOl9BvBXrGkzqAoWVrJmjHnLGPMTYBxQBrwpIv8RkZ+KSFo0A0wF1U5rEffGU3cAOn1Hkjr77LOZN28eq1evpqCggAkTJvCd73yHf/7zn7hcrpDniIgdeBL4PjACuExERjQ67BpgvzFmCPAY8MdG+x8DXovoi4mH4OWmWtJvrPV917roxqPiTu8zlsDUHVqzpoKEXd0sIrnAVcC1wBrgcaxC9WZUIksh1fVWspYTIlnTmrXkVVFRwaxZs/jHP/7B8ccfz6233srq1auZMGFCc6ecBGw2xmwxxtQD84ELGh1zATDb93ghMF58s2SKyIXAFmB9xF9MrAUvN9WSPsPB5oDdn0Y/JhV3ep85QmvWVLCwRoOKyL+AYcBc4HxjzC7frgUiUhqt4FJFtdOqPevSaAUDgDq31qwlo4svvpiNGzdyxRVX8Morr9CvnzWx65QpUyguLm7utP7A9qCfy4GTmzvGGOMWkYNArojUAr8CJgDNNoGKyPXA9QADBw5s8+uKmeB51lriyLASNq1ZS3l6n1GqeeFO3fEPY8zS4A0ikmGMcRpjmr0zKYu/GTS4Zi0rULOmyVoyuvbaaznvvPMabHM6nWRkZFBaWhpYMqaRUBsbf3xu7pj7gMeMMVXNXNs60JingacBiouLE/ejeeN51lrSbyx8+Zo1D1sLr10lPb3PKNWMcJtB7w+xbUUkA0llNb5m0IaT4lrJWm29JmvJ6J577mmy7ZRTTmnttHJgQNDP+cDO5o4REQfQHajEqoF7SETKgF8C/yMiN7cj9MTQeJ61lvQrgpoKOFge3ZhUvOl9Bu2zpkJrsWZNRI7CapbJEpHjOfKpvxvQJcqxpYwqXzNodkaoZlDts5ZMdu/ezY4dO6itrWXNmjWBf6iHDh2ipqamtdM/AQpFZBCwA5iKNfot2BJgGtZN6hLgHWM9yff8B4jIdKDKGPNEBF5SfIQ7zxrA0cdb33eugR4DWj5WJZ1Uvs+4XC7Ky8upq6sL+5yHhz2MwVCxtYIDtgNRjE7FSmZmJvn5+aSltX+cTGvNoOdidfbMBx4N2n4Y+J92P2sn469ZC55nLcOhzaDJ6PXXX2fWrFmUl5dz2223BbZ37dqVP/zhDy2e6+uDdjPwOmAHnjXGrBeR3wGlxpglwExgrohsxqpRmxqt1xJXHicg1uCB1vQdZR23czWMmBT10FTMtfs+IyIDgDnAUYAXeNoY83h0wmy78vJyunbtSkFBQXNdI5owFQZjDIU9C0kP58OMSmjGGCoqKigvL2fQoEHtvk6L/ymNMbOB2SIy2RizqN3P0slV+fqsZYdoBtVkLblMmzaNadOmsWjRIiZPntzm8319cpY22vbboMd1wI9aucb0Nj9xovHUW7Vq4dzA0jKt+dZ2rol+XCrmOnifcQO3G2NWi0hXYJWIvGmM+SLykbZdXV1dmxI1lXpEhNzcXPbu3duh67TWDHq5MeY5oEBEbmu83xjzaIjTVCM1Tg82OdL0CdYKBgBOnbojqTz33HNcfvnllJWV8eijTd/+wbVtqgXu+vD6q/n1HwfrX9JBBimoI/cZ34jRXb7Hh0VkA1aTakIka4Amaioi74HW2iCyfd9zOvxMnVh1vZvsdEeDP1imbwWDWq1ZSyrV1dUAVFVVxTmSJOepD28kqN/R42DVLKjcArmDoxaWiouI3Gd8q30cD6zsYDxxJYjOsaaaaK0Z9O++7/fFJpzUVO10N+ivBtoMmqx+9rOfAXDvvffGOZIk53G2PsdasOBBBpqspZRI3GdEJAdYBPzSGHOo0b7kmHswSux2O6NHjw78vHjxYt577z1KS0t54okjY5TOOOMMHnnkEYqLiykoKKC0tJTevXsH9s+aNavJOS1Zs2YN48aNY9myZZx77rkAlJWV8cMf/pDPP/88cNz06dPJycnhjjvu4KqrruL999+ne/fuGGN49NFHGT9+fCC+Xbt2kZmZSXp6Os888wxFRUUALFiwgAceeACPx8MPfvADHnroIQC2bdvGtGnTOHDgAB6PhxkzZgSmXHrwwQeZOXMmdrudv/zlL4EYr776av7973/Tp0+fBnFWVlYyZcoUysrKKCgo4IUXXqBnz548/PDDzJs3DwC3282GDRvYu3cvvXr1CvMvFJ5wF3J/SES6iUiaiLwtIvtE5PKIRpLCqus9dAkaCQq6gkGyu+uuuzh06BAul4vx48fTu3dvnnvuuXiHlTw8rrbVrPUZDo5M7beWwtp7n/EtRbUImGeM+Vfj/caYp40xxcaY4ry8vGiEHhWRql3Lyspi7dq1ga+CgoKIXLc1JSUlnHrqqZSUlLTpvIcffpi1a9fy5z//mZ///OcN9s2bN49169Zx4403cueddwLWSjJ33nknb7/9NuvXr+fbb7/l7bffBuD+++/n0ksvZc2aNcyfP58bb7wRgC+++IL58+ezfv16li1bxo033ojHY1WcXHXVVSxbtqxJXDNmzGD8+PFs2rSJ8ePHM2PGDADuvPPOwO/2wQcf5PTTT494ogbhT4p7jjHmLhG5CGseqB8B7wJ6dwpDtdPdYHABgN0mpNttuoJBknrjjTd46KGHeOmll8jPz+fFF1/kzDPP5PLL9TNMWNzOtvVZs6fBUaNhx+roxaTirc33Gd9SbDOBDYneh/qPH/+RjZUbWz2uxlWDwZDlyMImLdenDOs1jF+d9KtIhRgxxhgWLlzIm2++yfe+9z3q6urIzMxs0zVOOeUUduzY0ey+hx9+GIAtW7YwdOhQ/In42WefzaJFixg/fjwiwqFDVkXrwYMHOfroowF4+eWXmTp1KhkZGQwaNIghQ4bw8ccfc8opp3DaaadRVlbW5Dlffvll3nvvPcAaaHbGGWfwxz82XLq5pKSEyy67rE2vM1zhTorr/wh8HlBijKmMSjQpqsbpaTDHml9Gmk2bQZOUf7H2pUuXctlll0Xlk1RK87jCm2Mt2NHjrGWnvFpmUlR77jPfBa4AzhKRtb6v81o7qTOpra2lqKiIoqIiLrroopg85/Llyxk0aBCDBw/mjDPOYOnSpa2f1MiyZcu48MILW903ZMgQNm7cSFlZGW63m8WLF7N9u7Wq3/Tp03nuuefIz8/nvPPO469//SsAO3bsYMCAI3M25ufnN5sY+n377beBZQX79evHnj17Guyvqalh2bJl7ZolIBzh1qy9IiIbgVrgRhHJA8Kf5a+Tq3K66de96aeKzDS7NoMmqfPPP59hw4aRlZXFU089xd69e9v8ybFT8zjbnqz1Hwcf/x32fWU1i6pU0+b7jDHmQ0Iv0ZZwwq0B21CxAa/xMqTHEDLaUvvcDH8zaLDmRidGauRqSUkJU6daU0ROnTqVuXPncvHFF4f1vHfeeSd33XUXe/bs4aOPPmpw3E9+8hOqq6vxeDysXm3Vsvfs2ZO//e1vTJkyBZvNxne+8x22bNkSiOOqq67i9ttvZ8WKFVxxxRV8/vnnIVeH6Ohrf+WVV/jud78btQ/uYdWsGWPuBk4Bio0xLqAauCAqEaWgmvqmAwzAWh+01jdhrkouM2bMYMWKFZSWlpKWlkZ2djYvv/xyvMNKHp42Tt0BDQcZqJSj95nYyc3NZf/+/Q22VVZWNhhQ0F4ej4dFixbxu9/9joKCAm655RZee+01Dh8+HNbzPvzww2zevJn777+fadOmNTh23rx5fPPNN/z4xz/mpptuCmw///zzWblyJStWrOC4446jsLAQgJkzZ3LppZcCVtNpXV0d+/btIz8/P1D7Btbkxf4m0ub07duXXbt2AbBr1y769OnTYP/8+fOj1gQK4TeDAgwHpojIlVjL4JwTnZBST3V96GbQnAxHYMJclXw2bNjAggULmDNnDgsXLuSNN96Id0jJw93GqTsAcgshPUf7raU2vc/EwIknnsjy5cvZvXs3AKWlpTidzgZNg+311ltvMXbsWLZv305ZWRlbt25l8uTJLF68mJycHPr16xcYAFBZWcmyZcs49dRTG1zDZrNx66234vV6ef311xvsS0tL4/777+ejjz5iw4YNAIEmyf379/PUU09x7bXXAtboX/9zbdiwgbq6OvLy8pg0aRLz58/H6XTyzTffsGnTJk466aQWX9ekSZOYPXs2ALNnz+aCC458jjh48CDvv/9+g22RFlYzqIjMBQYDawF/hxGDtcyHakWoAQYA3bIcHKrVZC0ZXXHFFXz99dcUFRVht1uJuIhw5ZVXxjmyJOGph4yubTvHZrMWdd+1tvVjVdLR+0xD0ZxrrW/fvjz++OOcd955eL1ecnJyKCkpwWY7Un8zZsyYwM+XXnopY8aMYdasWSxevDhwzEcffUR+fn6Da5eUlDTpGzea4soiAAAgAElEQVR58mT+9re/ccUVVzBnzhxuuukmbr/9dsCaBmnw4KbT8YgI99xzDw899FBgWg2/rKwsbr/9dh555BFmzpzJrbfeyrp16wD47W9/y9ChQwH405/+xHXXXcdjjz2GiDBr1ixEhJEjR3LppZcyYsQIHA4HTz75ZOD/+GWXXcZ7770XqIG77777uOaaa7j77ru59NJLmTlzJgMHDuTFF18MxPPSSy9xzjnnkJ2dTbRIqLbbJgdZs0KPMOEcHCfFxcWmtLQ03mE04fUajv2fpfxifCG3TRjaYN/1c0rZVlnDsl+eFqfoVHsNHz6cL774oqU+GKuMMcUxDquBRC0TAPztu9DjGLjs+badt/QuWDsP/rtcVzJIMq2ViVjcZ2JdJjZs2MDw4W3rX+nvsza4x2AyHdoPNlWEei+05T4RbjPo51gL5ao2qvGN9swJ0QzaLSuNw3Vas5aMRo0aFWhCUO3gqQdHOxap7jMM6qvg4PbWj1XJRu8zSjUj3NGgvYEvRORjwOnfaIyZFJWoUki1fxH3EAMMumY6OFTrinVIKgL27dvHiBEjOOmkk8jIONJRfsmSJXGMKom42zEaFCDP98l0z0bo0flmo09xep9JMieffDJOp7PBtrlz5zZYMUFFRrjJ2vT2XFxEJgKPA3bgH8aYGY32Z2D1RzgBqACmGGPKfGu8bQC+9B36kTGm4VTGScI/gCAnRLLWLTONw043Hq/BbtMmnWQyffr0eIeQ3Dz17UvW+gyzvu/dAEO173mKmR7vAFTbrFyZ1MuwJpWwkjVjzPsicgxQaIx5S0S6YCVgzRIRO/AkMAFrNupPRGSJMeaLoMOuAfYbY4aIyFTgj8AU376vjTFFbXw9CSdQsxZygIE1Gq6qzk33Lm0cGafi6vTTT2fr1q1s2rSJs88+m5qamsByJSoM7U3WsnpCzlFWzZpKKe25zyQDY0zE5i9TySkS3TDDXRv0OmAh8Hffpv7A4ubPAOAkYLMxZosxph6YT9M5cy4AZvseLwTGS4q9q6taaAbtlmltO1SnTaHJ5plnnuGSSy4JLOy+Y8eOZmfbViG42zHPml+fYVbNmkop7bzPJLTMzEwqKioicrNWyckYQ0VFRYcnTQ+3GfQmrORrpe/JN4lIn5ZPoT8Q3Au4HDi5uWOMMW4ROQjk+vYNEpE1wCHgHmPM/4UZa0KpdvoHGDRfs3aw1kXHZ7dRsfTkk0/y8ccfc/LJ1lu6sLCwyfIjqgWedsyz5pc3HFbPBq/Xms5DpYr23GcSWn5+PuXl5ezduzfsc3ZX78ZrvHi6eEizaYtLKsjMzGwyxUlbhZusOY0x9f5KLxFxQKuTwISqIWt8TnPH7AIGGmMqROQEYLGIjDTGHGpwssj1wPVgTX6XiI4MMGham98r22oGqqyuj2lMquMyMjJITz/SjOd2u7WpI1zG+Jab6kDNmqsGDm6DngURDU3FVXvuMwktLS2NQYMGtemc6+Zfx0HnQV744QsMz9Vl1ZQl3I+l74vI/wBZIjIBeBF4pZVzyqFBhVE+sLO5Y3wFsztQaYxxGmMqAIwxq4CvgaGNzsUY87QxptgYU5yXlxfmS4mtlppBe+dYN6uKameTfSqxnX766fzhD3+gtraWN998kx/96Eecf/758Q4rOXh909W0p88aNBwRqlJJe+4zSnUK4SZrdwN7gc+AnwFLgXtaOecToFBEBolIOjAVaDyvwRLAv/jXJcA7xhgjInm+AQqIyLFAIbAlzFgTSktTd+TmWDerfYe1Zi3ZzJgxg7y8PEaPHs3f//53zjvvPO6///54h5Uc3L4PJ+2ZZw0g7zjru/ZbSzXtuc+krGiuYKCST7ijQb0ishhYbIwJq/Hd1wftZuB1rBE9zxpj1ovI74BSY8wSYCYwV0Q2A5VYCR3AacDvRMSNtezIz40xlW16ZQnCn6x1SWvaDNo1w0G63cY+rVlLOjabjQsvvJALL7yQRK3VTVge34eT9tasZfWArkfDHk3WUkl77jOpTJM1FazFZM03MvNe4Gas/mUiIh7gr8aY37V2cWPMUqxPR8Hbfhv0uA74UYjzFgGLwnkBia7K6SE73Y4txDxqIkJuTjoVVVqzliyMMdx333088cQTGGMwxmC327nlllv47W9/2/oFVMeTNYA+wzVZSxEdvc+kGvF35dZcTQVprRn0l8B3gRONMbnGmF5YIzq/KyL/FfXoUkC10x2yCdSvd04GFVVas5Ys/vznP7N8+XI++eQTKioqqKysZOXKlSxfvpzHHnss3uElB38zaEeTtX1fgVfntksBep8J4k/WtGZNBWstWbsSuMwY841/gzFmC3C5b59qRVW9O+S0HX65Oens05q1pDFnzhxKSkoajPA69thjee6555gzZ04cI0si/pq1jixS3XckuOug4uvIxKTiSe8zIejcbCpYa8lamjFmX+ONvv4EOgFMGFqrWcvN1pq1ZOJyuejdu3eT7Xl5ebhcrU9uLCITReRLEdksIneH2J8hIgt8+1f6ll5DRCaIyCoR+cz3/ayOv5o4CQwwaOfUHWAlawDfft7xeFS86X0miH/qEq1ZU8FaS9ZaqvLR6qAwWMla8yum9M5JZ191vX6KShLBc6u1ZR80WILt+8AI4DIRGdHosMASbMBjWEuwAewDzjfGjMYaQT23PfEnhEgka3nDwObQZC016H0mBE3WVLDWRoOOFZFDIbYL0LG1EzqJKqeH/j2a/1X1zsmg3u2lyumma2an+xCZdNatW0e3bt2abDfGUFdX19rpgSXYAETEvwRb8Hq5F3BkQeuFwBMiIsaYNUHHrAcyRSTDGJN81bKeCPRZc2RA76GwW5O1FKD3GaVa0WKyZoxJ+kV0462mvpVmUP9ca1X1mqwlgQ4u1t6RJdiCm4kmA2tCJWrJsKoHbl9S25E+a2A1hW5d0fF4VFx15D4jIs8CPwT2GGNGRS6q+NPWFhVMF9aLslb7rPlXMdB+a51BR5Zgs3aKjMRqGv1ZqCdIhlU9cPsHGHSgZg2g7yg4VA41STkFo4qMWcDEeAehVLRpshZlh+taGQ2afaRmTaW8di/B5vs5H3gJuNIYk7zDIAPNoB3oswZwlK8i5dv1HbuOSlrGmA/wlY9UoVN3qFA0WYsip9uD0+2le1bzzZt5XXV90E6kI0uw9QBeBf7bGLM8ZhFHQ2CAQQebQY8aa33fubpj11EqAWkzqAqmyVoUHa6zlprqltl8zVrPLro+aGdhjHFjzdL+OrABeMG/BJuITPIdNhPI9S3BdhvWeon4zhsC/EZE1vq++sT4JURGR9cG9cvJg9whsO2jjsekUpaIXC8ipSJSundv4q9ipVN3qFDCWhtUtc/BWmverW4t1KylO2x0z0rTmrVOogNLsN0PpMZK8ZEaYAAw8P/BxqXg9YJNP3uqpowxTwNPAxQXFyd8BhRoBtWaNRVE/7tF0SF/stbKKM8+XTP49lCr0z4olRoisTao38BToLYSKjZ1/FpKJRCtWVPBNFmLokP+ZtCsliswj+6RxY4DtbEISan4i8SkuH4DT7G+l33Y8WuppCMiJcAK4DgRKReRa+Idk1LRoMlaFIVbs9a/ZxY7D2jNmuok3BEaDQrQ61jolg9b3u34tVTSMcZcZozpZ4xJM8bkG2NmxjsmpaJBk7UoOlTXep81gP49sqisrqem3h2LsJSKL48TbGmR6WMmAkPOgi0fgEfLj0p+2mdNhaLJWhQdqvWPBm09WQPYqU2hqjNwOyMzuMBv8FngPKhTeKjU4JsWW/usqWCarEXRoToXaXYhM63lX3P/nlaytkObQlVn4HZ2fNqOYINOBwS+fidy11QqzjRZU8E0WYuiQ7UuumWmBebNaU6+L1nbVlkTi7CUii9XLaR1idz1uvSC/uM0WVMpQZtBVSiarEXRoTp3q/3VAPp2zSTDYWPrvuoYRKVUnLmqI5usgdUUWl4KtQcie12l4kRr1lQwTdai6FCtK6xkzWYTCnKzKavQmjXVCdTXQHoUkjXjgW8+iOx1lYqxQEuM5moqiCZrUXSgpr7FdUGDHZPbha0VWrOmOgFXDaRlR/aa+SdCeldtClVKpSRN1qJoX1U9vXPC60hd0DubrZU1eL36cUqluPrqyNes2dNg0Gnw9dugfX1UCtBmUBVMk7UoMcZQUe2kd054E38ek9uFereX3brslEp1rprI91kDGHwmHNgGFZsjf22lYiQwwECTNRVEk7Uoqa73UOfykpsdXs3aoFyrWahMBxmoVFdfA+kRbgYFOO771vcNSyJ/baViTEeDqmCarEVJRZW1pE5uuDVrvX3Jmg4yUKkuGqNBAbrnQ/8T4AtN1lTy05o1FUyTtSjZF0jWwqtZ69ctk3SHTQcZqNQXjdGgfsMnwa61UPlNdK6vlFJxoMlalPgnuB3QM7ybks0mDOzVhTJN1lQq87istUHTu0bn+qMvAbHB6jnRub5SMaLNoCqYJmtRUravBhEY0Csr7HMKcrPZqs2gKpX5J63N6hGd63fPh6ETYc1ccNdH5zmUiiJ/86c2g6pgmqxFyfqdB8nvmUWGwx72OQW5Vs2afqJSKat2v/U9q2f0nqP4GqjeC58vjN5zKBUl+v9fhaLJWhQcqKnn/a/2cs6Io9p03jG9s6lzefn2kDNKkSkVZ4FkLUo1a2CtZnDUaHj/j1azq1JJSJM2FUyTtShY+tluXB7DhUX923TesKOsfjxrt+v6hipFxaJmzWaDs34D+8vgo79F73mUiiJtBlXBNFmLgsVrdzA4L5tR/bu16byiAT3ITrfz/ld7oxSZUnFW5++zFsVkDaDwHDjuB/DO72Hnmug+l1JRoMmaCqbJWoTtOFDLx99UcmFR/yML8oYpzW7jnJFH8cq6nVQ53VGKUKk4Orzb+p6dF93nEYFJf4GcvjDvUqjcEt3nUypCAkma5moqiCZrEfZi6XYALmhjE6jftO8UUOV0M++jrZEMS6nEcHA7ZPaAjChN3REsuzdc/i/wumHW+VDxdfSfU6kI0Zo1FUyTtQiqc3l47qOtnDWsDwNz2zfpZ9GAHpw+NI8n391MZbVOPaBSzIFt1vQasZI3FK582VqPdNYPYO9XsXtupTpAkzUVLKrJmohMFJEvRWSziNwdYn+GiCzw7V8pIgVB+/7bt/1LETk3mnFGyl/f2cS+qnp+fvrgDl3n7u8Po6bew8Q/f8Cf3viSgzU6oi1VdLYy0cTuz6HPiNg+Z78xcNWrvhq2H8CnL1pJm44UTQmtlalkpaNBVTBHtC4sInbgSWACUA58IiJLjDFfBB12DbDfGDNERKYCfwSmiMgIYCowEjgaeEtEhhpjPNGKt63qXB4yHDZEBJfHy4ul5Tz13tdMHpfPSYN6dejaw/t1Y841J/H0B1t44t3NzP5PGTeeOYQx+d1ZvXU/RQN68p3BudhsbesTp+Ir1ctEq/ZshMM7If/E2D933xFw1VJ4/lL417XWNpsDcodAn+HQZ6T1vdex4HVZo1aNF7oPhB4DwRHesnEqtsIsU0klw26tJ13j1gnS1RFRS9aAk4DNxpgtACIyH7gACC5EFwDTfY8XAk+I1Sv/AmC+McYJfCMim33XW9GeQK6YuRKn2wsGvMaqXPYagzHWp5fgn73+bcaqhm74s3XcoVoX+2tcdM9K4+geWezYX8OhOjenHJvL/ReOak+ITXxncG++M7g3G3Yd4qFlG5nx2sYG+7tmOuielUZOhoOcDAceY6h3e3G6vdS7vaQ7bHTLdJDuiEzl6cOXjGVAryit59h5JEyZaJfPFkLpP60kxngBc+SxMY22m6bbq/ZAWjaMvChmITeQNxRuWQU718K+r2Dfl7D3S2u06PqXWjhRoFt/yOkD9jQryZMOlKseA+HCp9p/vgoWTplKGntq9rD9sNXv+cm1T3LhkAvjHJFKFNFM1voD24N+LgdObu4YY4xbRA4Cub7tHzU6t0mPfRG5HrgeYODAgc0GYgwI1vqbDhFsIoiAiFjbfY+tiirx/UyI46yfszMc9OuWyc6DdXx7qI6iAd05a1hfxg/rE/HaruH9uvHPn57Eqq372Xu4juKCXvzn6wo++aaSaqebw043VXVusuxCehcb6Q7ry+XxcqjWTb3HG5E4tEY+IhKmTLSL/01gs4P4EhaxAXLksQQ9hqDtNnBkQPHVkBPlkaAtsdkh/wTrK5izykrc9n9jxZnVExA4sBX2b7XmbKveC8YDHrcvKW0nLUyR1GqZimqZiLCemT0Z03sMmw9s5oz8M+Idjkog0UzWQmUtjf9LNXdMOOdijHkaeBqguLi42f+Az13b+H6YfE445si8VJPGHs2ksUfHMRrVTglTJtplzI+sr1SUkRM6ieO7cQlHha3VchHVMhFhabY05v1gXrzDUAkomgMMyoEBQT/nAzubO0ZEHEB3oDLMc5VKNlomlIosLReqU4hmsvYJUCgig0QkHatz9JJGxywBpvkeXwK8Y6whMEuAqb6RcYOAQuDjKMaqVCxomVAqssIpU0olvag1g/r629wMvA7YgWeNMetF5HdAqTFmCTATmOvrLF2JVdDwHfcCVidRN3BTUo16UyoELRNKRVZzZSrOYSkVcZIqc7mIyF4gGab9Hwhsi3cQYdJY2+8YY0wce9InTZlItL9ba5Ip3kSLVctEeBLt79aaZIo30WINu0ykTLKWLERkb7z/YYVLY1XRlmx/t2SKN5liVUck298tmeJNplgb0+WmYu9AvANoA41VRVuy/d2SKd5kilUdkWx/t2SKN5libUCTtdg7GO8A2kBjVdGWbH+3ZIo3mWJVRyTb3y2Z4k2mWBvQZC32no53AG2gsapoS7a/WzLFm0yxqiOS7e+WTPEmU6wNaJ81pZRSSqkEpjVrSimllFIJTJM1pZRSSqkEpsmaUkoppVQC02RNKaWUUiqBabKmlFJKKZXANFlTSimllEpgmqwppZRSSiUwTdaUUkoppRKYJmtKKaWUUglMkzWllFJKqQSmyZpSSimlVALTZE0ppZRSKoFpsqaUUkoplcA0WVNKKaWUSmCarCmllFJKJTBN1pRSSimlEpgma0oppZRSCUyTNaUSjIg8KyJ7ROTzoG3TRWSHiKz1fZ0XzxiVUkrFjhhj4h1DRPTu3dsUFBTEOwylAFi1atU+Y0xee84VkdOAKmCOMWaUb9t0oMoY80i419EyoRJJR8pEpGiZUImkLWXCEe1gYqWgoIDS0tJ4h6EUACKytb3nGmM+EJGCjsagZUIlko6UiUjRMqESSVvKhDaDKpU8bhaRT33NpD3jHYxSSqnY0GRNqeTwN2AwUATsAv4U6iARuV5ESkWkdO/evbGMTymlVJRosqZixrnlG6r+70OM1xvvUJKOMeZbY4zHGOMFngFOaua4p40xxcaY4ry8uHYPCovb5WH3loPxDkOpuCv/4nPc9fXxDkMlKE3WVEzUrlvHNxdcwPbrruPbB/4Q73CSjoj0C/rxIuDz5o5NJh+UfMWih1ZxaF9tvENRKm4OV+5jwX1389bMp+IdikpQmqypmNjz6GPYe/ak2w9+wP7nn6e+rCzeISUsESkBVgDHiUi5iFwDPCQin4nIp8CZwH/FNcgI2bv9MADOGnecI1Eqfg7s3gXAN2t08IMKLWVGg6rE5dq1i5qVK8n7r/+i+4UXcui11zjw8sv0ufXWeIeWkIwxl4XYPDPmgSilYsJVVweAs7oqzpGoRKU1ayrqqj78EICuZ51JWt8+ZB1/PNXL/xPnqJRSKjG4XVZfNY9ba5hVaJqsqair+Wgljj59SB8yBIAuJxZTt349nqrqOEemlFLx59GBBaoVmqypqKvbuJHMUaMQEQC6FJ8IHg91n30a58iUUir+3C5XvENQCU6TNRVVXqeT+m++IXPYcYFtmcOHAeD86qt4haUSRIqsdqdUh/ibQZVqjiZrKqqcmzaD10vG0CPJmiM3F3uvXtRt2hTHyJRSKjEEz6+WKut1q8jSZE1FlXOzlZBlDC1ssD2jsBDnV5qsdXa+lnGlOrXgPms6abgKRZM1FVWunTsBSOvfv8H2jMJCnJs366dIpVSnF9xnzaP911QImqypqHLt3Ik9rze2jIwG29OPOQZTU4OnoiJOkSmlVGII7rOm03eoUDRZU1Hl3rmTtH5HN9meNiAfAFd5eaxDUkqphOJpkKxpzZpqSpM1FVWunbtIO7ppspY+YAAA9ds1WVNKdW7u+qBmUE3WVAiarKmoMcbg2rWLtH79muzz92FzlW+PdVgqgWiXRaUaJmjaZ02FosmaihpPZSXG6QxZs2bLzMSRl0e9NoMqpTq54ARN+6ypUDRZU1ETGAl6dNOaNYC0AQNwaTNop6ZTdyjVMEHTqTtUKFFN1kRkooh8KSKbReTuEPszRGSBb/9KESlotH+giFSJyB3RjFNFh2vnLoCQNWsAaUcdhWv37liGpJRSCccb1Azq9XjiGIlKVFFL1kTEDjwJfB8YAVwmIiMaHXYNsN8YMwR4DPhjo/2PAa9FK0YVXYGatRB91gAcffvi3rNH51pTSjURxof920TkCxH5VETeFpFj4hFnJATXrHm9mqyppqJZs3YSsNkYs8UYUw/MBy5odMwFwGzf44XAePGt9i0iFwJbgPVRjFFFkWvXTmxdumDr3j3kfkffPpi6OryHDsU4MqVUIgvzw/4aoNgYMwbr/vFQbKOMnAbJmkebQVVT0UzW+gPBQ/3KfdtCHmOMcQMHgVwRyQZ+BdzX0hOIyPUiUioipXv37o1Y4CoyXDt34ji6H9JMx6S0Pn2s4779NpZhqQSilaqqGa1+2DfGvGuMqfH9+BGQH+MYI8brdiM263ZstBlUhRDNZC3UHbrxv+bmjrkPeMwYU9XSExhjnjbGFBtjivPy8toZpooWdzNzrPk5+va1jvt2T6xCUolKBxqohsL5sB/sGprpMpMMH+o9bjdpvlVevDrAQIXgiOK1y4EBQT/nAzubOaZcRBxAd6ASOBm4REQeAnoAXhGpM8Y8EcV4VYS5du4kc/ToZvcHkrU9WrPW6WkNm2oonA/71oEilwPFwOmh9htjngaeBiguLk7Id5rH7cKRnkF9ba32WVMhRTNZ+wQoFJFBwA5gKvDjRscsAaYBK4BLgHeM1dv8e/4DRGQ6UKWJWnLx1tTgOXCg5Zo1XzOoe4/WrHVWOnWHakY4H/YRkbOBXwOnG2OcMYot4ryeIzVr2gyqQolaM6ivD9rNwOvABuAFY8x6EfmdiEzyHTYTq4/aZuA2oMmIH5WcXLv803aEHgkKYEtPx96jh/ZZU0o1FviwLyLpWB/2lwQfICLHA38HJhljkvoTn9UMmgnoaFAVWjRr1jDGLAWWNtr226DHdcCPWrnG9KgEp6LqyIS4zdesgW/6Du2zppQKYoxxi4j/w74deNb/YR8oNcYsAR4GcoAXfYOYthljJjV70QTmcbtx+Pusac2aCiGqyZrqvAIT4jYzx5qfo28f3Fqz1mnpaFDVnDA+7J8d86CixON240hPB3TqDhWaLjelosK1cyfY7YF+ac1x5OXhTtARWiqGtO+a6sS8bhdp6f7RoFqzpprSZE1FhWvnTtL69kUcLVfeOvLycFdUaKdapVSnFdwMqmuDqlA0WVNR4dq5s9X+amAla3i9eCorYxCVUkolFmOMNcAgXfusqeZpsqaiwrVjB2n9w0zWQJtCOzvtu6Y6KeP1gjE6wEC1SJM1FXHG5cK9Zw9p/VuacNyiyVrnpvOsqc7O43YBHJlnTfusqRA0WVMR5/r2W/B6w2wG9U2Mq8lagIg8KyJ7ROTzoG29RORNEdnk+94znjEqpSLDv4i7Q5tBVQs0WVMR59oR3hxrAI683oAma43MAiY22nY38LYxphB4mxSZQFqn7lCdnbdJsqYDDFRTmqypiAt3QlwAW0YG9u7dNVkLYoz5AGuN3GAXALN9j2cDF8Y0qGjT5lDVSflr1rQZVLVEkzUVca4dOwBwhJGsATj66FxrYehrjNkF4PsecgI7EbleREpFpHSv/k6VSnhNmkF16g4VgiZrKuJcO3fiyMvD5puRuzWOvDzcezSxiARjzNPGmGJjTHGeb/CGUipxNR5goH3WVCiarKmIC3eONT9HXh6uvbo+aCu+FZF+AL7vqfUL075rqpM60mfNv9yUJmuqKU3WVMRZc6y1Pm2HnyMvD8/efRjtbd6SJcA03+NpwMtxjCVidOoO1dl5XFbNmj0tDRGb9llTIYWVrInIIhH5gYhocqdaZOrrrWTtmIFhn+PIy8O4XHgOHIhiZPHRnrIjIiXACuA4ESkXkWuAGcAEEdkETPD9nPQ0P099kydP5tVXX413GAnL7aoHrD5rYrNpzZoKKdwbyN+AHwObRGSGiAyLYkwqibl27gSvl/SBx4R9TopPjNvmsmOMucwY088Yk2aMyTfGzDTGVBhjxhtjCn3fU2t9Lq1hS1k33HADzz//PMAovX805XY6AStZs9ntOsBAhRRWsmaMecsY8xNgHFAGvCki/xGRn4pIWjQDVMmlfts2ANLbWLMGqZmsadlRnd3ZZ5/NvHnzADagZaAJl69mLS0jA5tdm0FVaG1pmskFrgKuBdYAj2PdgN6MSmQqKdVv9SVrAzVZ89Oyozq7iooKgFy0DDRxpGYtHZvNrpPiqpAc4RwkIv8ChgFzgfP98z0BC0SkNFrBqeRTv20bti5dsOfmhn1OKidrWnZUZ3fxxRezceNGsCoHtAw04gpqBhW7XfusqZDCStaAfxhjlgZvEJEMY4zTGFMchbhUkqrftpW0Y45B2jDMz5adja1Ll5RM1tCyEx4daJCyrr32Ws477zxEZLc/UdMycISrrg6AtMxMq8+axx3niFQiCrcZ9P4Q21ZEMhCVGlxbt7WpCdTPkZeyqxho2VGd2j333BNqs5YBn+oDldgdDjKzc7QZVDWrxZo1ETkK6A9kicjxHNYplBsAACAASURBVBmz1Q3oEuXYEpbL5aK8vJw63yci5WMMrjvvoDYnh8MbNrTpVPc9v6YG2nxevGVmZpKfn09aWpN+0g4ROYFOVHbaUy6GnJ3Ose6efHtwG/s26MxAqcBfJioqKtixYwe1tbWsWbMGoIuIjCOFywC0vRzkDBnBd24sZOPGjRT95BocaWlsSLL/g6plLdwnwtZaM+i5WB2j84FHg7YfBv6n3c+a5MrLy+natSsFBQVtau5Ldd76epxeL2n9++Po2bNN59Zv3463tpbMoUOjFF3kGWOoqKigvLycQYMGNd7dHXiETlR22lMuKndW43Z56Nkvm7R0e5QjVNEWXCY++OADZs2aRXl5ObfddhtYZeFPpHAZgLaXg8qd5RhjyO0/gH3btuLISKdH334xiFTFQiv3ibC1mKwZY2YDs0VksjFmUbufJcXU1dVpohaCqbeGoEuYa4IGE4cD406uvhoiQm5uLs0smF5hjDmzM5UdLRcquExMmzaNadOmsWjRIiZPnoyIfGWMOTPeMUZbW8uB8Rpsdl+tsqD9N1NMK/eJsLXWDHq5MeY5oEBEbmu83xjzaIjTOgW9ITVlfKOa2pus4fViPB7Enjw1LC28D3r5vneqsqPlQvnfA8899xyXX345ZWVlPProowB9g8tCqpYBaFs5MMaLiMN/IkaztZQTif+LrTWDZvu+53T4mVTKM/X1IDYr8WorX1u+cbuTKllrgb8DlpYd1SlVV1cDUFVV5d9kA7rGK55EZYwJ3MwF0Zo1FVKLPXqNMX/3fb8v1FdsQlSh2O12ioqKAl9lZWXMmjWLm2++ucFxZ5xxBqWl1lRGBQUF7Nu3r8H+UOc0p6CggNGjRzN69GhGjBjBPffcg9NXmwZWsvbEvHlkZWVx8OBBAPbs2cOgQYPYvXt34Lgbb7yRGTNm8N577yEizJw5M5DgrS5dhYjwyCOPADBlypTAaywoKKCoqAiwOvFOmzaN0aNHM3z4cB588MHA9ZctW8Zxxx3HkCFDmDHjyBKaP/nJTzjuuOMYNWoUV199NS7fAsrGGH7xi18wZMgQxowZw+rVqwPnTJw4kR49evDDH/4wrN9RkH2+a2vZiaF4lIuqqipuuOEGBg8ezPHHH88JJ5zAM888A0BZWRlZWVkNYpozZw4ABw8e5Morr2Tw4MEMHjyYK6+8MlBugs8bMWIEV155ZeD9CvDggw8yZMgQjjvuOF5//XXAan476aSTGDt2LCNHjuTee+8NHP/NN99w8sknU1hYyJQpU6j3dVn44IMPGDduHA6Hg4ULFzZ4XbNnz6awsJDCwkJmz54d2P7rX/+aAQMGkJPT8ueQn/3sZwDce++9/lh2aRloyhgDNl/NixCRmrV4lIOW3s8A69ev56yzzmLo0KEUFhby+9//3nrtvufJy8ujqKiIkSNHcskll1BTUwPA9OnTERE2b94cuNZjjz2GiARiLykpYfTo0YwZM4aJEycGXsf06dPp379/4PewdOmRWZRClSGAq6++mj59+jBq1KgGr6+yspIJEyZQWFjIhAkT2L9/f4P9n3zyCXa7vUk5ipRwF3J/SES6iUiaiLwtIvtE5PKoRKTCkpWVxdq1awNfBQUFMXned999l88++4yPP/6YLVu2cP311wf2eZ1OXlj6KieeeCIvvfQSAH369OFXv/oVd9xxBwCrV6/mww8/5Pbbbwdg9OjRLFiwIJCsLXhhAWPHjg1cc8GCBYHXOHnyZC6++GIAXnzxRZxOJ/+fvfsOj7JKHz7+PTPpJAESipAAoddARMQKFmTBAqiwAuoLWJZV+Km4WFAsuKsull3W1V1WxIoYEJSiq4AKrIJC6NINJUDoJIGQkCnJnPePKaRnkkxN7s91cZF55in3TObkuefU7du3s2nTJt59910yMjIoKipi4sSJfPvtt+zatYvU1FR27doF2JO1PXv2sH37dgoKCpg9ezYA3377Lenp6aSnpzNr1iwefvhh1/WffPJJ5syZU+P3S8qOb/mjXDz44IM0btyY9PR0tmzZwrJly8jOvrh0a/v27UvENGbMGAAeeOAB2rVrx/79+9m/fz9t27blwQcfLHPc9u3byczM5PPPPwdg165dzJs3j507d7Js2TImTJhAUVER4eHhrFy5km3btrF161aWLVvGunXrAHj66ad5/PHHSU9Pp3Hjxrz//vsAtG7dmo8++oi77767xGvKzs7mpZdeYv369aSlpfHSSy+5bk5DhgwhLS3N7ffnqaeeIjc3F0BJGSiHtqGU/VZsr1mrfbLmj3JQ2ee5oKCAoUOHMmXKFH777Te2bdvGzz//zL///W/X8SNHjmTr1q3s3LmTsLAw5s+f73ouOTmZefPmuR4vXLiQbt26AVBYWMhjjz3GqlWr+PXXX+nZsyfvvPOOa9/HH3/c9T7ccsstQMVlCGDcuHEsW7aszOubPn06AwYMID09nQEDBpSoCCgqKuLpp59m0KBBnngry+Vue9XvtNZPKaXuADKB3wOrgE+9FlmQOPHqq5h37/HoOcO7duGSZwN7sFR0dDT/+c9/aNWqFdnZ2TRu1Ij9+/eTV1DAm2+9xauvvsq4ceMAGD9+PB9//DGrVq1i6tSpvPPOO64hzK1btyY3N5dTWVnEas3yH37g1iFDylxPa83nn3/OypUrAXsfgPz8fAoLCykoKCAsLIzY2FjS0tLo0KED7dq1A2DUqFEsWbKEbt26uQoqQN++fcnMzARgyZIljBkzBqUUV155JWfPnuX48eO0aNGCAQMGsHr16tq8VfWy7Pz0+W+cOZJX5X6FliK01oSEGavs19GkVTT97gqs0cL79+8nLS2Nzz77DIPBfsNt2rQpTz/9dKXH7du3j02bNpW4Ib3wwgt06NCB/fv3YyzWFcBoNNK3b1+OHj0K2D+vo0aNIjw8nLZt29KhQwfS0tK46qqrXLVdVqsVq9WKUgqtNStXrnQups7YsWOZNm0aDz/8sOsm7ozdafny5QwcOJC4OHvXy4EDB7Js2TJGjx7NlVdeWa33aMWKFbz++utgHyG9g3pSBgBWfTSLU4cOVLqPpaAAY0gIxtDQi0tPhYdXuH+zNu24Ydz4Cp/3h6o+z6tXr+aaa67hd7/7HQBRUVG88847XH/99UycOLHEuQoLC8nPz6dxsRkFbr/9dpYsWcJzzz3HgQMHaNiwoeseorVGa01+fj7x8fHk5ubSoUOHSuOtrAz179+fjIyMco9x3gvGjh3L9ddfz2uvvQbA22+/zfDhw9mwYUO13zt3uTuxkXNykFuAVK11dmU7C+8rKChwVe3ecccdfokhNjaWtm3bkp6ejjab+fybbxg1YgT9+vVj7969nDp1CrDfCGbOnMnw4cPp1KkT/fv3L3GeESNGsODLL1m3dSuX9uhBeDl/qH766SeaN29Ox44dXcc0aNCAFi1a0Lp1a5544gni4uI4evQorVq1ch2XmJjousk5Wa1W5syZw+DBgwHcOqYWpOz4kK/Lxc6dO+nVq1eZZKe4/fv3l2iS+umnn9i1axcpKSllkrKUlBR27txZ4niTycT69evd+rwWFRWRkpJCs2bNGDhwIFdccQVZWVk0atSIEEfttTufb0+WiWLNtw2RMlCSqxJNuf73RJc1X5eDqj7PO3fu5LLLLitxTPv27cnLy3PWujJ//nxSUlJISEggOzubIcW+tMfGxtKqVSt27NhBamoqI0eOdD0XGhrKzJkzSU5OpmXLluzatYsHHnjA9fw777xDz549uf/++121wzX5fJ88eZIWLexTqrRo0cJ1fzt69CiLFi3ioYceqtZ7Vl3u1qx9pZTaAxQAE5RSTQGZERb8VgPmrOYurqKaCW+O0HP2ObCZzSxctowvFy3CYDBw5513smDBAte3ppSUFHr06MGECRPKnOOuu+5i5MiR7ExM5K4hQ9iQnl5mn9TUVEaPHu16nJaWhtFo5NixY+Tk5NCvXz9uuukmVzzFlX79EyZMoH///vTr16/Ea6jsmFqol2XH3RowT8+z5u9y8corr7BgwQJOnTrFsWPHgIvNmcUtWbKk3OsX72zuTPLS09MZMWIEPXv2dO1TmvMYo9HI1q1bOXv2LHfccQc7duygefPmFe5fEU+WiSFDhtClSxewD1j7ob6UAaDKGjCbzcapg/uJiY+nQaM4co4fw1ZUSHxi9VeBKc7X5aD457a87RU9X/z6I0eO5J133kFrzcSJE3njjTeYMmWKa79Ro0Yxb948li9fzg8//MCHH34I2L8MzJw5ky1bttCuXTseeeQR/vrXv/Lcc8/x8MMP8/zzz6OU4vnnn2fy5Ml88MEHHv18T5o0iddee61EouoNbtWsaa2nAFcBfbTWViAfGObNwET1xcfHl+n0mJ2dTZMmTbxyvfPnz5ORkUGnTp3YtmUL+w4dYtBtt5GUlMS8efNITU0tsb/BYCi3BuKSSy4hNDSUlb/8wg1XXFHm+cLCQr788ssS36Y+++wzBg8eTGhoKM2aNeOaa65h48aNJCYmcuTIEdd+mZmZtGzZ0vX4pZde4vTp086pBACqPKY2pOz4nzfLRbdu3di2bRs2m32JoKlTp7J161ZXbUFFunfvzpYtW1zHgf3GvW3bNrp27QpcTPL27dvHunXrWLp0KeDe57VRo0Zcf/31LFu2jCZNmnD27FkKHfMYuvP59mSZmD59Or/88gvALikDpTiTBnVxnjVvDQb1Zjmo6vPcvXt312AApwMHDhAdHU1MTMkBwkophgwZwo8//lhi+5AhQ5gzZw6tW7cmNjbWtd2ZlLZv3x6lFHfddRc///wzAM2bN8doNGIwGPjDH/7g6mtZk8938+bNOX78OADHjx+nWbNmAGzcuJFRo0aRlJTEwoULmTBhAosXL676Taum6qzv0hUYqZQaA4wAfufxaEStXH755axdu9Y18nLjxo2YzeYS1b2ekpeXx4QJE7j99ttp3Lgx8xcu5LlHHiEjI4OMjAyOHTvG0aNHOXTokFvn+/Of/8wrzzyDoZxvPN9//z1dunQhMTHRta1169asXLnS1Vdh3bp1dOnShcsvv5z09HQOHjyIxWJh3rx5DB06FIDZs2ezfPlyUlNTSySNQ4cO5ZNPPkFrzbp162jYsKGruttDpOz4kTfLRYcOHejTpw/PPfecq4OyyWQq95t76eMuvfRSXn754tKxL7/8Mr179y7T36ZFixZMnz7dNeJ56NChzJs3D7PZzMGDB0lPT6dv376cPn2as2fPAvZmMGe5UUpxww03uEapffzxxwwbVnmuNGjQIFasWEFOTg45OTmsWLGiVp2nHcsnxUkZKEk7kpuSU3d4J13zdjmo7PN8zz33sGbNGr7//nvA/vl89NFHeeqpp8o935o1a2jfvn2JbZGRkbz22mtMnTq1xPaEhAR27drlmnT2u+++c33hcSZXAIsWLXKN8KyoDFVm6NChrlHRxcvQwYMHXfe9ESNG8O9//5vbb7+98jesJpyd8yr7B8wBfgb+Dbzt+PdPd4711b/LLrtM+8quXbt8dq2KNGjQoNztixcv1pdeeqnu1auXvuaaa/SmTZtcz7Vp00a3aNFCJyQk6ISEBP3444/rDz/8UDdo0MC1LSEhQR85cqTcc7dp00b36NFDd+/eXXft2lU/++yzuqCgwP5cYqLe9sMPJfZ//PHH9fTp012Pr7vuOr1hwwbX41WrVulbb73V9dhy9Kgu2LVLv/jii/qNN95wbR87dqyeOXNmiXOfP39ejxgxQnfr1k137dpVv/76667n/vvf/+qOHTvqdu3a6Zdfftm13Wg06nbt2ulevXrpXr166ZdeeklrrbXNZtMTJkzQ7dq10z169CgR47XXXqubNGmiIyIidEJCgl62bFmZ96W8zwOwUfu57PiyTGhds3KRdTRPn8w4py3mQo/E4I9yce7cOT1+/HidlJSke/fura+55hr99ttva621PnjwoI6IiHB95nr16qXfeustrbXW2dnZ+p577tHt27fX7dq10/fcc4/OyclxHde9e3fXNWw2m+7Zs6f+8ccftdZav/zyy7pdu3a6U6dO+ptvvtFaa71t2zadkpKik5OTdffu3V2fb6213r9/v7788st1+/bt9YgRI7TJZNJaa52WlqYTEhJ0VFSUjouL0926dXMd8/777+v27dvr9u3b6w8++MC1/cknn9QJCQlaKaUTEhL0iy++WOY9Kf5ZuPfee/VVV12lgVP+vn/4okxUpxxYzWZ9fN9v+kLuOa211jknjuvThzJqHYM/ykFln2ettf7111/1ddddpzt16qTbt2+vp02bpm02m9Za6w8//FA3adJE9+rVSycnJ+ubb75Znzx5Umuty9wPnIrfT2bOnKm7dOmik5OT9W233abPnDmjtbZ/9nr06KGTk5P1kCFD9LFjx1zHl1eGtNZ61KhR+pJLLtEhISE6ISFBz549W2ut9ZkzZ/SNN96oO3TooG+88UadlZVVJqaxY8fqBQsWlPv+VHafcOef0m5k8Uqp3UA37c7OftKnTx9duprVW3bv3u3K3IX926Fp925C4psQeknZ/jHusp46ReGpU0R064aqpMN2oCnv86CU2qS17uPPsuPLMgE1KxeyNmjdVPyz0LVrV3bt2oXBYNikte7jz7h8USaqUw6sZjNZmYdp1LwFEdHRnDt1AovJRNPWSV6NUfheZfcJd4539464A7ikmrGJekJbLKA1KqLi4ebuUMVWMahDpOyIeq1Hjx4lJsUWF2ntaAY1XBwN6q1mUBHc3B0N2gTYpZRKA1xT1muth1Z2kFJqMPAWYARma62nl3o+HPgEuAzIAkZqrTOUUgOB6UAYYAGe1FqvdDNWUUtXXHFFiZUJAObMmUNycnK5+zvXBDVUMjeQO5wT42qrFWqwvmiAqlHZEYGnuuVC2J05c8Y5gWlHpdRS53YP3D/6A/8AegKjtNbemTrei1wV7s6RiB5awcCbpBz4h7vJ2rTqnlgpZQT+BQzEPhnoBqXUUq31rmK7PQDkaK07KKVGAa8BI7Ev1TNEa31MKdUDWA4kVDcGUTPr16+v1v62WizgXpyrZq3Ysjp1wDR/ByA8o7rlQthNmzYNgOuvv/448Dd3jnHz/nEYGAc84cFwfcuRrJVcwcCfAVVNyoF/uJWsaa3/p5RqA3TUWn+vlIrC/m2nMn2BfVrrAwBKqXnYh2sXL2zDuHgzWwi8o5RSWustxfbZCUQopcK11iXTeT/SlcwbU99osxkVGlrrBdiDMVmrqitaDctO0JJyIUqXieuuu845Klw5yoNH7h9a6wzHc7byTuBP7pYDbXMma86aNWkGrYs80WXZ3bVB/4A9mXrXsSkBqGoikQTgSLHHmZStHXPto7UuBM4B8aX2GQ5sKS9RU0qNV0ptVEptdA7b9YWIiAiysrI88guoC7TZjKplEyiAMhpRRqO9D1wQ0FqTlZVFREREhfvUsOwEJSkXorwy8d577zFixAiANo5Nnrp/BKTqlANXnzXn1B2BX7Emqsmd+4Q73G0GnYj9m856x8XTlVLNqjimvK8VpT+Hle6jlOqOvWm03Dl5tNazgFlgH+VTRTwek5iYSGZmJr5MEAOW1lhPnMAQFYWxoKDWpys8fRqysgipYlLRQBEREVFi/rdy1KTsBKWalIv8s2ZsRZpTuWEYQ4JnBLCoWOky8a9//Yu0tDTCw8Nt4NH7h1uUUuOB8WCfn9HbqlMOLKYCTOfPk1VgxmA0YsrPw1JwgWxTcHxhFe5x4z5RJXeTNbPW2nIx+1chVF1wMoHis+0lAscq2CfTcc6GQLbjGonAImCM1nq/m3H6RGhoKG3btvV3GAHBknmU/XcO55KXXqLxyLtqfb7Md2dh3r2b9suXeSC6gFCTshOUalIu5r2cRlZmHndNvZymrWKqPkAEnfDwcMKK9Wf14P3DLb7+Ul+dcrD526Ws+WgWE2Z/RmRMLGvnz2H9ogX8ad7Sqg8W9Yq7X2X/p5R6Foh0jNRcAHxVxTEbsI/+aauUCgNGAaU/gUuBsY6fRwArtdZaKdUI+C/wjNZ6rZsxCj+wHDwAQHj7dh45X1hiAtZjx1wze9cBNSk79UedTFtFcddddx2vvvoqgPLw/SPoFTq6fIQ4klllMKK1rS79/RMe4m6yNgU4DWwH/gh8AzxX2QGOPmj/h30k527gc631TqXUn5VSziHb7wPxSql9wJ8c18FxXAfgeaXUVse/Otl0FOzM++2VnmHtPJOshSYmoq1WCk+d8sj5AkC1y059JGMS6q7p06fTtGlTgAI8eP9QSl2ulMoEfg+8q5Ta6cWX4RWFFntX7JBQe7JmdExfZLMV+S0mEZjcHQ1qU0otBhZrrd3ukKK1/gZ7wSy+7YViP5uwF7TSx70MvFx6uwg8lgMHMTZqREhcnEfOF5pgb9e3Hj1K6CXBP5dsTctORZRSGcB5oAgo9PeM8EJUxWAwcPvttzN+/PjDWusR7h7nxv1jA/bm0aBVaLFgDA11rdji/N9WWIQxJNSfoYkAU2nNmrKbppQ6A+wB9iqlTiulXqjsOFF/mA/s91itGkBogn3AlzUz02Pn9Bcvlp0btNYpkqiJQKa1Ztq0aTRp0oQuXboA9JD7R0mFFourCRSkZk1UrKpm0EnANcDlWut4rXUccAVwjVLqca9HJwKe5cBBwtp5brBFaEJL+3mPHvXYOf2kGVJ2RD32j3/8g7Vr17JhwwaysrIAtiJloIRCi5mQsIvTHimDffq5orq15J7wgKqStTHAaK31QecGxySF9zqeE/VYYU4ORdnZhLdr77FzGsLDCWnWDGtm0CdrTfBO2dHACqXUJseUBCX4a+5BIUr75JNPSE1NLTEyUu4fJZWtWbMnazLAQJRWVbIWqrU+U3qjo++NNKjXc5aD9jzEkzVrAKGtWmE9fNij5/QD5aWyc43WujdwMzDRsT5i8fPP0lr30Vr3cXTqFsIvrFYrTZo0KbNd7h8XFVosrsEFIDVromJVJWuVzcwns/bVc5YDzmk7PFezBhDWujWWI0eq3jGwVfbVuMZlR2t9zPH/KezzEPat6bmE8KawytcKlvsHZZtBXX3WiqTPmiipqtGgvZRS5U0lr4DarZ0ggp75wEFUWBihLVt69LxhbVpzbtEibAUFGCIjPXpuH4rydNlRSjUADFrr846ffwf8uRYxCuE127ZtIzY2tvimSx1lQu4fDhUNMCgKovWRhW9UmqxprevsgtOi9iwHDhDWpk2tF3AvLcyxJIzl8BEiOnfy6Ll9aJMXRms2BxY5VkMIAT7TWteZpR5E3VJUqnZIKbVFRjCXVGixEBYV5XrsrGUrtErFoyjJ3eWmhCjDfPAAEV26evy8oa3t6z1bDh8K5mTN4xyds3v5Ow4hhGcUWsxENWrsehwSGurYLsmaKElWThY1YrNYsGYe9fjgArA3gwJ1YZCBqJKsNyXqr0JryWZQZ81akdSsiVIkWRM1Yj18GIqKCPfCgvbGmBiMcXFYDkmyVn/IelOi/rGW7rMWJjVronySrIkaMTun7WjrudULigtr3RrLoUNeObcIRFLDJuof+wCDi6NBpc+aqIgka6JGLAecyVqSV84f1qY1FmkGrQekRk3UX/apO4o1gzr6rBVJzZooRZI1USOWAwcIadYMY3S0V84f2ro1hcePYzOZvHJ+IYTwJ601hRYLocWbQR0T5FolWROlSLImasT022+Ed+jgtfOHtUkC6saC7kIIUVpRYSFoXaoZ1J6syQADUZoka6LatMWCed8+Irp5ftoOJ+eIUOm3VtdJXzVRPxVazAClRoOGOZ6TZE2UJMmaqDbzgQNgtRLe1YvJmnNiXBkRWk9I3zVRvzgTspJ91hzJmtSsiVIkWRPVZtq1G4AILyZrxoYNMTZsiOWw1KwJIeqei8naxWZQg9GIwWiU5aZEGZKsiWoz7dmNiowkrE0br14ntE0bmRi33pDmUFG/lNcM6nzsfE4IJ0nWRLWZtu8gonNnj68JWlpYmzZYMqRmrW6T5k9RP5XXDAr2EaHSZ02UJsmaqBab2Yxpxw4ie/f2+rXC2iZhPX6corx8r19L+IvUqIn6yVWzFhpeYntYRAQWmbJIlCLJmqgW044daKuVqMu8n6xFdOkKWmP+7TevX0v4m9Swifqlopq1sIhILAUF/ghJBDBJ1kS1XNi0GYDISy/1+rUiunYB7H3khBCiLrGa7bVnoRERJbaHRUVhKbjgj5BEAJNkTVRLwebNhLVrR0hcnNevFXLJJRgbNsS8e4/XryWEEL5UaHYOMCjdDCo1a6IsSdaE23RRERc2b/ZJEyiAUorwbl0p2LHDJ9cTQghfsTqStdCIUslapNSsibIkWRNuM23fji03lwZXXeWzazbo2xfz7t0U5uT47JrCH2SggahfXM2g4aWaQSMjsZikZk2UJMmacFvemrWgFFG+TNauvhqAC7/84rNrCiGEt7lq1sLLqVm7IDVroiRJ1oTb8tesISI5mZDGjX12zYju3THGx3Puv9/47JrCH2Q0qKhfrGYTBqMRY0hoie1hkZFYzSa0zeanyEQgkmRNuKXo3DkKfv2V6Guv8el1VUgIje68k7xVqzAfOOjTawshhLdYzaYyTaBgH2AASFOoKEGSNeGW8ytXgc1G9HXX+fzacePGYmjQgJMv/wWtpW+TECL4FZrNhJRqAgWIjG0IwIXcc74OSQQwSdaEW3K//YbQli2J6NnT59cOiY+n6eOTyP/5F3KXLvX59YUQwtOsZnOZ/moA0XHxAORlnfF1SCKASbImqlSYk0P+z78Qc/NglPJP36LGo0YReemlnHz1rxRmZfklBuF5UlEq6ivzhXzCoxqU2e5K1rLl75y4SJI1UaVzX3wBhYU0HDrMbzEog4EWf/kzRRcucPof//BbHEII4QmmvPNERMeU2R7jSNbOS7ImipFkTVTKZjaTPfczovr28xzg0wAAIABJREFUJaJzJ7/GEt6hA3F3j+bsF19iTk/3ayzCMwxGe02ttkkVm6hfTHl5RDSILrM9LDKKiOgYck+f8kNUIlBJsiYqlTV7NoXHjxP/x/H+DgWA+Icesg82ePNNf4ciPMBgsCdrtiJJ1kT9YsrPIyK6bLIG0OiSFpw9edzHEYlAFuLvAETgKMrL4/z332PZv5+i3PMU5eRw/rvviL3lFqKv8e2UHRUJadyYJg89xKk33iD/l198upqC8DxnzZqtSOaUEvWH1WKmIPccUQ3Ln7OyUfMWHPtN1kQWF3m1Zk0pNVgptVcptU8pNaWc58OVUvMdz69XSiUVe+4Zx/a9SqlB3oxTwIUNG9g/aDDHpzxD1kcfc/777zHt3EnD4XfS4tVX/B1eCY3vvYfQli05+fob6KIif4fjU1WVqWCjnDVr0gwqSqnN/SPQ/TT3IwDiE1uX+3yjS1py/sxpCq1W3wUlAprXkjWllBH4F3Az0A0YrZTqVmq3B4AcrXUHYAbwmuPYbsAooDswGPi343zCw7TWZM+dy6H77scYE0Obz+bSZdtWOq1dQ4cfvqflyy9jiCg7caM/GcLDaTr5T5h37ybzscewHDni75B8ws0yFVRcNWuSrIlianP/CHRaa9LTfsZgNNLh8ivK3afxJS3Q2kbu6ZM+jk4EKm82g/YF9mmtDwAopeYBw4BdxfYZBkxz/LwQeEfZ54YYBszTWpuBg0qpfY7zyQKRbtBa2+dEcP6z2ezLZBfbpq1WTHv2kP3Rx+StXEn0ddfR8s03MMaUHZ0UiBreeiuFp05z+u9/Z//3PxDeuTORl6YQ2SuFyF69CGvdChVS51r53SlTQUaWmRLlqvH9QwfYzNlaa7TNRlFRIedOnuD4vr3kZWcxeMLjZZaacmqa1A6AtfM/ZfCESeWudCDqF2/ezRKA4lUemUDprxGufbTWhUqpc0C8Y/u6UscmeC/UwJP52CTy16yxJ1bgSrpcyZZzW+l/1WSIiqLZE5OJu/9+lCG4xpvE3zeO2MGDyP3mG/LXriX36/9ydt581/MqIgJlLFUhW948cdWcOy68Y0eSPptbk5Bry50yFVSO7s0BIHN3Nq26xPk5GhFAanP/8Otssr/+sIz/zXkfW2ERNlsRtnK6aoRFRtH+soqLbtPWSVw+bAQblixk34Zfyk3WBj38GB37Xu3R2EXg8mayVt4dsHQ2UdE+7hyLUmo8MB6gdevy2/6DVYOrryb0kuaAsicTBoPjR8dj53al7O+WUo4Ja8vZbjCU2a4MRsLatiWq7+UYKxiRFAxCW7Qg/oEHiH/gAbTNhuXAAQq2bcN68iS283n2BNelbDJbky/hoc2a1SLiWqmyXARbmbj29x1ZsyCdNslN/B2KCCy1uX+U3MnHZSIuoRU9bvgdBqPR8S8Eg9GAwRhCdOM4wiIiadqmbYUjQZ363z2Odil92Ldpfbl9c2ObNvfWSxAByJvJWibQqtjjROBYBftkKqVCgIZAtpvHorWeBcwC6NOnT0BVfddW45F3+TuEoKMMBsI7dCC8Qwd/h+ItVZaLYCsTvQa0oteAVlXvKOqb2tw/SvB1mUjs0p3ELt09c65uPUjs1sMj5xLBzZvtXhuAjkqptkqpMOwDBkov7LgUGOv4eQSw0tHfYCkwyjHapy3QEUjzYqxCBAN3ypQQdUFt7h9C1Dleq1lz9CH4P2A5YAQ+0FrvVEr9GdiotV4KvA/McQwgyMZeIHHs9zn2zqSFwEStdf2ao0GIUioqU34OSwiPq839Q4i6yKvD5bTW3wDflNr2QrGfTcDvKzj2FSCwJvgSws/KK1NC1EW1uX8IUdeoulJrrJQ6DRzydxxuaA0c9ncQbpJYa66N1rqpPwMIkjIRaL+3qgRTvIEWq5QJ9wTa760qwRRvoMXqdpmoM8lasFBKnfb3Hyx3SazC24Lt9xZM8QZTrOKiYPu9BVO8wRRracE1sVbdcNbfAVSDxCq8Ldh+b8EUbzDFKi4Ktt9bMMUbTLGWIMma753zdwDVILEKbwu231swxRtMsYqLgu33FkzxBlOsJUiy5nuz/B1ANUiswtuC7fcWTPEGU6ziomD7vQVTvMEUawnSZ00IIYQQIoBJzZoQQgghRACTZE0IIYQQIoBJsiaEEEIIEcAkWRNCCCGECGCSrAkhhBBCBDBJ1oQQQgghApgka0IIIYQQAUySNSGEEEKIACbJmhBCCCFEAJNkTQghhBAigEmyJoQQQggRwCRZE0IIIYQIYJKsCSGEEEIEMEnWhBBCCCECmCRrQgghhBABTJI1IXxIKTVYKbVXKbVPKTWlnOfDlVLzHc+vV0ollXq+tVIqTyn1hK9iFkII4V+SrAnhI0opI/Av4GagGzBaKdWt1G4PADla6w7ADOC1Us/PAL71dqxCCCEChyRrQvhOX2Cf1vqA1toCzAOGldpnGPCx4+eFwACllAJQSt0OHAB2+iheIYQQASDE3wF4SpMmTXRSUpK/wxACgE2bNp3RWjcttTkBOFLscSZwRUX7aK0LlVLngHilVAHwNDAQqLAJVCk1HhgP0KBBg8u6dOlSq9chhKdUUCZ8Su4TIpBUp0zUmWQtKSmJjRs3+jsMIQBQSh0qb3M527Sb+7wEzNBa5zkq2sqltZ4FzALo06ePljIhAkUFZcKn5D4hAkl1ykSdSdaECAKZQKtijxOBYxXsk6mUCgEaAtnYa+BGKKVeBxoBNqWUSWv9jvfDFkII4U+SrAnhOxuAjkqptsBRYBRwd6l9lgJjgV+AEcBKrbUG+jl3UEpNA/IkURNCiPpBBhj4iLZpzq85irba/B2K8BOtdSHwf8ByYDfwudZ6p1Lqz0qpoY7d3sfeR20f8CegzPQeddmePXvYtWuXv8MQQvjJhQsHOXDgLS5cOOjvUAKK1Kz5yIUtpzj39QFs+VYaDkry6rWsViuZmZmYTCavXkdAREQEiYmJhIaGurW/1vob4JtS214o9rMJ+H0V55hW/UjtAvmzUVRUxPnz5wHYuXMnBoN8lwxG1S0TQuzZ+wJnzvxAbGwK2dk/UlR0gRMnFnPllSswGORzBJKs+Yy2FAFgKyj0+rUyMzOJiYkhKSmJyjqji9rRWpOVlUVmZiZt27b1dzhuCeTPRl5eHrm5uQA0atSIqKgoP0ckqisYy4Twr5ycNI4enUtMTHeys9cQE9ODZk0H81v6n8nJWUd8fL+qT1IPyFfXOshkMhEfHx9wN+O6RilFfHx8QNZSVSSQPxtmsxmj0QhAYaH3v9QIz6tJmZBVPeq3M2e+R6kwLus9j+uv28ZlvVNp2XIUBkMkZ7JW+Tu8gBHQyZpSqpFSaqFSao9SardS6ip/xxQsAvFmXBcF4/sciDFrrbFYLISHhxMSEiLJWhCrzudLVvUQ2TlradSoD0bjxZp0ozGc2NhkcnN/9WNkgSWgkzXgLWCZ1roL0At7p2whRB1jtVrRWhMWFobRaKSoqMjfIQnfkFU96jGbzUJ+/j5iY3uWeS42tid5eTux2Sx+iCzwBGyyppSKBfpjHx2H1tqitT7r36iEu5RSTJ482fX4zTffZNq0abzyyiukpKSQkpKC0Wh0/fzPf/6z3PNMmzaNhIQE134pKSmcPXuW1atX07BhQ9e2m266yXXMJ598Qo8ePejevTvdunXjzTffBGDBggV0794dg8FQYmJMi8XCfffdR3JyMr169WL16tWu566//no6d+7sus6pU6c8/E7VT4sWLUIpxZ49ewB78+zzzz9P37596devHwMHDuTgwYpHgyUlJdGvX8m+LCkpKfTo0cMr8RYWFtKkSROeeeYZj543Ojq63O3/+c9/+OSTTwAYN24cUVFRrsEXAI899hhKKc6cOePReNz16quveupU5a3qkVDRPo4R1c5VPRpgX9XjpcouoJQar5TaqJTaePr0aU/FLTzgwoUMtC4kukGnMs/FRHfDZrNQUHDYD5EFnoBN1oB2wGngQ6XUFqXUbEfhdJFCGLjCw8P58ssvy9xMpk6dytatW9m6dSuRkZGunx999NEKz/X444+79tu6dSuNGjUCoF+/fq5t33//PQDffvst//jHP1ixYgU7d+5k8+bNNGzYEIAePXrw5Zdf0r9//xLnf++99wDYvn073333HZMnT8ZmuzjFyty5c13XadasWe3fHEFqairXXnst8+bNA2D+/PmcPHmSX3/9lfXr1zN79mxiY2MrPcf58+c5csR+n9+927uV7itWrKBz5858/vnn2Ke9866HHnqIMWPGuB536NCBJUuWAGCz2Vi1ahUJCaVzGt/xYLLmkVU9KruA1nqW1rqP1rpP06Z+Xe1KlJKf/xsADcpJ1qKi7ANULlw44NOYAlUgjwYNAXoDj2it1yul3sI+59Tzzh1KL63jlygD3Nmv9mM5lu/Rc4a1bECjIe0r3SckJITx48czY8YMXnnlFY9evzJ//etfefPNN2nZsiVgn0bgD3/4AwBdu3Yt95hdu3YxYMAAAJo1a0ajRo3YuHEjffv29U3QfvLtt99y4sQJj57zkksu4eabb650n7y8PNauXcuqVasYOnQo06ZN4+jRo7Rs2RKDwYDBYKBly5auJLsid911F/Pnz+eJJ54gNTWV0aNHM2fOHMA+DciUKVNYvXo1ZrOZiRMn8sc//pG8vDyGDRtGTk4OVquVl19+mWHDhpGRkcHNN9/Mtddey88//0xCQgJLliwhMjISsCeXjz32GDNnzmTdunVcdZW9+2xSUhJ33303q1atwmq1MmvWLJ555hn27dvHk08+yUMPPcTq1at54YUXiI+PZ+/evfTv359///vfrqlJpk6dytdff01kZCRLliyhefPmTJs2jejoaJ54wt5nfvTo0cyfP597772X1atXc8011/Dttxe7af3973/ngw8+AODBBx9k0qRJZGRkMHjwYK699lrWrVtHr169uO+++3jxxRc5deoUc+fOpW/fvuTn5/PII4+wfft2CgsLmTZtGsOGDeOjjz5i6dKlXLhwgf3793PHHXfw+uuvM2XKFAoKCkhJSaF79+7MnTu3Bp8UF1nVox7Ld8ylFhXVrsxzzmQtP/8AkmMHds1aJpCptV7veLwQe/ImgsTEiROZO3cu586dq9V5ZsyY4WqGvOGGG1zbf/rpJ9d2Z0K4Y8cOLrvssmqdv1evXixZsoTCwkIOHjzIpk2bXDU2APfddx8pKSn85S9/8UmtSl23ePFiBg8eTKdOnYiLi2PDhg3cdtttLF++nJSUFJ599ll27NhRonazPCNGjODLL78E4KuvvmLIkCGu595//30aNmzIhg0b2LBhA++99x4HDx4kIiKCRYsWsXnzZlatWsXkyZNdv9P09HQmTpzIzp07adSoEV988QUABQUF/PDDD9x2222MHj2a1NTUEnG0atWKX375hX79+jFu3DgWLlzIunXreOEF1/R5pKWl8be//Y3t27ezf/9+V9z5+flceeWVbNu2jf79+7tqeUvr2LEjp0+fJicnh9TUVEaNGuV6btOmTXz44YesX7+edevW8d5777FlyxYA9u3bx2OPPcavv/7Knj17+Oyzz1izZg1vvvmmq3bslVde4cYbb2TDhg2sWrWKJ598kvx8+xe8rVu3Mn/+fLZv3878+fM5cuQI06dPd9WK1zJRg2KreiilwrCv6rG01D7OVT2g2KoeWut+WuskrXUS8A/gVUnUgovZdIywsCYYjeFlngsJiSE8rDkXLuz3Q2SBJ2Br1rTWJ5RSR5RSnbXWe4EBgExtXk1V1YB5U2xsLGPGjOGf//ynq4aiJh5//HFXDUNx/fr14+uvv65NiADcf//97N69mz59+tCmTRuuvvpqQkLsRWPu3LkkJCRw/vx5hg8fzpw5c0o0TwWzqmrAvCU1NZVJkyYBMGrUKD777DOefPJJduzYwZo1a/juu+8YOXIkn332WaUxxsXF0bhxY+bNm0fXrl1LzMu2YsUKfv31VxYuXAjAuXPnSE9PJzExkWeffZYff/wRg8HA0aNHOXnyJABt27YlJSUFgMsuu4yMjAwAvv76a2644QaioqIYPnw4f/nLX5gxY4ZrmpGhQ+2LTyQnJ5OXl0dMTAwxMTFERERw9qy9m23fvn1p185eezB69GjWrFnDiBEjCAsL47bbbnNd87vvvqvw9d55553MmzeP9evX8+6777q2r1mzhjvuuIMGDRq49vvpp58YOnQobdu2JTk5GYDu3bszYMAAlFIkJye7Xt+KFStYunSpq2+nyWTi8GF7P6EBAwa4aji7devGoUOHaNWqeEVY7WitC5VSzlU9jMAHzlU9gI1a66XY+y3PcazqkY09oRN1gMl8nPDwFhU+HxXV1lX7Vt8FbLLm8Agw1/GN6wBwn5/jEdU0adIkevfuzX33+eZX1717dzZt2sSNN97o9jEhISHMmDHD9fjqq6+mY8eOAK5+QTExMdx9992kpaXVmWTNH7Kysli5ciU7duxAKeUa9fnEE0/QoEEDbr75ZgYOHEh0dDRLly6tMqEcOXIkEydO5KOPPiqxXWvN22+/zaBBg0ps/+ijjzh9+jSbNm0iNDSUpKQk15xg4eEXv90bjUYKCgoAe3K5du1akpKSXK9h1apVrkEtzuMMBkOJcxgMBtcUJKWns3A+Dg0Ndf1sNBornbJk1KhR9O7dm7Fjx5ZY3aGy2t7S8RSP1XktrTVffPEFnTt3LnHs+vXry7wn3phSxd+regj/MZmO06BBxRUKkVFJnD69wocRBa5AbgZFa73V0TG0p9b6dq11jr9jEtUTFxfHXXfdxfvvv++T6z3zzDM89dRTrr5YZrO5wpGmThcuXHA1+3z33XeEhITQrVs3CgsLXQMkrFYrX3/9tddGG9YXCxcuZMyYMRw6dIiMjAyOHDlC69atSUtLK9F/bteuXSQmJlZ5vjvuuIOnnnqqTFI2aNAgZs6cidVqBeC3334jPz+fc+fO0axZM0JDQ1m1ahWHDh2q9Py5ubmsWbOGw4cPk5GRQUZGBv/617/KNIVWJS0tjYMHD2Kz2Zg/fz7XXntttY4HaN26Na+88goTJkwosb1///4sXrzY9TletGhRmZGylRk0aBBvv/22K+lzNqFWJjQ01PXeClETWmvM5mNEVFazFtkGqzUbqzXXh5EFpkCvWRN1wOTJk3nnnZp3JZkxYwaffvqp6/HixYsr3PeWW27h5MmT3HTTTWitUUpx//33A/bpIh555BFOnz7NrbfeSkpKCsuXL+fUqVMMGjQIg8FAQkKCq5O62Wxm0KBBWK1WioqKuOmmm1yDFUTNpKamMmVKyUnqb731ViZNmkSTJk0wm82AfeSuO+91TEwMTz/9dJntDz74IBkZGfTu3RutNU2bNmXx4sXcc889DBkyhD59+pCSkkKXLl0qPf+XX37JjTfeWKKGadiwYTz11FOuWN1x1VVXMWXKFLZv307//v2544473D62uD/+8Y9ltvXu3Ztx48a5BsQ8+OCDXHrppa5mzqo8//zzTJo0iZ49e6K1JikpqcruBePHj6dnz5707t3bE/3WRD1UWJhLUdEFwiMqTtYio9oAUFBwiNDQZF+FFpBUXekw3adPH1187qxAk/fLMc4u2U+DK1vQ+PYOXr3W7t27Kxz5KDyvvPdbKbVJa93HTyEB5ZeJQPtsaK05ceIEkZGRrilZAE6cOEFERESJbcFq9erVvPnmmx7pXxksgqlMCP84n7eHtLRb6dHjbZo3u6XcffLy9rI+7RZ6dH+L5s1v83GE3ledMhHQzaBCiLpNa43W2jWgw8lgMMgqBkLUYWaTfYaWyppBIyPtg1kuFFTeXaE+kGZQERBeeeUVFixYUGLb73//e6ZOneqniIQvOBOy4h3mnY9tNhtXXHFFmebGOXPmuEY4BoPrr7+e66+/3t9hCBFQTKbjAEREtKxwH6MxirCwZrKKAZKsiQAxdepUSczqIedcas5pMJycoxXXr19f3mFCiCBnMh9HqRDCwppUul9UZBsKLkjNmjSD1lF1pS9ioAvG9zmQYnYmaxXVrIngE0ifLxG4zKZjhIc3RyljpftFRrWRZlAkWauTIiIiyMrKkj+aXqa1Jisri4iICH+H4rZA+2xU1QwaKHEK9wRjmRD+YTIfJyK84iZQp6jINlgspygquuCDqAKXNIPWQYmJiWRmZiKL23tfRESEW/OBBYpA+2yYTCZMJhNnz54tMXGs2WymoKCgzHYR+IKtTAj/MJmO07DhpVXuFxlpn77jQsFhYqIrn2qnLpNkrQ4KDQ2lbdu2/g5DlEMpNRh4C/vSOrO11tNLPR8OfAJcBmQBI7XWGUqpvsAs527ANK31oupeP9A+G0uWLGHfvn1Mnjy5xPatW7eyfPlyHn30UeLi4vwUnRDCG7S2YTafqHRwgZNrrrULh+p1sibNoEL4iLJ3zvgXcDPQDRitlOpWarcHgBytdQdgBvCaY/sOoI/WOgUYDLyrlAr6L1sXLlwosaank3Mt2QsX6nfThxB1kcVyBq2tlU7b4RQVeXFi3PpMkjUhfKcvsE9rfUBrbQHmAcNK7TMM+Njx80JggFJKaa0vaK2dCzNGAHWiM5fFYimxOoCTM4Fzrs8phKg7TGb7tB2VrV7gFBISQ2hoXL0fZCDJmhC+kwAcKfY407Gt3H0cydk5IB5AKXWFUmonsB14qFjy5qKUGq+U2qiU2hgo/dIqYzabCQsLK7O9spq1LVu2VLmmpxAicJncmBC3uMjINlKz5u8AhKhHyuspX7qGrMJ9tNbrtdbdgcuBZ5RSZYbcaa1naa37aK37NG3atNYBe5vFYik3WauoZi0zM5MlS5bw8ccfywoHQgQpU4H9O6tzhYKqyFxrkqwJ4UuZQPG/TonAsYr2cfRJawhkF99Ba70byAd6eC1SH6koWXNO/VC6Zi09PR2wz88WDDWHQoiyCkyZhIQ0IiQkxq39I6PaYDIfp6jIXPXOdZQka0L4zgago1KqrVIqDBgFLC21z1JgrOPnEcBKrbV2HBMCoJRqA3QGMnwTtvdUlKwZDAYiIyPL1KxlZ1/MW48dK53nlu/kyZOsWrWKwsIyrcZCCD8oKDhMZKT707vYBxloTKYjVe5bV0myJoSPOPqY/R+wHNgNfK613qmU+rNSaqhjt/eBeKXUPuBPwBTH9muBbUqprcAiYILW+oxvX4Fnaa0rTNbA3m+tdM1aTk4Obdq0wWAwkJWV5dZ1vv76a/73v/+xffv2WscsPE8pNVgptVcptU8pNaWc58OVUvMdz69XSiU5tvdVSm11/NumlLrD17GLmjGZMomMcK8JFC7OtVaf1wgN+qH/QgQTrfU3wDeltr1Q7GcT8PtyjpsDzPF6gD5UVFSEzWYrdzQo2PutlZesde7cmby8PHJycqq8RmFhIUePHgXg0KFDXHpp1ZNwCt8pNp3NQOxdADYopZZqrXcV2801nY1SahT26WxGcnE6m0KlVAvsX2a+Km/gjQgcWtsoKDhK0yYD3T4mMrI1QL0eESo1a0IIvzCb7f1PKqtZK94MarFYyM/Pp3HjxjRu3LhEk2hFcnJyXGuMHj9+3ANRCw+T6WzqGbP5JFpbiHBzcAFAaGhjQkJi6vUgA0nWhBB+YbFYgIqTtdI1a86atEaNGtG4cWO3atacTaWJiYnk5OTIWqOBx+vT2YjAUmDKBCAywv0+a0opIiPbcKEgw0tRBT5J1oQQfuFOsla8Zs2ZnDVu3JjY2FjMZrPrHBVxJmvt27fHYrHIigiBx+vT2QTb3IN1ncnR78zdaTuc6vtca5KsCSH8oqpkLTIyEqvVitVqBcomawDnz5+v9Brnzp0jPDycFi3sk2+ePXvWI7ELj/H6dDbBNvdgXVdQkAkot9YFLS4qsg0m01FsNqt3AgtwkqwJIfzCnZo1uDgx7tmzZwkLCyMqKoqYGPv8TFUla3l5eURHR7uSu9zcXI/ELjxGprOpZwpMhwkPvwSDofyBRRWJjGqD1kWYTEe9FFlgk2RNCOEXzmStotGgpZecysnJoXHjxiilqp2sRUdHux6LwCHT2dQ/BQWZ1W4CBYiKTAKot/3WZOoOIYRfVDUatHTNWk5ODvHx8QCuZK2qmrK8vDxatGhBgwYNXI9FYJHpbOoXkymTuMZXV/u4qKgkAAouZDiGl9QvUrMmhPALd/qsgb1mTWvtqlkDe21caGholTVr+fn5REdHYzQaiYqKIj8/34OvQAhRHUVFZszmE0Q45k2rjtDQeEJCYsi/cNALkQU+nyRrSqkvlFK3KqWqfT2llFEptUUp9bU3YhOiJoYPH85///tf1xxeovqq02ctLy+PwsJCV7LmbAqtLFmzWCyYzWZXE2iDBg2kZs2LpEyIqjj7m1Vn2g4npRRRUe0okGTNq2YCdwPpSqnpSqku1Tj2Mex9GYQIGA8//DCfffYZHTt2ZMqUKezZs8ffIQUdZ7IWGhpa7vPFa9aKjwR1qipZc9aiOZO16OhoSda8SMqEqEqBqWbTdjhFRbblgiRr3qO1/l5rfQ/QG/tone+UUj8rpe5TSpX/lxpQSiUCtwKzfRGnEO666aabmDt3Lps3byYpKYmBAwdy9dVX8+GHH7qmmhCVc64LqlR502jZk7jQ0FAKCgpKTIjrFBsbW2my5kzMiidr0gzqPVImRFVMBY4JcWuarEUlYTIfo6iooOqd6xif9VlTSsUD44AHgS3AW9iTt+8qOewfwFNAufXqMtmh8KesrCw++ugjZs+ezaWXXspjjz3G5s2bGTjQ/TXv6jOz2VzhSFAn5yoG2dnZKKXK1Kzl5uZWuCqBM1lzDi6QPmveJ2VCVKbAdASDIYywsJrNdxcV1Q6on2uE+mQ0qFLqS6AL9pE7Q7TWzkX65iulNlZwzG3AKa31JqXU9eXto7WeBcwC6NOnj6wjI3zmzjvvZM+ePfy///f/+Oqrr1yTro4cOZJGtJ2yAAAgAElEQVQ+ffr4Obrg4KxZq0zDhg3JycmhqKiIhg0bEhJy8U9WbGwsRUVFrkEEpZWuWYuKisJisVBYWFjiPMIzpEyIqhQUHCEiohU16L4OlBwRGhNdnd5Uwc9Xf7FmO4ZnuyilwrXWZq11RaX4GmCoUuoW7Iv0xiqlPtVa3+vtYIWoyoMPPsgtt9xSYpuzpmjjxo0VNu2Ji9xJ1uLi4ti3bx9Wq9U1bYdTw4YNAfv0HZUla86aNWcfOJPJVO7+onakTIiqmAoyiYys/uACp4gIe/NpfZwY11fNoC+Xs+2Xyg7QWj+jtU7UWidhn9V6pSRqIlA899xzZbZdddVVfogkeLmTrMXHx5OXl8fx48dp0qRJieecqxKcO3eu3GPz8vKIiorCaDQCZSfZFZ4lZUJUpcB0mMiI6k/b4RQaGktISIxrMfj6xKs1a0qpS4AEIFIpdSkXF+SNBaK8eW0hvOHEiRMcPXqUgoICtmzZ4uovlZubK0lANVkslipruIrXpiUkJJR4rnjNWnny8/NdtWpQdpJd4RlSJmrParWSmZmJyWTydyheo7WN2Jg3sVhi2b275hM8NIx9C1OBsVbn8LWIiAgSExMrHPnuDm83gw7CPqggEfh7se3ngWfdPYnWejWw2oNxCVEjy5cv56OPPiIzM5M//elPru0xMTG8+uqrfows+LhTs9a2bdtyfwZctWaVJWvFk0GpWfMOKRO1l5mZSUxMDElJSXW2ubioqID8fBuRka0JDW1Y4/NcuBCFzWYhOrqjB6PzHq01WVlZZGZmlvkbVh1eTda01h8DHyulhmutv/DmtYTwhbFjxzJ27Fi++OILhg8f7u9wgpo7o0EjIyMZNWoUhYWFriWmnAwGA7GxsZU2gxavjZOaNe+QMlF7JpOpTidqADabfV5Fg6HyL2hVMRhCKSrKQ2sdFO+XUor4+HhqO2OFt5tB79VafwokKaX+VPp5rfXfyzlMiID16aefcu+995KRkcHf/17241u8ZkFUzp2aNYAuXSoe9RUbG+t2M6izZk2SNc+SMuEZwZB41IankjVlCENrG1oXoVRwjOr2xO/W26/U+ZdShl6JOsE5T1dNZ8JXSg3GPsegEfso6emlng8HPgEuA7KAkVrrDKXUQGA6EAZYgCe11itr+DL8TmvtdrJWmdjYWA4fPlxmu9VqLdMnLiwsDIPBIM2gHlbbMiHqB5u2oJQRpYy1Oo/BMY++1lZ8N6GF/3m7GfRdx/8vefM6QvjKH//4RwBefPHFah+r7H+l/gUMBDKBDUqppVrrXcV2ewDI0Vp3UEqNAl4DRgJnsM9ReEwp1QNYjn3wTlByzmhf22QtPj6e7du3l0n8nAlE8Zo1+9qCUVKz5mG1KROi/tA2S61r1eBizZzNZsFojKz1+YKFrxZyf10pFauUClVK/aCUOqOUkmk4RNB66qmnyM3NxWq1MmDAAJo0acKnn35a1WF9gX1a6wNaawswDxhWap9hwMeOnxcCA5RSSmu9RWt9zLF9JxDhqIULSlUt4u6upk3tM6GfOXOmxPbSc6w5RUZGSs2al9SwTIgAsmjRIpRSrnVdbTYbjz76KD169CA5OZnLL7+cgwcrXpszKSmJ5ORkkpOT6datG8899xxms9lxLgtKhTFjxgwiIiJcfU1PnTpF27ZtOXHihOs8EyZMYPr06axevRqlFO+//77rua1bd9KwYU/+9rcZgH3S5ZSUFFJSUkhKSiIlJQWwfyEcO3YsycnJdO3alb/+9a+ucyxbtozOnTvToUMHpk+/2Lhxzz330LlzZ3r06MH999/v+lKptebRRx+lQ4cO9OzZk82bN7uOGTx4MI0aNeK2226r2ZvuJl/Ns/Y7rXUucBv2GoVOwJM+urYQHrdixQpiY2P5+uuvSUxM5LfffuONN96o6rAE4Eixx5mUrR1z7aO1LgTOAfGl9hkObNFam0tfIFiWYPNUstasWTOAMp13y6tZA3uyJjVr3lHDMiECSGpqKtdeey3z5s0DYP78+Rw7doxff/2V7du3s2jRohLr85Zn1apVbN++nbS0NA4cOMD48ePRWmOzWTEYQklNTeXyyy9n0aJFgL0MP/300zzxxBMAbN68mTVr1jB58mQAkpOTmT9/vuv8n3++gOTkzmhtc8W4detWtm7dyvDhw7nzzjsBWLBgAWazme3bt7Np0ybeffddMjIyKCoqYuLEiXz77bfs2rWL1NRUdu2yN27cc8897Nmzh+3bt1NQUMDs2fZlyb/99lvS09NJT09n1qxZPPzww654nnzySebMmVPr974qvmrwdU4ucguQqrXOruudKUXd5vzG9c033zB69Gji4uLcOay8D33pZdIq3Ucp1R170+jvyrtAsCzB5vy2XdVo0KrExcVhMBg4efJkie3OZK30PG5RUVFkZ2fX6pqifDUsE6KU3377C+fzPDuHWEx0Vzp1er7SffLy8li7di2rVq1i6NChTJs2jePHj9OiRQsMBnu9TmKi+6sPREdH85///IdWrVqRlXWSsDDNwYNHycvL44033uDVV19l3LhxAIwfP56PP/6YVatWMXXqVN555x3XnGStW7cmNzeXkydP0qxZM5YtW8bA312H/bvsRVprPv/8c1autHflVUqRn59PYWEhBQUFhIWFERsbS1paGh06dKBdO/s6o6NGjWLJkiV069atxAocffv2JTPTPvnukiVLGDNmDEoprrzySs6ePet6bwYMGMDq1avdfl9qylc1a18ppfYAfYAflFJNgbo7+5+o84YMGUKXLl3YuHEjAwYM4PTp00RERFR1WCbQqtjjROBYRfso+1CnhkC243EisAgYo7Xe74GX4TeeqlkzGo0kJCSUGWQgzaC+V8MygVJqsFJqr1Jqn1JqSjnPhyul5jueX6+USnJsH6iU2qSU2u74/0aPv6h6ZPHixQwePJhOnToRFxfH5s2bueuuu/jqq69ISUlh8uTJbNmypVrnjI2NpW3btuzda29WXbBgCaNHj6Zfv37s3buXU6dOAfZpeGbOnMnw4cPp1KkT/fv3L3GeESNGsGDBAn7++Wd69+5NRHgkWheV2Oenn36iefPmdOzY0XVMgwYNaNGiBa1bt+aJJ54gLi6Oo0eP0qrVxT/DiYmJHD1acvkqq9XKnDlzGDx4MIBbx3ibT2rWtNZTlFKvAbla6yKlVD5l++oIETSmT5/O008/TWxsLEajkQYNGrBkyZKqDtsAdFRKtQWOYl9G7e5S+ywFxmJfjm0E9mXWtFKqEfBf4Bmt9VqPvhg/8FSyBtCmTRt+/vnnEoMMcnNziYyMLDNjuHOAQbDM0RRMalImZNBNWVXVgHlLamoqkyZNAuy1Tampqbzxxhvs3buXlStXsnLlSgYMGMCCBQsYMGCA2+fVWjtGbsLnn3/JokWLMRgM3HnnnSxYsICJEycCkJKSQo8ePZgwYUKZc9x1112MHDmSPXv2MHr0aH78cTlQVKIcp6amMnr0aNcxaWlpGI1Gjh07Rk5ODv369eOmm25yrbBRXOm/BRMmTKB///7069fP9RqqOsbbfDnutSv2+daKX/MTH15fCI/avXs3GRkZFBZerI4fM2ZMhftrrQuVUv+H/aZiBD7Q+v+3d+fxUdXn4sc/T0LIAmENIBAIIAhkgVAR3FoRsQK2IAK3eKug0KJXvba22tpNqdeftbbXpT/59br1qqhQlypYUXHBjcqmLLKIoAQJO2HLvs3z+2OWTEL2zMyZ5Xm/Xnklc+bMOU8y55vzzHfVrSJyN7BeVZcBTwKLRGQX7hq1WZ6X3wwMBn4nIt7/5t9V1cOB/r1CIZDJ2qBBg/j444/ZuXMnWVlZgHu9UO9yVP6Sk5Oprq6moqKizU2w5nQtLRP4DboBEBHvoBv/ZG0qsMDz80vAI95BN377+Abd1NeX0zSuoKCA9957jy1btiAiVFdXIyLcf//9JCYmMmnSJCZNmkSvXr149dVXm52sFRYWkpeXx+DB/dmyZQs7d+7i0ksvBdz/AwYNGuRL1sBdw+ZtcvV3xhlnkJCQwNtvv83DDz/MRx+9g2q1b661qqoq/vGPf/Dpp5/6XvP8888zceJEEhIS6NmzJxdccAHr16+nX79+7N1b03U4Pz+fPn36+B7//ve/58iRIzz66KO+benp6Y2+JhRCkqyJyCLgTGAj4K27VCxZMxHqmmuu4auvviI3N9e3ULiINHVjQlWXA8vrbLvT7+cyYGY9r7sHuCcAoYeFQCZrAwYMoEOHDmzZsqVWsta1a9fT9vVfxcCStcBqZZmob9DN2Ib28Xzg8Q668R8C3OigG2A+uPs/mdO99NJLzJ49u1aCctFFF/Hhhx8yZMgQ+vTpg8vlYvPmzYwYMaJZxywqKuLGG2/kiiuuoHPnFF5++S0WLFjAr371K98+AwcOZM+ePWRkZDR5vLvvvpvDhw97rq14oBqXq4K4uHa88847DBs2rFafuv79+/Pee+9x9dVXU1JSwurVq/npT39KZmYmO3fuZPfu3fTt25clS5bw/PPPA/DEE0/w1ltv8e6779ZKGqdMmcIjjzzCrFmzWLNmDZ07d6Z3797N+jsESqhq1kYDmVpfXaIxEWj9+vVs27bNmtJaKZDJWlxcHFlZWXz66aeUlZWRlJTEqVOn6r0B+K8P2tSoNtMyrSwTNugmDCxevJg77qjdXXD69Olce+21dOvWzTcgaMyYMdx8882NHuviiy/2jP50MW3aNH73u99RXb2Pl19+gzfffLvWvtOmTWPJkiX88pe/bDLG888/3/ezd2Jd9wxIKSxZsqRWEyjATTfdxHXXXUd2djaqynXXXedLNB955BEuu+wyqqurmTt3ru9D3g033EBGRgbnnXceAFdeeSV33nknkydPZvny5QwePJiUlBT+93//13eeb3/723zxxRcUFRWRnp7Ok08+yWWXXdbk79NSoUrWtgBnAAdCdD5jgio7O5uDBw+G/NNVtAjUaFCvESNGsHbtWrZu3crw4cMpKyurNxnz1qzZIIPAa2WZaMmgm/xoHnTjpPpGM95yyy3ccsstLTpOXl5evdsLCyvYvv1fJCf3q7W97vJkdeMYN24c48aNO+14v//93RQWbvUtYfXUU0+dtk/Hjh158cUX641n8uTJtUZ+evk33/sTERYuXFjvcx999FG92wMtVMlaGrBNRNYCvmpqVZ0SovMbE1BHjx4lMzOTMWPG1Eo4li1b5mBUkaOiooK4uDhfc1lb9e3bl7S0NDZs2EBaWhpQMwebP1vMPXhaWSZs0E2Uc6/jWRWQ1Qu8ROIQaedL1mJBqJK1BSE6jzEhsWDBAqdDiGjekZuBakYWEUaNGsXbb7/N1q1bgcaTNatZC7zWlAkbdBOZxo4d66sd91q0aBE5OTmn7etyuUeCigQuWQP3slMutWQtoFT1AxHJAIao6jsikoK7YBoTkS666CL27NnDzp07mTBhAiUlJVRXVzf9QgMQkEXc6xoxYgTvvPMOa9eupVOnTnTq1Om0fbzzflmyFnitLRM26MYtkqaTWbNmTbP39SZUgaxZ8x6vuro4oMcMlkB01w/V2qA/xj3k2jvUpC/waijObUwwPP7448yYMcO3iPW+ffu44oorHI4qcgQjWUtNTWX48OEAZGVl1Xvji4+PJykpyZK1ILAy0XpJSUkUFBQE5KYebtQVnGRN4hJwuSp9y06FK1WloKCgWRNENyZUzaA34Z5PZw2Aqu4UkdPbKIyJEAsXLmTt2rWMHeueZWDIkCG+2bhN08rLywOerAFMnTqVzMxMhg0b1uA+3olxTWBZmWi99PR08vPzT1vjNhpUVp6kurqYpKR46h/Y2zrV1cVUVp4gMRFqT98afpKSklq0VFd9QvUblqtqhfeTrmdET/R9hGiOKPzkFIsSExNrJRtVVVUR04QRDoI1KW1iYiLZ2dmN7pOSkmI1a0FgZaL1EhISGDhwoNNhBMXmz2+krHwXo0atCOhxjx9fzWcbrmdU7jN063ZBQI8djkK1NugHIvJrIFlELgVeBF4L0bnDg/3PiioXXXQR9957L6Wlpbz99tvMnDmT73//+06HFTGC0QzaXLY+aHBYmTD1KSvNJzm5bbVK9UlOdk9wXFq6t4k9o0OokrU7gCPA58D1uDuT/jZE5zYm4O677z569OhBTk4Ojz76KJMnT+aee6Kmr3PQOZmsWc1acFiZMPUpLfuG5KTArxyRmNgLkQRKy2IjWQvVaFCXiLwKvKqq0dco3yyeqjVrBY0KcXFxXHHFFVxxxRX06NHD6XAijtPJmvVZCzwrE6auysqTVFUVkhSEmjWReJKS+ljNWiCI2wIROQp8AewQkSMicmdTr4061gwaFVSVBQsWkJaWxrBhwxg6dCg9evTg7rvvdjq0iOJ0slZZWUllZaUj5482ViZMQ0pLvwEgOalfE3u2TnJSP8osWQuInwIXAOeoandV7YZ7kd4LROTWIJ/bmIB76KGHWLVqFevWraOgoIBjx46xZs0aVq1axYMPPuh0eBHB5XJRWVnpaJ81sLnWAsXKhGlIaVk+wGnLTAVKcnK/mGkGDXayNhu4SlV3ezeo6tfA1Z7nGiQi/URkpYhsF5GtIvKTIMdqTJOeeeYZFi9eXGvk1qBBg3j22Wd55plnHIwscngXcQ/GaNDmsFUMAsvKhGmIt9YrWMlaUnI/KiuPU1VVGJTjh5NgJ2sJqnq07kZPv7WEJl5bBfxcVYcD5wI3iUhmEGIMLeuzFtEqKyt9a0/669GjhzWrNZM3WXOyGRRsfdBAsTJhGlJatpd27brQrl1qUI7vTQJjod9asJO1xhbuanRRL1U9oKqfeX4uBLbjXvkgMlmftajQWILhVPIRacIlWbOatcCwMmEaUlq6NyjTdnjVTN/xTdDOES6CPRp0pIicqme7AM1ee0FEBgCj8KyAEMmicTmRWLJp06Z615xUVcrKyhyIKPJ4F4C2PmvRwcqEaUhp6V5SOw4P2vFTkjM859kTtHOEi6Ama6ra5sXaRaQj8DLwU1U9Vee5+cB8gP79Az+PSyCJVa1FBVusve2sz1p0sTJh6uNyVVFWlk/PnpOCdo527VJJSOhGSQzUrIVqUtxWEZEE3Inac6r6j7rPq+pjqjpaVUfbvD7GRAZvzZpTyVp8fDyJiYmWrBkTRGVl+ahWkZI8IKjnSU7uHxM1a2GbrIl7Ubknge2q+oDT8bSZVawZQEQmisgOEdklInfU83yiiPzd8/waTxcARKS7Z3R0kYg8Euq4A8npZA2gY8eOFBUVOXZ+Y6KdN4FKTskI6nlSkjNios9a2CZruOdnuwYYLyIbPV+TnQ6qzazLWswSkXhgITAJyASuqmeE8zzguKoOBh4E/ujZXgb8DrgtROEGjdN91sCSNWOCraQkDyAkNWtlZftxucqDeh6nhW2ypqofq6qo6ghVzfV8LXc6LmPaYAywS1W/VtUKYAkwtc4+U4GnPT+/BFwiIqKqxar6Me6kLaI53WcNIDU1lcLC6J+byRinlJTmER/fgfbtT5/WJZDcI0KV0tJ9QT2P08I2WYs63mZQGw0ay/oC/hMC5XP6dDS+fVS1CjgJdA9JdCFSXl6OiJCQ0NRUi8GTmppKUVGRjc4OA9Y1IDqVluSRnJyBu0dT8HibWaO935ola8aETn3/tepmC83Zp+ETiMwXkfUisv7IkSMtCi5UysvLSUxMDPo/8cZ07NiRyspKX5OscYZ1DYheJaV7SEkZEPTzJMfI9B2WrBkTOvmA/7or6cD+hvYRkXZAZ+BYc08QCSOky8vLHZ8sNTXVPaO6NYU6zroGRCGXq5KysnzfPGjB1D6hO/HxHaJ++g5L1kLGhoMa1gFDRGSgiLQHZgHL6uyzDJjj+XkG8J5GWVtdRUWFo/3VwF2zBtggA+cFvWtAJNQ2R5vS0m9QrSYlZWDTO7eRiHim77BkzQSC5Woxz3OjuRl4C/fyaS+o6lYRuVtEpnh2exLoLiK7gJ8Bvj48IpIHPABcKyL5kbpWrrcZ1ElWsxY2gt41IBJqm6NNcfFOADp0OCsk54uFudaCvdyUqSuq6khMS3lGNC+vs+1Ov5/LgJkNvHZAUIMLEUvWjJ+WdA3Ib03XABN6RcVfAkKHDoNDcr6U5AyOHl2JajXubpDRx2rWjDEhFQ7JWlJSEklJSZw4ccLROIx1DYhGxUVfkpzcj/j45JCcLyXlTFQrorop1GrWQsWaQY0BwiNZA+jatSvHjx93OoyYpqpVIuLtGhAP/M3bNQBYr6rLcHcNWOTpGnAMd0IH+LoGdALai8gVwHdVdVuofw9TW1HxlyFrAgXomDoMgMKi7SHpJ+cES9aMMSFVWlrqW0zdSV27duXQoUNOhxHzrGtAdHG5yiktzaNnj8tCds4OKUMQaUdR4TZ69Yz8hY7qY82goWa19yaGVVZWUlVVRXJyaJpHGtO1a1dOnDiBy+VyOhRjokZR8U5Uq+nYcWjIzhkfn0hKyiAKi7aH7JyhZslaqHgmALVUzcSy0tJSgLBJ1qqrq22QgTEBdOrUZgA6dRoR0vOmpmZSWLglalclsWQtRHxd1qLzOjKmWcItWQMoKChwOBJjosepU5tJSOhKUlK/pncOoC6dR1NRcZSSkt0hPW+oWLIWKjbAwJiwStZ69eoFYP3WjAmgwlOb6ZSaE/Ll5Lp2PReA4ydWh/S8oWLJmjEmZMIpWevYsSMdO3bkwIEDTodiTFSoqiqmqHgnqSFuAgVITh5AYvteHD/+ScjPHQqWrBljQqakpAQgLEaDAvTu3ZuDBw86HYYxUeHkyfWAiy5dzgn5uUWEbt2/Q0HBh7hc5SE/f7BZshZqUdr50ZjmCKeaNYA+ffpw5MgRX1zGmNY7dvxfiLSnS+ezHTl/zx6XUV1dxLFj/3Lk/MFkyVqohbgd35hwUlxcTEJCAgkJCU6HAsCgQYNQVXbvjs5OycaE0vHjn9C586iQrVxQV7du59OuXSqHD7/hyPmDyZK1ECvddCRqhxYb05RTp06Rmpoa8s7HDUlPTycpKYnt26N3fiZjQqG84iiFhdvo1vU8x2KIi0ukR9p3OXzkTaqqih2LIxgsWXNA+Ze2xI2JTYWFhb5F1MNBfHw8I0aMYNu2bRQXR9c/d2NC6eiRtwElrceljsbRp8+/UV1dzOHDy5veOYJYsuYArbQZ001sKiwspFOnTk6HUcs555xDdXU1H3zwgdOhGBOxDh95i+TkDDp2CN3KBfXp3PlsUlIGs2//3x2NI9AsWXNCeLQAGRNSquprBg0nPXr04JxzzmHdunXWd82YVigtzefYsY/p1fNyx7s4iAh9+/yAU6c2+FZTiAaWrBljQqKoqIjq6mo6d+7sdCinmTBhAt27d+eFF16wFQ2MaaH8/GcAoW/fq5wOBYA+fWbSrl0Xvt79F6dDCRhL1owxIXH06FEA0tLSHI7kdImJicyaNQsR4amnnrKEzZhmKizcxt78RZxxxlSSkvo4HQ4A7dqlktF/HgUFKzl2bJXT4QSEJWvGmJAI52QN3HHNnj2bqqoqnnzySWsSNaYexcVfsXHTPD76+Fw++HAUa9dNoX37bgw+85dOh1ZLv35zSUkZxLbtv6SiIvI/fFmy5gSbucPEoP3795OcnBx2fdb8nXHGGcydO5eUlBSefvppli5dSmFhodNhGRMWiou/4tPPruLkyY107z6OM864goyMGxh99oskJvZwOrxa4uOTyMr8byorj7Fp83yqq0ucDqlNwjpZE5GJIrJDRHaJyB1Ox9MmfgmaVlu2FquauqZFJFFE/u55fo2IDPB77lee7TtE5LJQxh0I33zzDf369SMuLqz/7dCjRw9+/OMfc95557Fp0yYefvhh3nrrLYqKipwOLSrFcpmIJCUle9iw4RoARp/9ApnD72PoWXcx+Mzbwqb5s65OnUaQnfUQp05t5rMN11BecdTpkFotbP9rikg8sBCYBGQCV4lIprNRBYZW29QdsaiZ1/Q84LiqDgYeBP7oeW0mMAvIAiYC/89zvIiwf/9+CgoKGDx4sNOhNEtiYiKXXXYZN910E1lZWaxevZqHHnqIFStWWNIWQLFcJiJJUfFOPtvw77i0gm+NWkSHDmc6HVKz9ejxXXJyHqGoaDurV09g1677KSmJvC4O7ZwOoBFjgF2q+jWAiCwBpgLbHI2qlbSqJkFzFVY6GIlxUHOu6anAAs/PLwGPiHss/FRgiaqWA7tFZJfneJ8EO+iysjJKSkpQVd8XUOtxQ18AFRUVvPPOOyQlJZGdnR3scAOqe/fuTJs2jW9/+9t8+OGHfPLJJ6xbt46srCwyMjLo0qULKSkpxMXFERcXh4i0euqC+Pj4sJuDLgQiskyEg6qqQioqjuFutqkpk97HinrWoq798+mPafD5yspjHDv2L/YfeIH4+I6MGvUsHTs6O49aa/TscRkdznmNr77+b77Z+wR7vnmUTp1y6dVzMqmpWSQkdEUkgbi4dgR6bq34+BTat+/e5uOEc7LWF9jr9zgfGOtQLG1WvP6Q7+fSL46RelG6g9EYhzTnmvbto6pVInIS6O7ZvrrOa/sGL9Qan3/+Oa+//nqbjpGQkMCMGTNISUkJUFShlZaWxpVXXsl3vvMdPvroI3bs2MHGjRsDeo7evXtz/fXXB/SYESAiy0Q42H/gJXbuvCfo5xFpzxm9vseZZ/4i7PqltUSHDmcyIuf/UV5+mIMHX+HQodfZueveoJ+3d++ZZA6/r83HCedkrb70tlZnLxGZD8wH6N+/fyhiarXOEwdQvvskxAmJA8NvnikTEk1e043s05zXBqVMDBw4kCuuuMJXa1Tfl+fcDX716dOHpKSkgMTjpLS0NKZNm4bL5eLYsWOcOnWK0tJSXC4XLperTev+Jic7s/i1wyKyTISD7t2+TcLwPwECIgiC70/i/1ji/H7Gb78mXofQrl0qHTsOdWxh9jBudewAACAASURBVGBITOxJRsb1ZGRcT1nZfopLvqaq8iSq1agGvtUrOTkjIMcJ52QtH+jn9zgd2O+/g6o+BjwGMHr06LDutZ84sLMlaabJa9pvn3wRaQd0Bo4187VBKRNpaWlhO92GU+Li4uzvEhgRWSbCQYcOg+nQITL6gIarpKQ+YTs4oq6wHWAArAOGiMhAEWmPuyPpModjMqYtmnNNLwPmeH6eAbyn7uqaZcAsz8i4gcAQYG2I4jYmWKxMGNMMYVuz5umbcDPwFhAP/E1VtzocljGt1tA1LSJ3A+tVdRnwJLDI01n6GO6bF579XsDd8boKuElVqx35RYwJECsTxjSPtKWPRTgZPXq0rl+/3ukwjAFARD5V1dFOxmBlwoQTKxPG1NaSMhE1yZqIHAH2OB1HM/QHvnE6iGayWFsvQ1UdHToVIWUi3N63pkRSvOEWq5WJ5gm3960pkRRvuMXa7DIRNclapBCRI07/w2oui9UEW6S9b5EUbyTFampE2vsWSfFGUqx1hfMAg2h1wukAWsBiNcEWae9bJMUbSbGaGpH2vkVSvJEUay2WrIXeSacDaAGL1QRbpL1vkRRvJMVqakTa+xZJ8UZSrLVYshZ6jzkdQAtYrCbYIu19i6R4IylWUyPS3rdIijeSYq3F+qwZY4wxxoQxq1kzxhhjjAljlqwZY4wxxoQxS9aMMcYYY8KYJWvGGGOMMWHMkjVjjDHGmDBmyZoxxhhjTBizZM0YY4wxJoxZsmaMMcYYE8YsWTPGGGOMCWOWrBljjDHGhDFL1owxxhhjwpgla8YYY4wxYcySNWOMMcaYMGbJmjHGGGNMGHMkWRORiSKyQ0R2icgdDezzbyKyTUS2isjzoY7RGGOMMSYciKqG9oQi8cCXwKVAPrAOuEpVt/ntMwR4ARivqsdFpKeqHg5poMYYY4wxYcCJmrUxwC5V/VpVK4AlwNQ6+/wYWKiqxwEsUTPGGGNMrHIiWesL7PV7nO/Z5u8s4CwRWSUiq0VkYsiiM8YYY4wJI+0cOKfUs61uW2w7YAgwDkgHPhKRbFU9UetAIvOB+QAdOnQ4e9iwYYGP1phW+PTTT4+qag8nY0hLS9MBAwY4GYIxPlYmjKmtJWXCiWQtH+jn9zgd2F/PPqtVtRLYLSI7cCdv6/x3UtXHgMcARo8erevXrw9a0Ma0hIjscTqGAQMGYGXChIuWlAkR6Qc8A5wBuIDHVPXhOvsI8DAwGSgBrlXVzxo7rpUJE05aUiacaAZdBwwRkYEi0h6YBSyrs8+rwMUAIpKGu1n065BGaYwxxilVwM9VdThwLnCTiGTW2WcS7g/xQ3C3sPw1tCEaEzohT9ZUtQq4GXgL2A68oKpbReRuEZni2e0toEBEtgErgdtVtSDUsRpjjAk9VT3grSVT1ULc94q6fZunAs+o22qgi4j0DnGoxoSEI/OsqepyVT1LVc9U1f/j2Xanqi7z/Kyq+jNVzVTVHFVd4kScwaaq3LVrH5+dLHY6FGMcUVBUzm0vbqKsstrpUEyYEpEBwChgTZ2nmjNYDRGZLyLrRWT9kSNHghVmYBQegsfGwT9/BiGeVsuENyf6rBmPUpfy6N4jPJl/hPxxuU6HY0zI/fHNL3jp03xGZ3Rl1pj+TodjwoyIdAReBn6qqqfqPl3PS07LcOr2bQ54kIH01q9g/wb317hfQUdHx2O0WWVlJfn5+ZSVlTkdiqOSkpJIT08nISGh1cewZM1Bp6rctQnxUt//HGOin3jut+F9BzVOEJEE3Inac6r6j3p2ac5gNeOg/Px8UlNTGTBgABKj9zlVpaCggPz8fAYOHNjq49jaoA4q9CRriXGxeREb4/3/bS0+xp9npOeTwHZVfaCB3ZYBs8XtXOCkqh4IWZBBEV33grKyMrp37x6ziRqAiNC9e/c21y5azZqDKu0OZWKcL1mzujVT2wXANcDnIrLRs+3XQH8AVf0fYDnuaTt24Z664zoH4jRNiOVEzSsQfwOrWXNQtSVrJubZP3JzOlX9WFVFVUeoaq7na7mq/o8nUfMORLvJM1AtR1VtAjVzmoMHDzJr1izOPPNMMjMzmTx5Ml9++SXZ2dn17l9VVUVaWhq/+tWvam3/5z//yahRoxg5ciSZmZk8+uijAOzYsYNx48aRm5vL8OHDmT9/flB+D6tZc1CV5WomxlkzqDENsULRVqrKtGnTmDNnDkuWuCeV2LhxI4cOHWrwNStWrGDo0KG88MIL3HvvvYgIlZWVzJ8/n7Vr15Kenk55eTl5eXkA3HLLLdx6661Mnepe4vzzzz8Pyu9iNWsOcnnuUGK1CyZGea98uy0ZYwJt5cqVJCQkcMMNN/i25ebm0q9fvwZfs3jxYn7yk5/Qv39/Vq9eDUBhYSFVVVV0794dgMTERIYOHQrAgQMHSE9P970+JycnGL+K1aw5qdqXrBkTm3xdOaxqzZjo9sYdcDDAtU5n5MCk+xp8esuWLZx99tnNPlxpaSnvvvsujz76KCdOnGDx4sWcd955dOvWjSlTppCRkcEll1zC9773Pa666iri4uK49dZbGT9+POeffz7f/e53ue666+jSpUsgfrtarGbNQd5mUEvWTKyyqTuM8WOd8R31z3/+k4svvpiUlBSmT5/OK6+8QnW1e9aGJ554gnfffZcxY8bw5z//mblz5wJw3XXXsX37dmbOnMn777/PueeeS3l5ecBjs5o1B7nsFmUMYBVrxkS9RmrAgiUrK4uXXnqp2fsvXryYVatWMWDAAAAKCgpYuXIlEyZMANxNnDk5OVxzzTUMHDiQp556CoA+ffowd+5c5s6dS3Z2dotr9JrDatYcVGV3KBPjagYYWFkwxgTW+PHjKS8v5/HHH/dtW7duHXv27Dlt31OnTvHxxx/zzTffkJeXR15eHgsXLmTx4sUUFRXx/vvv+/bduHEjGRkZALz55ptUVlYC7pGnBQUF9O172qpnbWbJmoOqvc2gVvNtYpQNMDCmAfYBps1EhFdeeYW3336bM888k6ysLBYsWECfPn3YsWMH6enpvq9HH32U8ePHk5iY6Hv91KlTWbZsGdXV1dx///0MHTqU3Nxc7rrrLl+t2ooVK8jOzmbkyJFcdtll/OlPf+KMM84I+O9izaAOsnnWTKzzThZpRcEYsB7MgdenTx9eeOGF07Z7a8Ma061bN44cOQLA8uXL693ngQce4IEHGlpkI3CsZs1B1TZ1hzHGGGOaYMmag6qtNsEYwJpBjTGmMZasOajablGmBUSkn4isFJHtIrJVRH7idExtZQMMjPFjHZhNA6zPmoOqbZ410zJVwM9V9TMRSQU+FZG3VXWb04G1lnUBMKYh0fEBRlVjfjH3QHwYtZo1B/n6rMX2dWyaSVUPqOpnnp8Lge1A4MeIG2NMACQlJVFQUBDTNeeqSkFBAUlJSW06jtWsOajKlpsyrSQiA4BRwBpnI2kb+6BiTPRKT08nPz/fN6IyViUlJdVaP7Q1LFlzkCt2P2yYNhCRjsDLwE9V9VSd5+YD8wH69+/vQHStE8MfvI3xE12fXhISEhg4cKDTYUQFawZ1kM2zZlpKRBJwJ2rPqeo/6j6vqo+p6mhVHd2jR4/QB9hC0XVrMiaA7P5g/Fiy5iBrBjUtIe5euk8C21U1+LMwhpBGSWdqY4wJBkvWHOTyfLcRcaaZLgCuAcaLyEbP12Sng2oL67NmjDFNsz5rDqpy2WhQ03yq+jFRVhFry02Z+ojI34DvAYdVNbue58cBS4Hdnk3/UNW7QxdhkNS6GVihMDUsWXNQtdMBGOMwW8jdNOAp4BHgmUb2+UhVvxeacBxgn2CMH2sGdZDLCqMxgN2XTG2q+iFwzOk4jAkXlqw5yAYYmJhnF79pvfNEZJOIvCEiWQ3tJCLzRWS9iKwP//m+rBnU1M+SNQfZQu7GuNloUNNCnwEZqjoS+L/Aqw3tGGnT2fhYdbPxY8mag7zzrNnkuCZW2Uho0xqqekpVizw/LwcSRCTN4bACzG4MpoYjyZqITBSRHSKyS0TuaGS/GSKiIjI6lPGFijdZq7ZCaWKcVSKYlhCRMzzzDiIiY3DfywqcjcqY4An5aFARiQcWApcC+cA6EVmmqtvq7JcK3EKEr33YGO9oUFvJwMQqm7bG1EdEFgPjgDQRyQfuAhIAVPV/gBnAf4hIFVAKzNJoWC3cv0BEwa9jAseJqTvGALtU9WsAEVkCTAW21dnvv4D7gdtCG17oWDOoiXWWq5n6qOpVTTz/CO6pPaKY3RhMDSeaQfsCe/0e53u2+YjIKKCfqv6zsQNF1iif03mTNKtZM7HKW5EQDZUixhgTLE4ka/V9mPb9pxaROOBB4OdNHShiR/l41PRZMyY2eQcYWK5mTB1WKIwfJ5K1fKCf3+N0YL/f41QgG3hfRPKAc4Fl0TjIwPqsGeNmJcCYuqxUmBpOJGvrgCEiMlBE2gOzgGXeJ1X1pKqmqeoAVR0ArAamqOp6B2INKu8KBpasmVhlAwyMaYDdF4yfkCdrqloF3Ay8BWwHXlDVrSJyt4hMCXU8TvI1g1qZNDHO7kvGgA25MQ1p02hQEXkZ+Bvwhqq6mvs6zySGy+tsu7OBfce1JcZw5k3SFHcHa7FqhpjR2rITbeyKj37Tp09n7ty5TodhTERra83aX4F/B3aKyH0iMiwAMcUM/8lwrXYt5ljZ8WPLTUWv//iP/+D5558HyLZrvQWsutn4aVOypqrvqOoPgW8BecDbIvIvEblORBICEWA0859fzVYxiC1WdjysNjnqTZgwgeeeew7c3V7yiNVrvTmsOJgGtLnPmoh0B64FfgRsAB7GfQN6u63HjnYuv09OVfYpKuZY2am5N9nlH90KCgoAuhPD13rLWaEwNdraZ+0fwDBgEfB9VT3geervIhJ1ozcDzX9+NVvFILZY2XHzTYrrbBgmiK688kq++OILcFcOxOy13mL2Ccb4aetyU094Bgv4iEiiqparatTNixZo/lN22PQdMcfKjj+7/qPWj370IyZPnoyIHPQmajF9rTeblQlTo63NoPfUs+2TNh4zZtTqs2blMtZY2aFmBQMTvX7729/WtznmrvXmsfJg6teqmjUROQP3ep7JnnU8vVdYJyAlQLFFncrKSvLz8ykrKwNgrquKq7q4/3QHdn3JYetsHXGSkpJIT08nIaF5/aSt7NSWIC5+853u5KSVs337dqfDMQHgLRMFBQXs27eP0tJSNmzYAJAiIt8iRq/1FrPaZuOntc2gl+HuGJ0OPOC3vRD4dRtjilr5+fmkpqYyYMAARISvS8oprHL3XBvaMYmEOCcWlDCtpaoUFBSQn5/PwIEDm/syKzt+hnWqpHOnPvTp1YPeXZKdDse0kX+Z+PDDD3nqqafIz8/nZz/7Gbiv+f8mRq/1lrNkzdRoVbKmqk8DT4vIdFV9OcAxRa2ysjJfouZWUxitWEYeEaF79+4cOXKk2a+xslNbh3gX7VJSbULoKOFfJubMmcOcOXN4+eWXmT59OiLypape7HSMxkSi1jaDXq2qzwIDRORndZ9X1QfqeZmBWjcl/wTNkrXI1NIkw8pOHdLyv6EJb97389lnn+Xqq68mLy+PBx54AKCX/zUfc9d6c/iXBWsGNX5a2wzawfO9Y6ACiUXa4AMTxazs1MsKQLQpLi4GoKioyLspDkh1Kp7IY2XC1GhtM+ijnu+/D2w4MUbr/dFEMSs7tVmdWvS6/vrrAbjrrrsAWLBgwQG77lvAataMnzb1aBeR+0Wkk4gkiMi7InJURK4OVHDRzsmi+MorryAi3skqcblc3HLLLWRnZ5OTk8M555zD7t27G3z9gAEDyMnJITc3l9zcXP71r3/x/vvv873vfa/Wftdeey0vvfQSAOPGjWP9evccmHl5eQwZMoS33nqLkpISfvjDH5KTk0N2djYXXnih79P4m2++ydChQxk8eDD33Xef77iPPPIIgwcPRkQ4evSob7uqcssttzB48GBGjBjBZ5995ntu4sSJdOnS5bQYGzrWc889x4gRIxgxYgTnn38+mzZtatHfuDFWdmpzsiwcPHiQWbNmceaZZ5KZmcnFF19MSkoKubm5dOvWjYEDB5Kbm8uECRPqfX1eXh7Jycnk5uaSmZnJ7NmzqaysrLXPT37yE/r27YvL5aq1/Y033mD06NEMHz6cYcOGcdtttwGwY8cOxo0bR25uLsOHD2f+/PmAe0T5nDlzyMnJYfjw4fzhD38AYO/evVx88cUMHz6crKwsHn74Yd85jh07xqWXXsqQIUO49NJLOX78OABffPEF5513HomJifz5z3+uFVdD5c7rP//zP+nYsXmVw7/4xS84deoUgDT3WheRv4nIYRHZ0sDzIiJ/EZFdIrLZM8o0CtjHF9MAVW31F7DR830a8DTQDdjUlmO29uvss8/WcLdt27Zaj78sKtWNJ4t148liLamqDmksM2fO1AsvvFDvuusuVVV9/vnndfr06Vpd7Y5j7969euzYsQZfn5GRoUeOHKm1beXKlXr55ZfX2jZnzhx98cUXVVX1oosu0nXr1unevXv1rLPO0qVLl6qq6r333qu33nqr7zVffPGFlpWVaVVVlQ4aNEi/+uorLS8v1xEjRujWrVtVVfWzzz7T3bt3nxbH66+/rhMnTlSXy6WffPKJjhkzxvfcO++8o8uWLTstxoaOtWrVKt/fYPny5bWO5a/u+6qqCqxXh8tOJJSJNZ9t0k17j+v+EyWOnN/lcum5556rf/3rX33bNmzYoB9++KGq1r5+G7J7927NyspSVdWqqiq9+OKL9dlnn/U9X11drf369dOxY8fqypUrfds///xzHTRokG7fvl1VVSsrK3XhwoWqqvrd735XX331Vd++mzdvVlXV5557Tn/wgx+oqmpxcbFmZGTo7t27df/+/frpp5+qquqpU6d0yJAhvrJy++236x/+8AdVVf3DH/6gv/jFL1RV9dChQ7p27Vr99a9/rX/6059852qs3Kmqrlu3Tq+++mrt0KFDg38T/zIxcuRIVVUFdjX3Wge+g3s5qi0NPD8ZeAN3dnMusKax42mklIml/6l6Vyf314HNTkdjgqyp+4T/V1tXMPBOLjUZWKyqx6yzcPP8bmc+604W+ybGTY6PI66Nf7rsjsn815D0JvcrKipi1apVrFy5kilTprBgwQIOHDhA7969ifNMH5Ke3vRxWuPgwYPMnj2be+65hylTpgBw4MABMjIyfPsMHToUgE8++YTBgwczaNAgAGbNmsXSpUvJzMxk1KhR9R5/6dKlzJ49GxHh3HPP5cSJE77f7ZJLLuH9998/7TUNHev888/3/XzuueeSn5/fqt+5AVZ26vj9a1vZtv9UQI+Z2acTd30/q8HnV65cSUJCAjfccINvW25ubqvPFx8fz5gxY9i3b1+tc2RnZ/ODH/yAxYsXM27cOADuv/9+fvOb3zBs2DAA2rVrx4033gi4y4R/GczJyQHcnfeLi4upqqqitLSU9u3b06lTJ7p160bv3r0BSE1NZfjw4ezbt4/MzEyWLl3qu+7nzJnDuHHj+OMf/0jPnj3p2bMnr7/+eq3fYe3atQ2Wu+rqam6//Xaef/55XnnllWb9TfxqGTvTzGtdVT8UkQGN7DIVeMZzw1stIl1EpLfWLGUV+awZ1Php68Rer4nIF8Bo4F0R6QGUtT0sE0yvvvoqEydO5KyzzqJbt2589tln/Nu//RuvvfYaubm5/PznP/dOYtmoiy++mNzcXMaOHdvsc8+ePZubb76ZmTNn+rbNnTuXP/7xj5x33nn89re/ZefOnQDs27ePfv36+fZLT0+vdROsT2te0xxPPvkkkyZNavNx/FjZCQNbtmzh7LPPDtjxysrKWLNmDRMnTvRtW7x4MVdddRXTpk3jn//8py95aezct956K+PHj2fSpEk8+OCDnDhxAoAZM2bQoUMHevfuTf/+/bntttvo1q1brdfm5eWxYcMGX7k8dOiQL5Hr3bs3hw8fbvR3aKwMPfLII0yZMsV3vOb4/ve/701IOxC4a70vsNfvcb5n22lEZL6IrBeR9S2ZZseYcNKmmjVVvUNE/gicUtVqESnG/YnHNOG/hqSzo7iUcpeiCoNTEunQLj4k5168eDE//elPAfen5sWLF/OnP/2JHTt28N577/Hee+9xySWX8OKLL3LJJZc0eJyVK1eSlpbme9zQp2X/7RMmTGDRokVce+21pKS4JzHPzc3l66+/ZsWKFbzzzjucc845fPLJJ97mjgaPVZ/WvKYpK1eu5Mknn+Tjjz9u03H8Wdk5XWM1YOHuq6++Ijc3l507dzJjxgxGjBgBQEVFBcuXL+fBBx8kNTWVsWPHsmLFCi6//PJGj3fddddx2WWX8eabb7J06VIeffRRNm3axPr164mPj2f//v0cP36cb3/720yYMMFXC1ZUVMT06dN56KGH6NSpU6t+l4bK0P79+3nxxRfrrZ1uzH333ccvf/lLunXrtk1VKwN0rddXqOutilLVx4DHAEaPHh3e1VW1/leFd6gmtNraDAowHPecUf7HeiYAx40JQmiLZEFBAe+99x5btmxBRKiurkZEuP/++0lMTGTSpElMmjSJXr168eqrrzaarNXVvXt3X+dlr2PHjtVK6H7xi1/w7LPPMnPmTJYuXUq7du7LpmPHjlx55ZVceeWVxMXFsXz5cs4//3z27q358Jyfn0+fPn0ajSE9Pb3Fr2nM5s2b+dGPfsQbb7xB9+7dW32cBljZcVhWVpZvAExbnHnmmWzcuJEDBw4wbtw4li1bxpQpU3jzzTc5efKkrxmzpKSElJQULr/8crKysvj0008ZOXJkvcfs06cPc+fOZe7cuWRnZ7Nlyxaef/55Jk6cSEJCAj179uSCCy5g/fr1DBo0iMrKSqZPn84Pf/hDrrzySt9xevXq5esKcODAAXr27Nno79JQGdqwYQO7du1i8ODBvt9l8ODB7Nq1q8m/j2cpsW4iMttvc1uu9Xygn9/jdGB/G44XfqwZ1Php62jQRcCfgQuBczxfowMQV0xQrfl4GKpi+dJLLzF79mz27NlDXl4ee/fuZeDAgXz44Yfs3+/+X+dyudi8eXOtfmTNMWTIEPbv3+9b43HPnj1s2rTptD5ADz74IJ06dWLevHmoKqtWrfIleRUVFWzbto2MjAzOOeccdu7cye7du6moqGDJkiW+fm4NmTJlCs888wyqyurVq+ncuXOLmmz8ffPNN1x55ZUsWrSIs846q1XHaIiVnfAwfvx4ysvLefzxx33b1q1bxwcffNCq4/Xu3Zv77rvPN0pz8eLFPPHEE+Tl5ZGXl8fu3btZsWIFJSUl3H777dx77718+eWXgLvceSaP5c033/Q1lx48eJCCggL69u1L//79ee+991BViouLWb16NcOGDUNVmTdvHsOHD/cu7eQzZcoUnn76aQCefvpppk5tvFKroXJ3+eWXc/DgQd/vkpKS0qxE7ZprrvGOcu1I4K71ZcBsz6jQc4GTUdVfDbCaNVNLc0ci1PcFbAekLccI1FfYj/LR00cNbi8s0S2FJbrxZLGeqqwKSQwXXXSRvvHGG7W2PfzwwzpgwAD91re+pVlZWZqVlaXXXXedlpaWNnic+kaDqqp+/PHHOnbsWB05cqSOHj1aV6xYUevc69atU1XV8vJyvfTSS/W2227Tp59+WnNycjQ7O1szMzP19ttvV5fLparu0Z1DhgzRQYMG6T333FMr5r59+2p8fLz27t1b582bp6ru0X033nijDho0SLOzs33nU1W98MILNS0tTZOSkrRv37765ptvNnqsefPmaZcuXXTkyJE6cuRIbegaa+Vo0KCXnUgoE06PBlVV3bdvn86cOVMHDRqkmZmZOnnyZP3yyy9VteWjQVXd1+CIESP0/fff165du+rJkydr7T9t2jRdsmSJqqq+9tpr+q1vfUuHDRumw4cP19tuu01VVW+99VY966yzdMSIETpixAhdtGiRqqoWFhbqjBkzNDMzU4cPH67333+/qqp+9NFHCmhOTo7ven399ddVVfXo0aM6fvx4HTx4sI4fP14LCgpUVfXAgQPat29fTU1N1c6dO2vfvn19sTZU7vw1dzTosGHD1OVytWjkG7AYOABU4q5FmwfcANzgeV6AhcBXwOfA6OYcN+zLxLJbakaD7vvM6WhMkLWkTIi2oapVRF4EbtEw+EQzevRo9c7hFa62b9/O8OHDax4XlaJApUsZlJJIaoj6rJnAqvu+AojIp6raYO1BKMpOJJSJtRs2k9SjPz06JtpC7lHEv0zMnDmTv/zlL/Tp06fRMhEKYV8mXvsJfPqU++cfr4S+UTJ9nKlXU/cJf23ts5YGbBORtUC5d6OqNt5WZQB3JXeom0FN2LCyY2LC0aNHyczMBBgiIsu82+1ab4rdFUyNtiZrCwIRRCwL95m1xo4dS3l5ea1tixYt8nWYNq22oKUvEJG/Ad8DDqtqdsAjclAk3JY+//xzrrnmmlrbEhMTWbNmjUMRRYYFCxYAMG7cuAPAfzsaTNgL9zuCcUpbp+74QEQygCGq+o6IpADWltcIVfVNJaF4p5Xw9dMIO3Yjalxr37dWlp2ngEeIqhGjErbXfl05OTls3LjR6TDCXt3386KLLmLPnj3g7qP5gd0nmikyioUJkbaOBv0x8BLwqGdTX+DVtgYVrZKSkigoKKj1z8w+R0UuVaWgoICkpKQWv7Y1ZUdVPwSOtfhkYazUFUdVyamISdhM4+orE48//jgzZswA8A4vt/tEs1iZMDXa2gx6EzAGWAOgqjtFpPFJfGJYeno6+fn5eGfRPlhWQbs4odylVCW0IyW+rQtKmFBLSkpq7dJcQSk7IjIfmA/Qv3//th4u6HaXJFJ56ADZRSc5lZLQ9AtM2KtbJhYuXMjatWtJTEx0gd0nGuU/Ka59gDF+2pqslatqhbdZzzO5p11hDUhISGDgwIG+x9M++pycWj3JMgAADNxJREFU1GQ+OlHE/2T24YpeXR2MzoRYUMqORtJs7UAVcfyfDwuYe0En7vz+8KZfYCJOYmIi7du39z22+4QxLdfWqpwPROTXQLKIXAq8CLzW9rBiQ7UqCZ6btcvhWEzIWdkxMeGiiy7i3nvvBRC71lvC8llTo63J2h3AEdyTEl4PLAd+29SLRGSiiOwQkV0ickc9z/9MRLaJyGYRedfTETvqVAPt49zJWrVVeceaVpWdaKV2Y4pa9913Hz169AAoxa715rN7gvHTpmRNVV24O4reqKozVPVxbaKnsIjE4555ehKQCVwlIpl1dtuAe0bqEbg7Yd/fljjDlUuVBHG/BZasxZZWlp3FwCfAUBHJF5F5oYg1mHwjo+3yj1pxcXFcccUVAN8091qPXTbkzNSvVcmaZz22BSJyFPgC2CEiR0Tkzma8fAywS1W/VtUKYAlQa7E6VV2pqiWeh6txL9IbdaoVEjw1ay771xUT2lJ2VPUqVe2tqgmqmq6qTwY/4uCye3b0UlUWLFhAWloaw4YNA8huwX3CWG2z8dPamrWfAhcA56hqd1XtBowFLhCRW5t4bV9gr9/jfM+2hswD3mhlnGHNv89ald20YkVbyo4xEeOhhx5i1apVrFu3joKCAoCN2LXefHZPMH5am6zNBq5S1d3eDar6NXC157nG1FfPW+9VKSJXA6OBPzXw/HwRWS8i673TYUQSF5Do7bPmbCgmdNpSdoyJGM888wyLFy+uNQLervWWsGTN1GhtspagqkfrblTVI0BTkyXlA/38HqcD++vuJCITgN8AU1S1vO7znvM9pqqjVXW0pwNrxHB5PjV5a9asz1rMaEvZiTp22UevyspK0tLSTtseq9e6MW3R2mStopXPAazDvaDvQBFpD8wClvnvICKjcM/sPkVVD7cyxrBW7blJ1fRZs7tWjGhL2Yk6Ngo0evnPrVaPmLvWW8zuCcZPayfFHSkip+rZLkCja++oapWI3Ay8hXt9uL+p6lYRuRtYr6rLcDd7dgRe9IwW+0ZVp7Qy1rDkrUlr76tZczIaE0KtLjvRzAYaRJ9NmzbRqVMn/02jPNd+TF/rzWdlwtRoVbKmqm1ahFdVl+Oea8d/251+P09oy/EjQbWnICbYPGsxpa1lJ9rYZR+9qqtr98QVkQ2qOtqhcIyJaLYYpUO8U3W0982z5mAwxjjMLn9j6rBPMsaPJWsO8TWD+kaDWsE0scd71VufTWPqsjJhaliy5pC6AwysGdTEIrvsTUOasSzhtZ5Jdjd6vn7kRJxBY4XD+GntAAPTRi7Pp6Z4EQRbwcDEtmqX0xGYcOK3LOGluKd7Wiciy1R1W51d/66qN4c8wGARW27K1M9q1hzirVmLB+LFatZMbPJO3WGjQU0dTS5LGJVqlQMrE6aGJWsO8SZn8SK0E7EVDExM8t6brM+aqaO5yxJOF5HNIvKSiPSr5/nIXenGyoTxY8maQ7zJWpxAnIitDWpimjWDmjqasyzha8AAVR0BvAM8Xd+BImqlG2sGNQ2wZM0h3ntTvAjxWM2CiW3WDGrqaHJZQlUt8FuK8HHg7BDFFjzWDGoaYMmaQ7w1ae1EiBexedZMTPImafZhxdTRnGUJe/s9nAJsD2F8wWdlwvix0aAO8U/W4myAgYlx9mHF+GvmsoS3iMgUoAo4BlzrWMCBUqsZ1AqFqWHJmkOqXN5kzd0Ual12TCyyAQamIc1YlvBXwK9CHZcxTrBmUIdUeu5N7byjQe1mZWKQ96q3PmvG1GFFwvixZM0h3mbQBBHi/B4bE4uqbVZoY+qwMmFqWLLmEF+yFuceYGD3KhOLappBnY3DmLBjH+CNH0vWHFLTZ01sBQMTs7wrGLgsWzPGmAZZsuaQSr9m0HhbwcDEOPuwYkxdViZMDUvWHFLlt9xUHDbAwMQmawY1pgF2TzB+LFlzSKVfn7V21gxqYpw1gxpjTMMsWXOIrWBgTE1Dj40GNaYuKxOmhiVrDvEOMEiwFQxMLPNc93b9G1OHlQnjx5I1h1T6atawqTtMzLNmUGMAbLkpUz9L1hxS5SmHCXFCPEK1FUwTg3zNoFaLYAyWoJmGWLLmkEq/Pmvt44QKq1kwMcg3GtSuf2Nqsw8wxo8law4prXYv3Z4SF0dKfBwl1baUu4ldVrNmDFgzqGmIJWsO8SZrSfFxJFuyZmKUdwUDu/yNAUvQTEMsWXNIictFomdd0JS4OEpcdrcysceaQY1pgNU2Gz+WrDmkuNpFh3j3n9+aQU2ssgEGxjTEyoSpYcmaQ45UVJKWkABYsmZil7dGzWrWjAHU7z5gH2CMH0vWHHKkoooe7dsB7mStUpVKu2GZGONducBq1owBXNVOR2DClCPJmohMFJEdIrJLRO6o5/lEEfm75/k1IjIg9FEG1+GKSnp6k7U499tQUm0F1TSuqbITabyfT6psvTVTR0zeJ/xr1qwZ1PgJebImIvHAQmASkAlcJSKZdXabBxxX1cHAg8AfQxtlcKkqh8qr6Nm+phkUsEEGplHNLDsRxeWpUTteUoFa7ZrxiNn7hH8ZsPJg/DhRszYG2KWqX6tqBbAEmFpnn6nA056fXwIuEREhwuWXVbDs8Ake3HOIUpeLQSmJAKS2iwfgmX0FvH7kBK8fOWE3LlOf5pSdiHGytJJVu44CUFJRzYpthxyOyISR2LxPVJX5PbB7gKnhRLLWF9jr9zjfs63efVS1CjgJdK97IBGZLyLrRWT9kSNHghRu4PzrRBHzt+Zx/+6DtBfhwq4dAbiwa0d6tW/Hg3sOMW9LHvO25GF1bKYeTZadSCoTe4+VsPNwke/xEx997WA0JszE3n3C5YJDW2oeH9jsXCwm7LRz4Jz1ffKp+xGiOfugqo8BjwGMHj067D+GXNq9EyvPGUqH+Di6JbSjo6dGrUf7BNafl8XXpeW+jtY28sPUo8lyEUll4qxeqbx284WcdUZHDp0s902QawyxeJ+Ii4N//ztUlkHZCeh7ttMRmTDiRLKWD/Tze5wO7G9gn3wRaQd0Bo6FJrzg6ZrQjq4J9f/JE+KEoR2SQhyRiTDNKTsRo327OHLSOwPQv3uKw9GYMBOb94muA5yOwIQpJypw1gFDRGSgiLQHZgHL6uyzDJjj+XkG8J5aJy5jmlN2jIkGdp8wxk/Ia9ZUtUpEbgbeAuKBv6nqVhG5G1ivqsuAJ4FFIrIL9yelWaGO05hw01DZcTgsYwLO7hPG1OZEMyiquhxYXmfbnX4/lwEzQx2XMeGuvrJjTDSy+4QxNSRaao1F5Aiwx+k4mqE/8I3TQTSTxdp6Garaw8kAIqRMhNv71pRIijfcYrUy0Tzh9r41JZLiDbdYm10moiZZixQicsTpf1jNZbGaYIu09y2S4o2kWE2NSHvfIineSIq1LpshIvROOB1AC1isJtgi7X2LpHgjKVZTI9Let0iKN5JircWStdA76XQALWCxmmCLtPctkuKNpFhNjUh73yIp3kiKtRZL1kLvMacDaAGL1QRbpL1vkRRvJMVqakTa+xZJ8UZSrLVYnzVjjDHGmDBmNWvGGGOMMWHMkrUQEZGJIrJDRHaJyB1Ox1OXiOSJyOcislFE1nu2dRORt0Vkp+d7Vwfj+5uIHBaRLX7b6o1P3P7i+VtvFpFvORW3aZiViTbHZ2UiyliZaHN8UVsmLFkLARGJBxYCk4BM4CoRyXQ2qnpdrKq5qjra8/gO4F1VHQK863nslKeAiXW2NRTfJGCI52s+8NcQxWiaycpEQDyFlYmoYWUiIJ4iSsuEJWuhMQbYpapfq2oFsASY6nBMzTEVeNrz89PAFU4FoqofcvoizQ3FNxV4Rt1WA11EpHdoIjXNZGWijaxMRB0rE20UzWXCkrXQ6Avs9Xuc79kWThRYISKfish8z7ZeqnoAwPO9p2PR1a+h+CLh7x3rIuE9sjJhQikS3iMrEw5xZG3QGCT1bAu3YbgXqOp+EekJvC0iXzgdUBtEwt871kXCe2RlwoRSJLxHViYcYjVroZEP9PN7nA7sdyiWeqnqfs/3w8AruKvkD3mrhT3fDzsXYb0aii/s/94m/N8jKxMmxML+PbIy4RxL1kJjHTBERAaKSHtgFrDM4Zh8RKSDiKR6fwa+C2zBHeMcz25zgKXORNighuJbBsz2jPY5FzjprQY3YcPKRHBYmYhcViaCIzrKhKraVwi+gMnAl8BXwG+cjqdObIOATZ6vrd74gO64R8/s9Hzv5mCMi4EDQCXuT0TzGooPd/X2Qs/f+nNgtNN/Y/uq9z21MtG2GK1MRNmXlYk2xxi1ZcJWMDDGGGOMCWPWDGqMMcYYE8YsWTPGGGOMCWOWrBljjDHGhDFL1owxxhhjwpgla8YYY4wxYcySNWOMMcaYMGbJmjHGGGNMGLNkzRhjjDEmjP1/0N0+T8Wvu1EAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 720x720 with 12 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n",
-    "\n",
-    "Tranformation possibilities\n",
-    "* using the minimum and maximum scaler\n",
-    "* standardisation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Data transformation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# converting Pandas dataframe to ndArray\n",
-    "dataArray = data.to_numpy()\n",
-    "\n",
-    "# seperating the predictor and response variables\n",
-    "target = dataArray[:,11]\n",
-    "predictors = dataArray[:,0:11]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# using minMaxScaler to set all values between 0 and 1\n",
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "scaler = MinMaxScaler(feature_range=(0,1))\n",
-    "rescaledPredictors = scaler.fit_transform(predictors)\n",
-    "                        \n",
-    "\n",
-    "# using StandardScaler\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "scaler1 = StandardScaler().fit(predictors)\n",
-    "standardizedPredictors = scaler1.transform(predictors)\n",
-    "                        \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Feature selection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            predictor        score         pvalue\n",
-      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
-      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
-      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
-      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
-      "0         FULL_Charge  1214.902838  3.404241e-224\n",
-      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
-      "8       AS_DAYM780201   717.317408  4.911002e-142\n"
-     ]
-    }
-   ],
-   "source": [
-    "# using univariate statistics F-test as an alternative to chi-squared test (some values are zero and chi2 returns an error)\n",
-    "\n",
-    "# untransformed data\n",
-    "from sklearn.feature_selection import SelectKBest, f_classif\n",
-    "bestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
-    "fit = bestFeatures.fit(predictors, target)\n",
-    "\n",
-    "scores = pd.DataFrame(fit.scores_) \n",
-    "pvalues = pd.DataFrame(fit.pvalues_)\n",
-    "columns = pd.DataFrame(data.columns[0:11])\n",
-    "\n",
-    "featureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\n",
-    "featureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n",
-    "\n",
-    "print(featureValues.nlargest(7, 'score'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         re_predictor     re_score      re_pvalue\n",
-      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
-      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
-      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
-      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
-      "0         FULL_Charge  1214.902838  3.404241e-224\n",
-      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
-      "8       AS_DAYM780201   717.317408  4.911002e-142\n",
-      "         st_predictor     st_score      st_pvalue\n",
-      "7  AS_MeanAmphiMoment  2813.868178   0.000000e+00\n",
-      "1  FULL_AcidicMolPerc  1697.249565  4.220004e-295\n",
-      "2     FULL_AURR980107  1572.280677  1.887905e-277\n",
-      "3     FULL_DAYM780201  1350.300743  6.997934e-245\n",
-      "0         FULL_Charge  1214.902838  3.404241e-224\n",
-      "5     FULL_OOBM850104   785.124798  7.441087e-154\n",
-      "8       AS_DAYM780201   717.317408  4.911002e-142\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(None, None)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# checking the transformed data\n",
-    "\n",
-    "# rescaledPredictors\n",
-    "reBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
-    "reFit = reBestFeatures.fit(rescaledPredictors, target)\n",
-    "\n",
-    "reScores = pd.DataFrame(reFit.scores_) \n",
-    "rePvalues = pd.DataFrame(reFit.pvalues_)\n",
-    "reColumns = pd.DataFrame(data.columns[0:11])\n",
-    "\n",
-    "reFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\n",
-    "reFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n",
-    "\n",
-    "\n",
-    "\n",
-    "# standardizedPredictors\n",
-    "stBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
-    "stFit = stBestFeatures.fit(standardizedPredictors, target)\n",
-    "\n",
-    "stScores = pd.DataFrame(stFit.scores_) \n",
-    "stPvalues = pd.DataFrame(stFit.pvalues_)\n",
-    "stColumns = pd.DataFrame(data.columns[0:11])\n",
-    "\n",
-    "stFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\n",
-    "stFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n",
-    "\n",
-    "print(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.08173854 0.13773548 0.0922014  0.09119859 0.04664165 0.08280402\n",
-      " 0.03303368 0.297985   0.05331243 0.03219657 0.05115262]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# using feature importance\n",
-    "from sklearn.ensemble import ExtraTreesClassifier\n",
-    "model = ExtraTreesClassifier()\n",
-    "model.fit(predictors, target)\n",
-    "print(model.feature_importances_)\n",
-    "\n",
-    "# visualising feature importance\n",
-    "importances = pd.Series(model.feature_importances_, index=data.columns[0:11])\n",
-    "importances.nlargest(10).plot(kind='barh')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Building the classification model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
-       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
-       "                   warm_start=False)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\n",
-    "from sklearn.model_selection import train_test_split # random split\n",
-    "from sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\n",
-    "p_train, p_test, t_train, t_test = train_test_split(predictors, target, \n",
-    "                                                    test_size=0.30,random_state=42)\n",
-    "\n",
-    "logit = LogisticRegression() # making instance of model\n",
-    "\n",
-    "# fitting the model on untransformed data\n",
-    "logit.fit(p_train, t_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Measuring model performance\n",
-    "\n",
-    "We can measure the performance of a classification problem using precison, F1 Score, ROC curve\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# predict on test data\n",
-    "predictions = logit.predict(p_test) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 15.0, 'Predicted label')"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# the confusion matrix\n",
-    "from sklearn import metrics\n",
-    "cm = metrics.confusion_matrix(t_test, predictions)\n",
-    "cm\n",
-    "sb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n",
-    "          square=True, cmap='Blues') \n",
-    "plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy:  91.77631578947368\n",
-      "Precision:  92.82608695652173\n",
-      "Recall:  91.04477611940298\n",
-      "MCC:  0.8356480321235562\n"
-     ]
-    }
-   ],
-   "source": [
-    "# performance metrics\n",
-    "print(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\n",
-    "print(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\n",
-    "print(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "print('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n",
-    "                                                      # higher values are better\n",
-    "# not affected by unbalanced classes\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ROC curve of true positive rate against false positive rate\n",
-    "# shows tradeoff between sensitivy and specificity\n",
-    "\n",
-    "pred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\n",
-    "fpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\n",
-    "auc = metrics.roc_auc_score(t_test, pred_probs)\n",
-    "plt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\n",
-    "plt.legend(loc=4)\n",
-    "plt.ylabel('tpr'), plt.xlabel('fpr')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['False' 'True']\n",
-      "386\n",
-      "372\n"
-     ]
-    }
-   ],
-   "source": [
-    "# performance on new data\n",
-    "new_pred = logit.predict(new.values)\n",
-    "\n",
-    "pred_df = pd.DataFrame(new_pred) \n",
-    "pred_df.columns=[\"CLASS\"]\n",
-    "pred_df.index.name=\"Index\" \n",
-    "pred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n",
-    "\n",
-    "#csv file output\n",
-    "pred_df.to_csv(\"ilujumba.csv\") \n",
-    "print(pred_df['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(pred_df.groupby('CLASS').size()[0].sum())\n",
-    "print(pred_df.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Logistic regression on rescaled data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy:  91.66666666666666\n",
-      "Precision:  92.81045751633987\n",
-      "Recall:  90.8315565031983\n",
-      "MCC:  0.8335025900424667\n"
-     ]
-    }
-   ],
-   "source": [
-    "p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n",
-    "                                                        test_size=0.30,random_state=42)\n",
-    "\n",
-    "logit1 = LogisticRegression() # making instance of model\n",
-    "\n",
-    "# fitting the model on rescaled data\n",
-    "logit1.fit(p1_train, t1_train)\n",
-    "\n",
-    "# predict on test data\n",
-    "predictions1 = logit1.predict(p1_test)\n",
-    "\n",
-    "# performance metrics\n",
-    "print(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\n",
-    "print(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\n",
-    "print(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "print('MCC: ',matthews_corrcoef(t1_test, predictions1))\n",
-    "\n",
-    "# rescaling new data\n",
-    "newArray = new.to_numpy()\n",
-    "rescaledNew = scaler.fit_transform(newArray)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['False' 'True']\n",
-      "378\n",
-      "380\n"
-     ]
-    }
-   ],
-   "source": [
-    "# performance on new data (rescaled)\n",
-    "new_pred1 = logit1.predict(rescaledNew)\n",
-    "\n",
-    "pred_df1 = pd.DataFrame(new_pred1) \n",
-    "pred_df1.columns=[\"CLASS\"]\n",
-    "pred_df1.index.name=\"Index\" \n",
-    "pred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n",
-    "\n",
-    "#csv file output\n",
-    "pred_df1.to_csv(\"ilujumba1.csv\") \n",
-    "print(pred_df1['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(pred_df1.groupby('CLASS').size()[0].sum())\n",
-    "print(pred_df1.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Logistic regression on standardized data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy:  91.8859649122807\n",
-      "Precision:  93.21663019693655\n",
-      "Recall:  90.8315565031983\n",
-      "MCC:  0.8379994044962448\n",
-      "['False' 'True']\n",
-      "388\n",
-      "370\n"
-     ]
-    }
-   ],
-   "source": [
-    "p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n",
-    "                                                        test_size=0.30,random_state=42)\n",
-    "\n",
-    "logit2 = LogisticRegression() # making instance of model\n",
-    "\n",
-    "# fitting the model on rescaled data\n",
-    "logit2.fit(p2_train, t2_train)\n",
-    "\n",
-    "# predict on test data\n",
-    "predictions2 = logit2.predict(p2_test)\n",
-    "\n",
-    "# performance metrics\n",
-    "print(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\n",
-    "print(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\n",
-    "print(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\n",
-    "print('MCC: ',matthews_corrcoef(t2_test, predictions2))\n",
-    "\n",
-    "# standardizing new data\n",
-    "standardizedNew = scaler1.transform(newArray)\n",
-    "\n",
-    "# performance on new data (standaridized)\n",
-    "new_pred2 = logit2.predict(standardizedNew)\n",
-    "\n",
-    "pred_df2 = pd.DataFrame(new_pred2) \n",
-    "pred_df2.columns=[\"CLASS\"]\n",
-    "pred_df2.index.name=\"Index\" \n",
-    "pred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n",
-    "\n",
-    "#csv file output\n",
-    "pred_df2.to_csv(\"ilujumba2.csv\") \n",
-    "print(pred_df2['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(pred_df2.groupby('CLASS').size()[0].sum())\n",
-    "print(pred_df2.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Using selected features, rescaled data and Logistic regression\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy:  90.5701754385965\n",
-      "Precision:  91.36069114470843\n",
-      "Recall:  90.19189765458422\n",
-      "MCC:  0.8113912389992642\n",
-      "['False' 'True']\n",
-      "390\n",
-      "368\n"
-     ]
-    }
-   ],
-   "source": [
-    "p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n",
-    "                                                        test_size=0.30,random_state=42)\n",
-    "\n",
-    "logit3 = LogisticRegression() # making instance of model\n",
-    "\n",
-    "# fitting the model on rescaled data\n",
-    "logit3.fit(p3_train, t3_train)\n",
-    "\n",
-    "# predict on test data\n",
-    "predictions3 = logit3.predict(p3_test)\n",
-    "\n",
-    "# performance metrics\n",
-    "print(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\n",
-    "print(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\n",
-    "print(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "print('MCC: ',matthews_corrcoef(t3_test, predictions3))\n",
-    "\n",
-    "# rescaling new data\n",
-    "# newArray = new.to_numpy()\n",
-    "# rescaledNew = scaler.fit_transform(newArray)\n",
-    "\n",
-    "# performance on new data (rescaled)\n",
-    "new_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n",
-    "\n",
-    "pred_df3 = pd.DataFrame(new_pred3) \n",
-    "pred_df3.columns=[\"CLASS\"]\n",
-    "pred_df3.index.name=\"Index\" \n",
-    "pred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n",
-    "\n",
-    "#csv file output\n",
-    "pred_df3.to_csv(\"ilujumba3.csv\") \n",
-    "print(pred_df3['CLASS'].unique())\n",
-    "\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(pred_df3.groupby('CLASS').size()[0].sum())\n",
-    "print(pred_df3.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Using cross-validation and Logistic Regression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8387328752276078\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import KFold\n",
-    "from sklearn.model_selection import cross_val_score\n",
-    "\n",
-    "kfold = KFold(n_splits=10, random_state=42)\n",
-    "model6 = LogisticRegression()\n",
-    "model6.fit(predictors, target)\n",
-    "\n",
-    "results = cross_val_score(model6, predictors, target)\n",
-    "print(results.mean())\n",
-    "\n",
-    "model6_pred = model6.predict(rescaledNew)\n",
-    "df6 = pd.DataFrame(model6_pred)\n",
-    "df6.columns = ['CLASS']\n",
-    "df6.index.name = 'Index'\n",
-    "df6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n",
-    "\n",
-    "df6.to_csv('ilujumba7.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Naive Bayes classifier with kfold cross-validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.887773129281193\n",
-      "['True' 'False']\n",
-      "370\n",
-      "388\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.naive_bayes import GaussianNB\n",
-    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
-    "model7 = GaussianNB()\n",
-    "model7.fit(predictors, target)\n",
-    "\n",
-    "results = cross_val_score(model7, predictors, target)\n",
-    "print(results.mean())\n",
-    "\n",
-    "model7_pred = model7.predict(newArray)\n",
-    "df7 = pd.DataFrame(model7_pred)\n",
-    "df7.columns = ['CLASS']\n",
-    "df7.index.name = 'Index'\n",
-    "df7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n",
-    "\n",
-    "df7.to_csv('ilujumba7.csv')\n",
-    "print(df7['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(df7.groupby('CLASS').size()[0].sum())\n",
-    "print(df7.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Naive Bayes classifier on rescaled features\n",
-    "\n",
-    "Assumes that all features are independent of each other and each feature contributes equally to the resulting class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.887773129281193\n",
-      "['True' 'False']\n",
-      "347\n",
-      "411\n"
-     ]
-    }
-   ],
-   "source": [
-    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
-    "model8 = GaussianNB()\n",
-    "model8.fit(rescaledPredictors, target)\n",
-    "\n",
-    "results1 = cross_val_score(model8, rescaledPredictors, target)\n",
-    "print(results1.mean())\n",
-    "\n",
-    "model8_pred = model8.predict(rescaledNew)\n",
-    "df8 = pd.DataFrame(model8_pred)\n",
-    "df8.columns = ['CLASS']\n",
-    "df8.index.name = 'Index'\n",
-    "df8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n",
-    "\n",
-    "df8.to_csv('ilujumba8.csv')\n",
-    "print(df8['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(df8.groupby('CLASS').size()[0].sum())\n",
-    "print(df8.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Naive Bayes and kfold validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean for results 0.9101496004863645\n",
-      "cross-predicted accuracy  0.6405529953917051\n",
-      "['True' 'False']\n",
-      "370\n",
-      "388\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import cross_val_predict\n",
-    "from sklearn.naive_bayes import GaussianNB\n",
-    "\n",
-    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
-    "model9 = GaussianNB()\n",
-    "model9.fit(predictors, target)\n",
-    "\n",
-    "results = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\n",
-    "print('mean for results', results.mean())\n",
-    "\n",
-    "predic = cross_val_predict(model9, predictors, target, cv =10)\n",
-    "accuracy = metrics.r2_score(target, predic)\n",
-    "print('cross-predicted accuracy ', accuracy)\n",
-    "\n",
-    "model9_pred = model9.predict(newArray)\n",
-    "df9 = pd.DataFrame(model9_pred)\n",
-    "df9.columns = ['CLASS']\n",
-    "df9.index.name = 'Index'\n",
-    "df9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n",
-    "\n",
-    "df9.to_csv('ilujumba9.csv')\n",
-    "print(df9['CLASS'].unique())\n",
-    "\n",
-    "#printing the numbers of False and True\n",
-    "print(df9.groupby('CLASS').size()[0].sum())\n",
-    "print(df9.groupby('CLASS').size()[1].sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Comparing several algorithms to look at the nature of the decision boundaries created"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#importing classifiers from the sklearn library\n",
-    "\n",
-    "from matplotlib.colors import ListedColormap\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.neural_network import MLPClassifier #1\n",
-    "from sklearn.neighbors import KNeighborsClassifier #2\n",
-    "from sklearn.svm import SVC #3\n",
-    "from sklearn.gaussian_process import GaussianProcessClassifier #4\n",
-    "from sklearn.gaussian_process.kernels import RBF #5\n",
-    "from sklearn.tree import DecisionTreeClassifier #6\n",
-    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\n",
-    "from sklearn.naive_bayes import GaussianNB #9\n",
-    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\n",
-    "from sklearn.linear_model import LogisticRegression #11\n",
-    "\n",
-    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
-    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
-    "         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n",
-    "\n",
-    "classifiers = [\n",
-    "    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n",
-    "    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n",
-    "    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n",
-    "    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n",
-    "    DecisionTreeClassifier(),\n",
-    "    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n",
-    "    MLPClassifier(max_iter=1000), #iterations until converge\n",
-    "    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n",
-    "    GaussianNB(),\n",
-    "    QuadraticDiscriminantAnalysis(),\n",
-    "    LogisticRegression()]\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Dimensionality reduction\n",
-    "https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Ia5IkSYPQzDn1eyPiBBqH4tcdds/MY2urSpIkDVgz08R+H3gn8BEaT2ibQOMQvCRJGkaaCfVdMvMrwEuZeQmNiWj2qLcsSZI0UM2E+trnpv8pInYHxgHttVUkSZIGpZlz6vMiYjzwZWA+MBb4Sq1VSZKkAesz1CNiE+CFzHwOuAPYaUiqkiRJA9bn4ffMfAM4cYhqkSRJG6CZc+o3R8Q/RsTEiHj72lftlUmSpAFp5pz62vvRT+iyLfFQvCRJw0q/oZ6ZzTyRTZIktVgzh98lSdIIYKhLklQIQ12SpEL0G+rR8MmImFutbx8R0+ovTZIkDUQzI/V/BvYBjq7WXwS+XVtFkiRpUJq5pe19mblXRNwLkJnPRcSf1VyXJEkaoKYe6BIRo2jcm05EtAFv1FqVJEkasGZC/Tzg34B3RMSZwM+B/1VrVZIkacCamXzmsohYDHwICOCQzFxWe2WSJGlA+g31iJgOPJiZ367Wt4yI92Xm3bVXJ0mSmtbM4fcLgFVd1l+qtkmSpGGkmVCPzMy1K9XjWJu5al6SJA2hZkL9sYg4KSJGV6+TgcfqLkySJA1MM6H+98D7gf8AOoH3AXPqLEqSJA1cM1e/Pw0cNQS1SJKkDdDM1e9twGeB9q7tM/PY+sqSJEkD1cwFbz8G7gQWAK/XW44kSRqsZkJ9i8z8nwPtOCImApcC76Qxrey8zPxWtzYBfAv4GPAyMDszlwz0syRJUnMXyl0fER8bRN9rgH/IzPcA04ETIuK93dp8FNi1es3B+98lSRq0ZkL9ZBrB/kpEvBARL0bEC/3tlJlPrR11Z+aLwDJgu27NDgYuzYaFwFYRse0AfwZJkkRzV79vuaEfEhHtwFSg+9Sy2wFPdlnvrLY91W3/OVS30W2//fYbWo4kSUVqama4iBhP4xD5mLXbMvOOJvcdC1wDfD4zu4/wo4ddcr0NmfOAeQAdHR3rvS9Jkpq7pe04GofgJwD30Tg/fhewfxP7jqYR6Jdl5rU9NOkEJnZZnwD8vv+yJUlSd82eU/8r4InM/CCNw+gr+tupurL9QmBZZn6zl2bzgb+LhunA85n5VC9tJUlSH5o5/L46M1dHBBGxWWY+HBHvbmK/fYFPAQ9ExH3Vti8B2wNk5neAG2jczvYojVvajhnwTyBJkoDmQr0zIrYCfgTcHBHP0cQh8sz8OT2fM+/aJoETmilUkiT1rZmr3z9eLZ4eEbcC44Cbaq1KkiQNWK+hHhF/npkvRMTbu2x+oPpzLPBsrZVJkqQB6Wuk/kNgJrCYxm1m0e3PnWqvTpIkNa3XUM/MmdUV7B/IzH8fwpokSdIg9HlLW3Uh278NUS2SJGkDNHOf+sKI+KvaK5EkSRukmVvaPgj894h4AniJ6px6Zu5Za2WSJGlAmgn1j9ZehSRJ2mDN3Kf+BEBEvIMuD3SRJEnDS7/n1CNiVkT8FngcuB1YDtxYc12SJGmAmjn8/jUaT2ZbkJlTI+KDwNH1lqVzbv5NSz73lAPe1ZLPlSRtuGaufn8tM1cCm0TEJpl5KzCl5rokSdIANTNS/1NEjAXuAC6LiKeBNfWWJUmSBqqZkfrBwCvAKTQe5PI74G/rLEqSJA1cXw90OR/4YWb+ssvmS+ovSZIkDUZfI/XfAv83IpZHxFkR4Xl0SZKGsV5DPTO/lZn7AB+g8ZjVf42IZRExNyK8RFqSpGGm33PqmflEZp6VmVOB/wZ8HFhWe2WSJGlAmpl8ZnRE/G1EXEZj0pnfAIfVXpkkSRqQvi6UO4DGJDMHAb8CrgDmZOZLQ1SbJEkagL7uU/8S8EPgHzPz2SGqR5IkDVKvoZ6ZHxzKQiRJ0oZpZvIZSZI0AhjqkiQVwlCXJKkQhrokSYUw1CVJKoShLklSIQx1SZIKUVuoR8RFEfF0RPy6l/dnRMTzEXFf9ZpbVy2SJG0M+ppRbkNdDJwPXNpHmzszc2aNNUiStNGobaSemXfQeGSrJEkaAq0+p75PRNwfETdGxG4trkWSpBGtzsPv/VkC7JCZqyLiY8CPgF17ahgRc4A5ANtvv/3QVShJ0gjSspF6Zr6Qmauq5RuA0RGxTS9t52VmR2Z2tLW1DWmdkiSNFC0L9Yh4Z0REtTytqmVlq+qRJGmkq+3we0RcDswAtomITuA0YDRAZn4HOBw4PiLWAK8AR2Vm1lWPJEmlqy3UM/Poft4/n8Ytb5Ik6S3Q6qvfJUnSW8RQlySpEIa6JEmFMNQlSSqEoS5JUiEMdUmSCmGoS5JUCENdkqRCtPKBLhqBzrn5Ny353FMOeFdLPleSRhJH6pIkFcJQlySpEIa6JEmFMNQlSSqEoS5JUiEMdUmSCmGoS5JUCENdkqRCGOqSJBXCUJckqRCGuiRJhTDUJUkqhKEuSVIhDHVJkgphqEuSVAhDXZKkQhjqkiQVwlCXJKkQhrokSYUw1CVJKkRtoR4RF0XE0xHx617ej4g4LyIejYilEbFXXbVIkrQxqHOkfjFwYB/vfxTYtXrNAS6osRZJkopXW6hn5h3As300ORi4NBsWAltFxLZ11SNJUulaeU59O+DJLuud1TZJkjQIrQz16GFb9tgwYk5ELIqIRStWrKi5LEmSRqZWhnonMLHL+gTg9z01zMx5mdmRmR1tbW1DUpwkSSNNK0N9PvB31VXw04HnM/OpFtYjSdKItmldHUfE5cAMYJuI6AROA0YDZOZ3gBuAjwGPAi8Dx9RViyRJG4PaQj0zj+7n/QROqOvzJUna2DijnCRJhTDUJUkqhKEuSVIhDHVJkgpR24Vy0lA55+bftORzTzngXS35XEnqjSN1SZIKYahLklQIQ12SpEIY6pIkFcJQlySpEIa6JEmFMNQlSSqE96lLNfH+eUlDzZG6JEmFMNQlSSqEoS5JUiEMdUmSCmGoS5JUCENdkqRCGOqSJBXC+9SljYj3zktlc6QuSVIhDHVJkgphqEuSVAhDXZKkQhjqkiQVwlCXJKkQhrokSYUw1CVJKoShLklSIWoN9Yg4MCIeiYhHI+KLPbw/OyJWRMR91eu4OuuRJKlktU0TGxGjgG8DBwCdwD0RMT8zH+rW9MrMPLGuOiRJ2ljUOff7NODRzHwMICKuAA4Guoe6pI2cc9JLb406D79vBzzZZb2z2tbdYRGxNCKujoiJPXUUEXMiYlFELFqxYkUdtUqSNOLVGerRw7bstn4d0J6ZewILgEt66igz52VmR2Z2tLW1vcVlSpJUhjpDvRPoOvKeAPy+a4PMXJmZr1ar3wX2rrEeSZKKVmeo3wPsGhE7RsSfAUcB87s2iIhtu6zOApbVWI8kSUWr7UK5zFwTEScCPwFGARdl5oMRcQawKDPnAydFxCxgDfAsMLuueiRJKl2dV7+TmTcAN3TbNrfL8qnAqXXWIEnSxqLWUJekkcxb7TTSOE2sJEmFMNQlSSqEoS5JUiEMdUmSCmGoS5JUCENdkqRCGOqSJBXCUJckqRCGuiRJhXBGOUkaQZzlTn1xpC5JUiEMdUmSCmGoS5JUCENdkqRCGOqSJBXCUJckqRCGuiRJhTDUJUkqhKEuSVIhDHVJkgphqEuSVAhDXZKkQvhAF0nSBvNBM8ODI3VJkgphqEuSVAgPv0uSirQxnhJwpC5JUiEMdUmSClFrqEfEgRHxSEQ8GhFf7OH9zSLiyur9uyOivc56JEkqWW2hHhGjgG8DHwXeCxwdEe/t1uwzwHOZuQtwDnBWXfVIklS6Okfq04BHM/OxzPxP4Arg4G5tDgYuqZavBj4UEVFjTZIkFavOUN8OeLLLeme1rcc2mbkGeB7YusaaJEkqVmRmPR1HHAF8JDOPq9Y/BUzLzM91afNg1aazWv9d1WZlt77mAHOq1XcDj9RS9OBsAzzT6iJGCL+r5vldNc/vqnl+V80bbt/VDpnZ1l+jOu9T7wQmdlmfAPy+lzadEbEpMA54tntHmTkPmFdTnRskIhZlZker6xgJ/K6a53fVPL+r5vldNW+kfld1Hn6/B9g1InaMiD8DjgLmd2szH/h0tXw4cEvWdehAkqTC1TZSz8w1EXEi8BNgFHBRZj4YEWcAizJzPnAh8P2IeJTGCP2ouuqRJKl0tU4Tm5k3ADd02za3y/Jq4Ig6axgCw/K0wDDld9U8v6vm+V01z++qeSPyu6rtQjlJkjS0nCZWkqRCGOqD1N8UuGqIiIkRcWtELIuIByPi5FbXNNxFxKiIuDcirm91LcNdRGwVEVdHxMPV37F9Wl3TcBURp1T/D/46Ii6PiDGtrmm4iIiLIuLpiPh1l21vj4ibI+K31Z/jW1ljswz1QWhyClw1rAH+ITPfA0wHTvC76tfJwLJWFzFCfAu4KTMnAZPxe+tRRGwHnAR0ZObuNC5e9sLk/3IxcGC3bV8EfpaZuwI/q9aHPUN9cJqZAldAZj6VmUuq5Rdp/KPbfWZBVSJiAnAQ8L1W1zLcRcSfA39N4y4aMvM/M/NPra1qWNsU2LyaE2QL1p83ZKOVmXew/hwpXacxvwQ4ZEiLGiRDfXCamQJX3VRP4ZsK3N3aSoa1c4EvAG+0upARYCdgBfCv1emK70XE21pd1HCUmf8BnA38O/AU8Hxm/rS1VQ17f5GZT0FjcAK8o8X1NMVQH5yeHjrjbQR9iIixwDXA5zPzhVbXMxxFxEzg6cxc3OpaRohNgb2ACzJzKvASI+QQ6VCrzgcfDOwI/CXwtoj4ZGurUh0M9cFpZgpcVSJiNI1Avywzr211PcPYvsCsiFhO45TO/hHxg9aWNKx1Ap2ZufbIz9U0Ql7r+xvg8cxckZmvAdcC729xTcPdHyNiW4Dqz6dbXE9TDPXBaWYKXAHVo3QvBJZl5jdbXc9wlpmnZuaEzGyn8Xfqlsx0NNWLzPwD8GREvLva9CHgoRaWNJz9OzA9Irao/p/8EF5U2J+u05h/GvhxC2tpWq0zypWqtylwW1zWcLUv8CnggYi4r9r2pWq2QWlDfQ64rPrl+jHgmBbXMyxl5t0RcTWwhMYdKfcyQmdMq0NEXA7MALaJiE7gNOAbwFUR8RkavxSNiNlPnVFOkqRCePhdkqRCGOqSJBXCUJckqRCGuiRJhTDUJUkqhKEu1SQiXo+I+6qnYv2/iNiil3Y3RMRWg+j/L6vblAZb3/KI2Gaw+48UETE7Iv6y1XVIQ8FQl+rzSmZOqZ6K9Z/A33d9Mxo2ycyPDeZBJJn5+8w8/K0qtmCzaUyNKhXPUJeGxp3ALhHRXj33+59pTAQyce2Iuct7362ee/3TiNgcICJ2iYgFEXF/RCyJiJ2r9r+u3p8dET+OiJsi4pGIOG3tB0fEjyJicdXnnP4KjYgDq8+4PyJ+Vm17e9XP0ohYGBF7VttPj4hLqlqXR8ShEfF/IuKBqpbRVbvlEXFWRPyqeu1Sbd8hIn5W9fuziNi+2n5xRJwXEb+MiMci4vAu9f1TRNxT7fPValuP3121XweNCWruq7Z9IyIeqvY/+y34bysNG4a6VLPqUZcfBR6oNr0buDQzp2bmE92a7wp8OzN3A/4EHFZtv6zaPpnGnN1P9fBR04BPAFOAIyKio9p+bGbuTSPcToqIrfuotQ34LnBY9VlrZ9H6KnBvZu4JfAm4tMtuO9N4XOzBwA+AWzNzD+CVavtaL2TmNOB8Gk+jo1q+tOr3MuC8Lu23BfYDZtKY3YuI+HD1HU2rfs69I+Kve/vuMvNqYBHwicycAmwOfBzYrfrMr/f2XUgjkaEu1WfzamrcRTSmmbyw2v5EZi7sZZ/HM3PtdLqLgfaI2BLYLjP/DSAzV2fmyz3se3NmrszMV2g8sGO/avtJEXE/sJDGg4h27aPm6cAdmfl49VlrnzG9H/D9atstwNYRMa5678bqISEP0Jg2+aZq+wNAe5e+L+/y5z7V8j7AD6vl73epGeBHmflGZj4E/EW17cPV614aRzomdfl51vvuevj5XgBWA9+LiEOBnr5HacRy7nepPq9Uo8N1Gs/S4KU+9nm1y/LrNEaWPT3qtyfd53zOiJhB4wld+2TmyxFxGzCmjz6ih37Wbu/t814FyMw3IuK1/K+5p9/gzf/GZC/LPfW5rt9unx/A/87Mf3lTcRHt9PzdvbnzxnMbptF4oMlRwInA/r3UIo04jtSlYa56/nxnRBwCEBG4UEkJAAABOUlEQVSb9XIl/QHVue/NgUOAXwDjgOeqQJ9EYyTel7uAD0TEjtVnvb3afgeNQ/tUvyg8U9U1EEd2+fOuavmXNMKVqv+f99PHT4BjI2JsVct2EfGOfvZ5Ediyaj8WGFc9UOjzNA7hS8VwpC6NDJ8C/iUizgBeo3Gu+41ubX5O4xD2LsAPM3NRRDwA/H1ELAUeoXEIvleZuaK6mO7aiNiExjOkDwBOB/616udl/uuRlAOxWUTcTWMwcXS17STgooj4J2AF/TxlLTN/GhHvAe6qjnqsAj5JY2Tem4uB70TEKzSubfhxRIyhMeo/ZRA/hzRs+ZQ2qQARMRvoyMwTW11LTyJiOY36nml1LVLJPPwuSVIhHKlLklQIR+qSJBXCUJckqRCGuiRJhTDUJUkqhKEuSVIhDHVJkgrx/wHicFh/2wnKugAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# preprocessing the dataset\n",
-    "dataArray = data.to_numpy()\n",
-    "X, y = dataArray[:,0:11], dataArray[0:,11]\n",
-    "X = StandardScaler().fit_transform(X)\n",
-    "\n",
-    "# reducing dimensions of the dataset using PCA  https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\n",
-    "from sklearn.decomposition import PCA\n",
-    "pca = PCA()\n",
-    "pca.fit_transform(X)\n",
-    "pca_variance = pca.explained_variance_\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\n",
-    "plt.legend()\n",
-    "plt.ylabel('Variance ratio')\n",
-    "plt.xlabel('Principal components')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3038, 8)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\n",
-    "ninety_five = pca2.fit_transform(X)\n",
-    "ninety_five.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Explained variance:  0.9528002773163116\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Eight features explain 95% of the variance in the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pca2 = PCA(3) # keeping features three principal components\n",
-    "principalComponents = pca2.fit_transform(X)\n",
-    "\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "plt.figure(figsize=(10,6))\n",
-    "ax = plt.axes(projection='3d')\n",
-    "ax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n",
-    "           linewidths=1, alpha=.5,\n",
-    "           edgecolor='k', s= 200,\n",
-    "           c=data['CLASS'])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3038, 3)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "principalComponents.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\n",
-    "finalDf = pd.concat([principalDf, data['CLASS']], axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>PC1</th>\n",
-       "      <th>PC2</th>\n",
-       "      <th>PC3</th>\n",
-       "      <th>CLASS</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-0.423532</td>\n",
-       "      <td>-0.528139</td>\n",
-       "      <td>0.010959</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-1.428729</td>\n",
-       "      <td>-1.258821</td>\n",
-       "      <td>-1.767912</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-1.211100</td>\n",
-       "      <td>2.074380</td>\n",
-       "      <td>-1.508877</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.912438</td>\n",
-       "      <td>2.516293</td>\n",
-       "      <td>-0.041182</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.947851</td>\n",
-       "      <td>2.369553</td>\n",
-       "      <td>-0.713456</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        PC1       PC2       PC3  CLASS\n",
-       "0 -0.423532 -0.528139  0.010959      1\n",
-       "1 -1.428729 -1.258821 -1.767912      1\n",
-       "2 -1.211100  2.074380 -1.508877      1\n",
-       "3 -0.912438  2.516293 -0.041182      1\n",
-       "4 -0.947851  2.369553 -0.713456      1"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "finalDf.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Variance explained by three PCs:  65.30291340147794 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Visualising the top 2 principal components"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize = (6,6))\n",
-    "ax = fig.add_subplot(111) \n",
-    "ax.set(xlim=(-10,10), ylim=(-10,10))\n",
-    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
-    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
-    "ax.set_title('top 2 components', fontsize = 20)\n",
-    "\n",
-    "targets = [0, 1]\n",
-    "colors = ['r', 'g']\n",
-    "\n",
-    "for target, color in zip(targets,colors):\n",
-    "    indices = finalDf['CLASS'] == target\n",
-    "    ax.scatter(finalDf.loc[indices, 'PC1']\n",
-    "               , finalDf.loc[indices, 'PC2']\n",
-    "               , c = color\n",
-    "               , s = 50)\n",
-    "ax.legend(targets)\n",
-    "ax.grid()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# splitting the into training and test part\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# creating mesh for the contour plot\n",
-    "\n",
-    "h = .02  # step size in the mesh\n",
-    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
-    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
-    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca4 = PCA(n_components=2)\n",
-    "\n",
-    "# applying PCA on training set\n",
-    "pca4.fit(X_train)\n",
-    "\n",
-    "#applying transform on training and testing sets\n",
-    "train_ = pca4.transform(X_train)\n",
-    "test_ = pca4.transform(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Explained variance:  0.5330557904151474\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((2126, 2), (912, 2))"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_.shape, test_.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
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-      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
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-   "source": [
-    "figure = plt.figure(figsize=(27, 15))\n",
-    "i = 1\n",
-    "\n",
-    "datasets=[data]\n",
-    "for ds_cnt, ds in enumerate(datasets):\n",
-    "    # just plot the dataset first\n",
-    "    cm = plt.cm.RdBu\n",
-    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
-    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
-    "\n",
-    "    if ds_cnt == 0:\n",
-    "        ax.set_title(\"Input data\")\n",
-    "        # Plot the top 2 principal components for training data\n",
-    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n",
-    "                    edgecolors='k')\n",
-    "        # Plot the top 2 principal components for the testing data\n",
-    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
-    "                    edgecolors='k')\n",
-    "        ax.set_xlim(xx.min(), xx.max())\n",
-    "        ax.set_ylim(yy.min(), yy.max())\n",
-    "        ax.set_xticks(())\n",
-    "        ax.set_yticks(())\n",
-    "        i += 1\n",
-    "\n",
-    "        # iterate over classifiers\n",
-    "\n",
-    "    for name, clf in zip(names, classifiers):\n",
-    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
-    "        clf.fit(train_, y_train)\n",
-    "        score = clf.score(test_, y_test)\n",
-    "\n",
-    "        # Plot the decision boundary. For that, we will assign a color to each\n",
-    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
-    "\n",
-    "        if hasattr(clf, \"decision_function\"):\n",
-    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n",
-    "        else:\n",
-    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n",
-    "\n",
-    "        # Put the result into a color plot\n",
-    "        Z = Z.reshape(xx.shape)\n",
-    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
-    "\n",
-    "        # Plot the training points\n",
-    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n",
-    "        # Plot the testing points\n",
-    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.6)\n",
-    "\n",
-    "        ax.set_xlim(xx.min(), xx.max())\n",
-    "        ax.set_ylim(yy.min(), yy.max())\n",
-    "        ax.set_xticks(())\n",
-    "        ax.set_yticks(())\n",
-    "        if ds_cnt == 0:\n",
-    "            ax.set_title(name)\n",
-    "            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n",
-    "            i += 1\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
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diff --git a/Assignment Colab/ilujumba.csv b/Assignment Colab/ilujumba.csv
deleted file mode 100644
index 76aa5d8..0000000
--- a/Assignment Colab/ilujumba.csv	
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diff --git a/Assignment Colab/ilujumba1.csv b/Assignment Colab/ilujumba1.csv
deleted file mode 100644
index 6545f6a..0000000
--- a/Assignment Colab/ilujumba1.csv	
+++ /dev/null
@@ -1,759 +0,0 @@
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diff --git a/Assignment Colab/ilujumba2.csv b/Assignment Colab/ilujumba2.csv
deleted file mode 100644
index 529b97a..0000000
--- a/Assignment Colab/ilujumba2.csv	
+++ /dev/null
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diff --git a/Assignment Colab/ilujumba3.csv b/Assignment Colab/ilujumba3.csv
deleted file mode 100644
index 085569c..0000000
--- a/Assignment Colab/ilujumba3.csv	
+++ /dev/null
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diff --git a/Assignment Colab/ilujumba7.csv b/Assignment Colab/ilujumba7.csv
deleted file mode 100644
index d5d0b48..0000000
--- a/Assignment Colab/ilujumba7.csv	
+++ /dev/null
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diff --git a/Assignment Colab/ilujumba8.csv b/Assignment Colab/ilujumba8.csv
deleted file mode 100644
index b665d8e..0000000
--- a/Assignment Colab/ilujumba8.csv	
+++ /dev/null
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diff --git a/Assignment Colab/ilujumba9.csv b/Assignment Colab/ilujumba9.csv
deleted file mode 100644
index d5d0b48..0000000
--- a/Assignment Colab/ilujumba9.csv	
+++ /dev/null
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From 78d3fcd6375bd21d01610a440f3fc32ab898fb61 Mon Sep 17 00:00:00 2001
From: Ibra Lujumba <ibra.lujumba@gmail.com>
Date: Mon, 16 Mar 2020 16:05:56 +0300
Subject: [PATCH 10/10] New submission

Omits bokeh commands since they make the kernel exceed assigned RAM. The kernel restarts shortly afterwards.
---
 .../Ibra Lujumba_kaggle-checkpoint.ipynb      | 1439 +++++++++++++++++
 Assignment Colab/Ibra Lujumba_kaggle.ipynb    | 1439 +++++++++++++++++
 2 files changed, 2878 insertions(+)
 create mode 100644 Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb
 create mode 100644 Assignment Colab/Ibra Lujumba_kaggle.ipynb

diff --git a/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb b/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb
new file mode 100644
index 0000000..4c4273f
--- /dev/null
+++ b/Assignment Colab/.ipynb_checkpoints/Ibra Lujumba_kaggle-checkpoint.ipynb	
@@ -0,0 +1,1439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ACE-Uganda Kaggle competition\n",
+    "### by Ibra Lujumba\n",
+    "#### 2019/HD07/27842U"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Contents\n",
+    "\n",
+    "- Exploratory Data Analysis\n",
+    "\n",
+    "-  Data transformation\n",
+    "\n",
+    "- Feature selection\n",
+    "\n",
+    "- Building a machine learning classifier\n",
+    "\n",
+    "- Measuring performance of a classifier\n",
+    "\n",
+    "- Machine learning is an iterative process\n",
+    "\n",
+    "- Visualising decision boundaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Exploratory Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exploratory data analysis (EDA) is the first step towards building a model that is capable of making predictions about the data. This is done to try to understand the properties of the data before any machine learning algorithm is used to make predictions using the data.\n",
+    "\n",
+    "The go-to libraries for manipulating data in python are **numpy**, and **pandas**. **matplotlib** and **seaborn** are used for data visualisation. These lbraries are imported using the **import** keyword."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Importing python modules for data analysis and visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # manipulation of arrays\n",
+    "import pandas as pd # manipulating dataframes\n",
+    "import matplotlib.pyplot as plt # data visualisation\n",
+    "import seaborn as sb # data visualisation,it is based on plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is necessary to ignore verbose warnings from functions in python. The data being manipulated may not necessarily meet the description of the input arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ignoring warnings that may arise\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Importing the datasets\n",
+    "The pandas library has the functionality to read data in delimited text files. The delimiter may be a tab(\\t), comma(,), semi-colon(;) or another metacharacter which is specified using the 'sep=' argument.\n",
+    "For comma-seperated files (csv), pd.read_csv() function is used. The default delimiter is a comma so there is no need to explicitly specify it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls ../input/ace-class-assignment/ # '!' specifies that this is not a python command \n",
+    "                                    # and should be executed outside the notebook\n",
+    "\n",
+    "# reading in the data\n",
+    "data = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\n",
+    "new = pd.read_csv('../input/ace-class-assignment/Test.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Checking the dimensions of the data as well as the datatype of each column\n",
+    "Dimensions of the dataframe are the number of rows and columns represented in the format (rows, columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking dimensions of the datasets\n",
+    "data.shape, new.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is important to check the type of data stored in the dataframe. Machine learning algorithms utilise numeric data to make predictions.\n",
+    "In cases where, there exists a non-numeric data type, it should be converted to numeric values through binarisation or numeric coding for different classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the datatypes of the variables\n",
+    "data.dtypes, new.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the values in all the variables exists as either floats or integers.\n",
+    "\n",
+    "##### Proceeding to work with the training dataset to build the classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Descriptive statistics of the train dataset such as arithmetic mean, standard deviation, quartiles and number of non-NA values in each column. These values inform what the next steps should be. \n",
+    "These steps include:\n",
+    "- cleaning of the dataset to remove rows that don't meet preset criteria\n",
+    "- taking care of skewed distributions\n",
+    "- taking care of missing values problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\n",
+    "AS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\n",
+    "For all the variables, the data points are not widely spread"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the proprotions of classses\n",
+    "data.groupby('CLASS').size()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both classes have an equal number of entries\n",
+    "\n",
+    "Obtaining pairwise correlation values for the variables in the train dataset to check if variables are independent of each other or multicollinearity exists within data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\n",
+    "pearsoncorr = data.corr(method='pearson')\n",
+    "\n",
+    "# visualizing the correlation matrix as a heatmap to make interpretation easier\n",
+    "plt.figure(figsize=(10,10))\n",
+    "top_corr = pearsoncorr.index\n",
+    "sb.heatmap(pearsoncorr, \n",
+    "            xticklabels=pearsoncorr.columns,\n",
+    "            yticklabels=pearsoncorr.columns,\n",
+    "            cmap='RdYlGn',\n",
+    "            annot=True,\n",
+    "            linewidth=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the p-values associated with the correlation values using the code below.\n",
+    "\n",
+    "`from scipy.stats import pearsonr`\n",
+    "\n",
+    "`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n",
+    "\n",
+    "Getting the *p-value* associated with a correlation value is necessary since it acknowledges whether the observed correlation between variables is significant or not. Normally, significance is confirmed if the *p-value* is below a threshold.\n",
+    "\n",
+    "In this examples, all the p-values for the observed correlations were significant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using a scatter plot matrix to visualise correlations\n",
+    "plt.figure(figsize=(40,40))\n",
+    "sb.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\n",
+    "These variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n",
+    "\n",
+    "Variables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This confirms that NT_EFC195 is a categorical variable with two classes 0 and 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[['CLASS','NT_EFC195']].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NT_EFC195 assumes both values irrespective of class\n",
+    "\n",
+    "Getting the associated p-values to check the correlation of features with the class. \n",
+    "The value of 1 at the bottom should be ignored since these values are obtained from the last column of a matrix of pairwise correlations therefore it corresponds to self correlation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "data.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the distribution and skewness of variables\n",
+    "plt.figure(figsize=(10,6))\n",
+    "data.skew().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of the variables are minimally skewed except NT_EFC195. Further checks will be done to try to understand the properties of this variable.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.groupby('NT_EFC195').size() # majority of the instances are of Class 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The skewedness in this variable can be understood by having most of its values at zeros\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n",
+    "\n",
+    "Tranformation possibilities\n",
+    "* using the minimum and maximum scaler\n",
+    "* standardisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Data transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Better performance of algorithms can be obtained if the data is transformed.\n",
+    "Some algorithms are may take features with large values as the most important features in the predictions which creates bias within predictions since predictions are majorly based on that variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Seperating the predictor variables from the target variable. Transformation should not be applied on the target variable.\n",
+    "\n",
+    "Operations on data are carried out on numpy ndArrays. The pandas dataframe is first converted to an ndArray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataArray = data.to_numpy()\n",
+    "\n",
+    "# seperating the predictor and response variables\n",
+    "target = dataArray[:,11]\n",
+    "predictors = dataArray[:,0:11]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numeric data may be rescaled (all values are put between a specified range) or standardized to have a mean of zero for normally distributed data.\n",
+    "Transformations have the advantage of improving performance of machine learning classifiers.\n",
+    "\n",
+    "Rescaling is done using the MinMaxScaler while standardising is done using the StandardScaler() functions. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using minMaxScaler to set all values between 0 and 1\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "rescaledPredictors = scaler.fit_transform(predictors)\n",
+    "                        \n",
+    "\n",
+    "# using StandardScaler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler1 = StandardScaler().fit(predictors)\n",
+    "standardizedPredictors = scaler1.transform(predictors)\n",
+    "                        \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Feature selection\n",
+    "This is done to be able to know which features contribute significantly to prediction of class. Once these features are known, they can be used to predict class. This has the advantage of reducing training time, preventing overfitting as well as improving accuracy of the model since redundant features are not used in the prediction process.\n",
+    "\n",
+    "On this dataset,univariate statistics such as F-test was used as an alternative to chi-squared test (some values after transformation are zero and chi2 returns an error)\n",
+    "\n",
+    "The F-test and feature selection were done on both the original and transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# untransformed data\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif\n",
+    "bestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "fit = bestFeatures.fit(predictors, target)\n",
+    "\n",
+    "scores = pd.DataFrame(fit.scores_) \n",
+    "pvalues = pd.DataFrame(fit.pvalues_)\n",
+    "columns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "featureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\n",
+    "featureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n",
+    "\n",
+    "print(featureValues.nlargest(7, 'score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the transformed data\n",
+    "\n",
+    "# rescaledPredictors\n",
+    "reBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "reFit = reBestFeatures.fit(rescaledPredictors, target)\n",
+    "\n",
+    "reScores = pd.DataFrame(reFit.scores_) \n",
+    "rePvalues = pd.DataFrame(reFit.pvalues_)\n",
+    "reColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "reFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\n",
+    "reFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "# standardizedPredictors\n",
+    "stBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "stFit = stBestFeatures.fit(standardizedPredictors, target)\n",
+    "\n",
+    "stScores = pd.DataFrame(stFit.scores_) \n",
+    "stPvalues = pd.DataFrame(stFit.pvalues_)\n",
+    "stColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "stFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\n",
+    "stFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n",
+    "\n",
+    "print(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The importance of different features can be ranked using the ExtraTressClassifier which is also known as the Extremely Randomized Trees. In this Extra Trees classifier, the features and splits are selected at random; hence, “Extremely Randomized Tree”. Since splits are chosen at random for each feature in the Extra Trees Classifier, it’s less computationally expensive than a Random Forest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using feature importance\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "model = ExtraTreesClassifier()\n",
+    "model.fit(predictors, target)\n",
+    "print(model.feature_importances_)\n",
+    "\n",
+    "# visualising feature importance\n",
+    "importances = pd.Series(model.feature_importances_, index=data.columns[0:11])\n",
+    "importances.nlargest(10).plot(kind='barh')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Building the classification model\n",
+    "Finding te right model for a given dataset is an iterative methods. It is advisable to pursue all combinations of data and algorithms to find the best algorithm that is able to offer satisfactory results on a given dataset.\n",
+    "It cannot be expected that an algorithm that performs well on a particular dataset will perform the same eay in another dataset.\n",
+    "Therefore, an exhaustive approach is necessary to find the best performing algorithm for a given dataset.\n",
+    "\n",
+    "Some of these combinations are (for original and transformed data):\n",
+    "- model + train/test sets\n",
+    "- model + kfold crossvalidation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\n",
+    "from sklearn.model_selection import train_test_split # random split\n",
+    "from sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\n",
+    "p_train, p_test, t_train, t_test = train_test_split(predictors, target, \n",
+    "                                                    test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on untransformed data\n",
+    "logit.fit(p_train, t_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Measuring model performance\n",
+    "Logistic regression is a supervised learning algorithm. Performance of the algorithm is measured as the number of correct predictions made by the algorithm out the the number of true class values.\n",
+    "We can measure the performance of a classification problem using precison, F1 Score, Receiver Operating Curve (ROC) curve, accuracy and Matthews Correlation Coefficient.\n",
+    "\n",
+    "Other performance metric are;\n",
+    "- True Positives (TP) / True Positive Rate (TPR): Number of correct positive predictions / Probability of predicting positive given that the actual class is positive\n",
+    "- False Negatives (FN) / False Negative Rate (FNR): Number of wrong negative predictions / Probability of predicting negative given that the actual class is positive\n",
+    "- True Negatives (TN) / True Negative Rate (TNR): Number of correct negative predictions / Probability of predicting negative given that the actual class is negative\n",
+    "- False Positives (FP) / False Positive Rate (FPR): Number of wrong positive predictions / Probability of predicting positive given that the actual class is negative\n",
+    "- Precision (P): Proportion of predicted positives that are correct\n",
+    "- Recall (R): Proportion of actual positives captured"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict on test data\n",
+    "predictions = logit.predict(p_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the confusion matrix\n",
+    "from sklearn import metrics\n",
+    "cm = metrics.confusion_matrix(t_test, predictions)\n",
+    "cm\n",
+    "sb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n",
+    "          square=True, cmap='Blues') \n",
+    "plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n",
+    "                                                      # higher values are better\n",
+    "# not affected by unbalanced classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Performance metrics are inter-related and can be visualised using an ROC curve where the true positive rate is plotted against the false positive rate. Essentially, the curve shows the tradeoff between sensitivity and specificity.\n",
+    "The maximum area under the curve (AUC) is one which represents 100% accuracy therefore higher values of AUC are desired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\n",
+    "fpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\n",
+    "auc = metrics.roc_auc_score(t_test, pred_probs)\n",
+    "plt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('tpr'), plt.xlabel('fpr')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The confusion matrix above shows the number of true positives, true negatives, false positives as well as false negatives.\n",
+    "The model made a total of 75 wrong class predictions. It can be seen that logistic regression has a good performance of the original dataset where all features are utilised in the prediction of the class.\n",
+    "\n",
+    "Something to keep in mind is that a good performance of the training set by an algorithm is not an indicator of how the model will performance when new data is presented to it. However, a good performance is desirable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance on new data\n",
+    "new_pred = logit.predict(new.values)\n",
+    "\n",
+    "pred_df = pd.DataFrame(new_pred) \n",
+    "pred_df.columns=[\"CLASS\"]\n",
+    "pred_df.index.name=\"Index\" \n",
+    "pred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df.to_csv(\"ilujumba.csv\") \n",
+    "print(pred_df['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the csv file was submitted to the competition, the model had an 83.33% accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Machine learning is an iterative process\n",
+    "\n",
+    "Different combinations of logistic regression with the data were tried.\n",
+    "- train_test split\n",
+    "\n",
+    "    model + rescaled data\n",
+    "    \n",
+    "    model + standardised data\n",
+    "\n",
+    "\n",
+    "- kfold crossvalidation\n",
+    "\n",
+    "    model + rescaled data\n",
+    "    \n",
+    "    model + standardised data\n",
+    "\n",
+    "#### Logistic regression on rescaled data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit1 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit1.fit(p1_train, t1_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions1 = logit1.predict(p1_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t1_test, predictions1))\n",
+    "\n",
+    "# rescaling new data\n",
+    "newArray = new.to_numpy()\n",
+    "rescaledNew = scaler.fit_transform(newArray)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance on new data (rescaled)\n",
+    "new_pred1 = logit1.predict(rescaledNew)\n",
+    "\n",
+    "pred_df1 = pd.DataFrame(new_pred1) \n",
+    "pred_df1.columns=[\"CLASS\"]\n",
+    "pred_df1.index.name=\"Index\" \n",
+    "pred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df1.to_csv(\"ilujumba1.csv\") \n",
+    "print(pred_df1['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df1.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df1.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This combination returned an 88.11% accuracy after submission."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on standardized data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit2 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit2.fit(p2_train, t2_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions2 = logit2.predict(p2_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\n",
+    "print('MCC: ',matthews_corrcoef(t2_test, predictions2))\n",
+    "\n",
+    "# standardizing new data\n",
+    "standardizedNew = scaler1.transform(newArray)\n",
+    "\n",
+    "# performance on new data (standaridized)\n",
+    "new_pred2 = logit2.predict(standardizedNew)\n",
+    "\n",
+    "pred_df2 = pd.DataFrame(new_pred2) \n",
+    "pred_df2.columns=[\"CLASS\"]\n",
+    "pred_df2.index.name=\"Index\" \n",
+    "pred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df2.to_csv(\"ilujumba2.csv\") \n",
+    "print(pred_df2['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df2.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df2.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model streategy returned an 86.03% accuracy after submission which is better than when the original data is used but slightly lower than when rescaled features are used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using selected features, rescaled data and Logistic regression\n",
+    "\n",
+    "An attempt was made to manually select for features that showed the highest importance in the prediction of class. All further strategies on data using Logistic Regression were done using rescaled data with the assumption that it could provide better model performance results when compared to other transformations of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit3 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit3.fit(p3_train, t3_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions3 = logit3.predict(p3_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t3_test, predictions3))\n",
+    "\n",
+    "# rescaling new data\n",
+    "# newArray = new.to_numpy()\n",
+    "# rescaledNew = scaler.fit_transform(newArray)\n",
+    "\n",
+    "# performance on new data (rescaled)\n",
+    "new_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n",
+    "\n",
+    "pred_df3 = pd.DataFrame(new_pred3) \n",
+    "pred_df3.columns=[\"CLASS\"]\n",
+    "pred_df3.index.name=\"Index\" \n",
+    "pred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df3.to_csv(\"ilujumba3.csv\") \n",
+    "print(pred_df3['CLASS'].unique())\n",
+    "\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df3.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df3.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using cross-validation and Logistic Regression\n",
+    "\n",
+    "During cross-validation, the data is split into kfolds. k-1 folds are used in the training are used while the kth fold is used for testing. The process is repeated for k times until each fold has been used as a testing set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42)\n",
+    "model6 = LogisticRegression()\n",
+    "model6.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model6, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model6_pred = model6.predict(rescaledNew)\n",
+    "df6 = pd.DataFrame(model6_pred)\n",
+    "df6.columns = ['CLASS']\n",
+    "df6.index.name = 'Index'\n",
+    "df6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n",
+    "\n",
+    "df6.to_csv('ilujumba7.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another thing to keep in mind is to exhaustive use of simple models on the data. The reason for this is that they have a good reputation in terms of performance on different types of data and their method of prediction is well understood. \n",
+    "\n",
+    "\n",
+    "Another simple algorithm for classification is the Naive Bayes classifier. This algorithm assumes that all features are independent of each other and each feature contributes equally to the resulting class. For this reason, it is called 'naive'.\n",
+    "\n",
+    "#### Naive Bayes classifier with kfold cross-validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model7 = GaussianNB()\n",
+    "model7.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model7, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model7_pred = model7.predict(newArray)\n",
+    "df7 = pd.DataFrame(model7_pred)\n",
+    "df7.columns = ['CLASS']\n",
+    "df7.index.name = 'Index'\n",
+    "df7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df7.to_csv('ilujumba7.csv')\n",
+    "print(df7['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df7.groupby('CLASS').size()[0].sum())\n",
+    "print(df7.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier on rescaled features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model8 = GaussianNB()\n",
+    "model8.fit(rescaledPredictors, target)\n",
+    "\n",
+    "results1 = cross_val_score(model8, rescaledPredictors, target)\n",
+    "print(results1.mean())\n",
+    "\n",
+    "model8_pred = model8.predict(rescaledNew)\n",
+    "df8 = pd.DataFrame(model8_pred)\n",
+    "df8.columns = ['CLASS']\n",
+    "df8.index.name = 'Index'\n",
+    "df8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df8.to_csv('ilujumba8.csv')\n",
+    "print(df8['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df8.groupby('CLASS').size()[0].sum())\n",
+    "print(df8.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes and kfold validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model9 = GaussianNB()\n",
+    "model9.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\n",
+    "print('mean for results', results.mean())\n",
+    "\n",
+    "predic = cross_val_predict(model9, predictors, target, cv =10)\n",
+    "accuracy = metrics.r2_score(target, predic)\n",
+    "print('cross-predicted accuracy ', accuracy)\n",
+    "\n",
+    "model9_pred = model9.predict(newArray)\n",
+    "df9 = pd.DataFrame(model9_pred)\n",
+    "df9.columns = ['CLASS']\n",
+    "df9.index.name = 'Index'\n",
+    "df9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df9.to_csv('ilujumba9.csv')\n",
+    "print(df9['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df9.groupby('CLASS').size()[0].sum())\n",
+    "print(df9.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model strategy returned the highest score after submission with a 99.599% accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing several algorithms to look at the nature of the decision boundaries created"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://medium.com/cascade-bio-blog/creating-visualizations-to-better-understand-your-data-and-models-part-2-28d5c46e956"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Algorithms define a set of hyperplanes that divide the datapoints to their respective classes and span the feature space trained on. Visualising enables one to understand the limitations of a given algorithm on a dataset given to it.\n",
+    "Thus decision boundaries enable one to understand to how the training data selected affects performance of the algorithm."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ten sklearn non-linear classifier algorithms were compared. KNearest Neighbors, Support Vector Machines (Linear and RBF kernels), Gaussian Process Classifier, Decision Trees, Random Forest, Neural Networks, AdaBoost, Naive Bayes, \"Quadratic Discriminant Analysis, Logistic Regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing classifiers from the sklearn library\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPClassifier #1\n",
+    "from sklearn.neighbors import KNeighborsClassifier #2\n",
+    "from sklearn.svm import SVC #3\n",
+    "from sklearn.gaussian_process import GaussianProcessClassifier #4\n",
+    "from sklearn.gaussian_process.kernels import RBF #5\n",
+    "from sklearn.tree import DecisionTreeClassifier #6\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\n",
+    "from sklearn.naive_bayes import GaussianNB #9\n",
+    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\n",
+    "from sklearn.linear_model import LogisticRegression #11\n",
+    "\n",
+    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
+    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
+    "         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n",
+    "\n",
+    "classifiers = [\n",
+    "    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n",
+    "    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n",
+    "    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n",
+    "    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n",
+    "    DecisionTreeClassifier(),\n",
+    "    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n",
+    "    MLPClassifier(max_iter=1000), #iterations until converge\n",
+    "    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n",
+    "    GaussianNB(), #NaiveBayes\n",
+    "    QuadraticDiscriminantAnalysis(),\n",
+    "    LogisticRegression()]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dimensionality reduction\n",
+    "\n",
+    "https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/\n",
+    "\n",
+    "https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the data is multi-dimensional, it was reduced using Principal Component Analysis (PCA) to reduce it to two components.\n",
+    "Trial runs were done to check how much of the variation in the data is explained by the principal components.\n",
+    "\n",
+    "Another thing to keep in mind is that PCA works best on standardised/normalised data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preprocessing the dataset\n",
+    "dataArray = data.to_numpy()\n",
+    "X, y = dataArray[:,0:11], dataArray[0:,11]\n",
+    "X = StandardScaler().fit_transform(X)\n",
+    "\n",
+    "# reducing dimensions of the dataset using PCA  \n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA()\n",
+    "pca.fit_transform(X)\n",
+    "pca_variance = pca.explained_variance_\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Variance ratio')\n",
+    "plt.xlabel('Principal components')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\n",
+    "ninety_five = pca2.fit_transform(X)\n",
+    "ninety_five.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eight features explain 95% of the variance in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca2 = PCA(3) # keeping features three principal components\n",
+    "principalComponents = pca2.fit_transform(X)\n",
+    "\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "plt.figure(figsize=(10,6))\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n",
+    "           linewidths=1, alpha=.5,\n",
+    "           edgecolor='k', s= 200,\n",
+    "           c=data['CLASS'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The three pincipal components wete visualised using a 3D plot. The figure above shows clustering of the three components. Each component is not exactly independent of the others so the clusters overlap to some extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#converting principal component ndarrays to DataFrame format\n",
+    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\n",
+    "finalDf = pd.concat([principalDf, data['CLASS']], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finalDf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualising the top 2 principal components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize = (6,6))\n",
+    "ax = fig.add_subplot(111) \n",
+    "ax.set(xlim=(-10,10), ylim=(-10,10))\n",
+    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
+    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
+    "ax.set_title('top 2 components', fontsize = 20)\n",
+    "\n",
+    "targets = [0, 1]\n",
+    "colors = ['r', 'g']\n",
+    "\n",
+    "for target, color in zip(targets,colors):\n",
+    "    indices = finalDf['CLASS'] == target\n",
+    "    ax.scatter(finalDf.loc[indices, 'PC1']\n",
+    "               , finalDf.loc[indices, 'PC2']\n",
+    "               , c = color\n",
+    "               , s = 50, alpha = 0.4)\n",
+    "ax.legend(targets)\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting the into training and test part\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A mesh grid is required. This can be thought of as a matrix of coordinates upon which the model will make decisions.\n",
+    "These are then visualised to reveal decision boundaries.\n",
+    "The mesh grip was created based on the data and a step size of 0.02"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating mesh for the contour plot\n",
+    "\n",
+    "h = .02  # step size in the mesh\n",
+    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
+    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two principal components were used to enable visualisation on a scatter plot.\n",
+    "\n",
+    "The parameters for the PCA were generated on the training data and these were applied on both the training and training sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca4 = PCA(n_components=2)\n",
+    "\n",
+    "# applying PCA on training set\n",
+    "pca4.fit(X_train)\n",
+    "\n",
+    "#applying transform on training and testing sets\n",
+    "train_ = pca4.transform(X_train)\n",
+    "test_ = pca4.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_.shape, test_.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After transforming the data and the creating the meshgrid, decision boundaries for the algorithms were created by iterating over the classifiers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "figure = plt.figure(figsize=(27, 15))\n",
+    "i = 1\n",
+    "\n",
+    "datasets=[data]\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
+    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "        # Plot the top 2 principal components for training data\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n",
+    "                    edgecolors='k')\n",
+    "        # Plot the top 2 principal components for the testing data\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
+    "                    edgecolors='k')\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        i += 1\n",
+    "\n",
+    "        # iterate over classifiers\n",
+    "\n",
+    "    for name, clf in zip(names, classifiers):\n",
+    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "        clf.fit(train_, y_train)\n",
+    "        score = clf.score(test_, y_test)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n",
+    "        # Plot the testing points\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.4)\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(name)\n",
+    "            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n",
+    "            i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Accuracies of the different algorithms are indicated on the lower right corner.\n",
+    "\n",
+    "The plots show training points in solid colors and testing points semi-transparent.Decision boundaries for GaussianProcessClassifier, RandomForest and AdaBoost are complicated while the decison boundaries for LogisticRegression, NaiveBayes, NeuralNetwork, and Linear SVM are simpler. GaussianProcess and RBF SVM have contoured decision boundaries which seperate points with similar characteristics.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Assignment Colab/Ibra Lujumba_kaggle.ipynb b/Assignment Colab/Ibra Lujumba_kaggle.ipynb
new file mode 100644
index 0000000..4c4273f
--- /dev/null
+++ b/Assignment Colab/Ibra Lujumba_kaggle.ipynb	
@@ -0,0 +1,1439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ACE-Uganda Kaggle competition\n",
+    "### by Ibra Lujumba\n",
+    "#### 2019/HD07/27842U"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Contents\n",
+    "\n",
+    "- Exploratory Data Analysis\n",
+    "\n",
+    "-  Data transformation\n",
+    "\n",
+    "- Feature selection\n",
+    "\n",
+    "- Building a machine learning classifier\n",
+    "\n",
+    "- Measuring performance of a classifier\n",
+    "\n",
+    "- Machine learning is an iterative process\n",
+    "\n",
+    "- Visualising decision boundaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Exploratory Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exploratory data analysis (EDA) is the first step towards building a model that is capable of making predictions about the data. This is done to try to understand the properties of the data before any machine learning algorithm is used to make predictions using the data.\n",
+    "\n",
+    "The go-to libraries for manipulating data in python are **numpy**, and **pandas**. **matplotlib** and **seaborn** are used for data visualisation. These lbraries are imported using the **import** keyword."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Importing python modules for data analysis and visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # manipulation of arrays\n",
+    "import pandas as pd # manipulating dataframes\n",
+    "import matplotlib.pyplot as plt # data visualisation\n",
+    "import seaborn as sb # data visualisation,it is based on plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is necessary to ignore verbose warnings from functions in python. The data being manipulated may not necessarily meet the description of the input arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ignoring warnings that may arise\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Importing the datasets\n",
+    "The pandas library has the functionality to read data in delimited text files. The delimiter may be a tab(\\t), comma(,), semi-colon(;) or another metacharacter which is specified using the 'sep=' argument.\n",
+    "For comma-seperated files (csv), pd.read_csv() function is used. The default delimiter is a comma so there is no need to explicitly specify it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls ../input/ace-class-assignment/ # '!' specifies that this is not a python command \n",
+    "                                    # and should be executed outside the notebook\n",
+    "\n",
+    "# reading in the data\n",
+    "data = pd.read_csv('../input/ace-class-assignment/AMP_TrainSet.csv')\n",
+    "new = pd.read_csv('../input/ace-class-assignment/Test.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Checking the dimensions of the data as well as the datatype of each column\n",
+    "Dimensions of the dataframe are the number of rows and columns represented in the format (rows, columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking dimensions of the datasets\n",
+    "data.shape, new.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is important to check the type of data stored in the dataframe. Machine learning algorithms utilise numeric data to make predictions.\n",
+    "In cases where, there exists a non-numeric data type, it should be converted to numeric values through binarisation or numeric coding for different classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the datatypes of the variables\n",
+    "data.dtypes, new.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the values in all the variables exists as either floats or integers.\n",
+    "\n",
+    "##### Proceeding to work with the training dataset to build the classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Descriptive statistics of the train dataset such as arithmetic mean, standard deviation, quartiles and number of non-NA values in each column. These values inform what the next steps should be. \n",
+    "These steps include:\n",
+    "- cleaning of the dataset to remove rows that don't meet preset criteria\n",
+    "- taking care of skewed distributions\n",
+    "- taking care of missing values problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the count, all values for all cells in the dataset exist. i.e, there are no missing values for any of the variables\n",
+    "AS_DAYM780201 and FULL_DAYM780201 have the highest mean and highest maximum. FULL_OOBM850104 has a negative mean\n",
+    "For all the variables, the data points are not widely spread"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the proprotions of classses\n",
+    "data.groupby('CLASS').size()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both classes have an equal number of entries\n",
+    "\n",
+    "Obtaining pairwise correlation values for the variables in the train dataset to check if variables are independent of each other or multicollinearity exists within data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use this resource to understand the output https://realpython.com/numpy-scipy-pandas-correlation-python/#pearson-correlation-coefficient\n",
+    "pearsoncorr = data.corr(method='pearson')\n",
+    "\n",
+    "# visualizing the correlation matrix as a heatmap to make interpretation easier\n",
+    "plt.figure(figsize=(10,10))\n",
+    "top_corr = pearsoncorr.index\n",
+    "sb.heatmap(pearsoncorr, \n",
+    "            xticklabels=pearsoncorr.columns,\n",
+    "            yticklabels=pearsoncorr.columns,\n",
+    "            cmap='RdYlGn',\n",
+    "            annot=True,\n",
+    "            linewidth=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the last row, FULL_Charge and AS_MeanAmphiMoment have the highest positive correlation values with CLASS whereas second,third and fourth variables have the most negative correlation values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the p-values associated with the correlation values using the code below.\n",
+    "\n",
+    "`from scipy.stats import pearsonr`\n",
+    "\n",
+    "`data.corr(method=lambda x, y: pearsonr(x, y)[1]) - np.eye(len(train.columns))`\n",
+    "\n",
+    "Getting the *p-value* associated with a correlation value is necessary since it acknowledges whether the observed correlation between variables is significant or not. Normally, significance is confirmed if the *p-value* is below a threshold.\n",
+    "\n",
+    "In this examples, all the p-values for the observed correlations were significant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using a scatter plot matrix to visualise correlations\n",
+    "plt.figure(figsize=(40,40))\n",
+    "sb.pairplot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some variables are significantly correlated with each other which raises the problem of multicollinearity (variables are correlated with each other as well as with the response variable).\n",
+    "These variables are Full_Charge, FULL_AcidicMolPer, FULL_AURR980107,...\n",
+    "\n",
+    "Variables that require further investigation - NT_EFC195, AS_MeanAmphiMoment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data['AS_MeanAmphiMoment'].unique()), data['NT_EFC195'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This confirms that NT_EFC195 is a categorical variable with two classes 0 and 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[['CLASS','NT_EFC195']].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NT_EFC195 assumes both values irrespective of class\n",
+    "\n",
+    "Getting the associated p-values to check the correlation of features with the class. \n",
+    "The value of 1 at the bottom should be ignored since these values are obtained from the last column of a matrix of pairwise correlations therefore it corresponds to self correlation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "data.corr(method=lambda x, y: pearsonr(x, y)[1])['CLASS']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the distribution and skewness of variables\n",
+    "plt.figure(figsize=(10,6))\n",
+    "data.skew().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of the variables are minimally skewed except NT_EFC195. Further checks will be done to try to understand the properties of this variable.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.groupby('NT_EFC195').size() # majority of the instances are of Class 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The skewedness in this variable can be understood by having most of its values at zeros\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.plot(kind='density', subplots=True, layout=(4,3), figsize=(10,10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Values for AS_FUK010112, CT_RACS820104,FULL_GEOR030101 and FULL_AURR980107 lie close to zero compared to the rest of the variables.\n",
+    "\n",
+    "Tranformation possibilities\n",
+    "* using the minimum and maximum scaler\n",
+    "* standardisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Data transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Better performance of algorithms can be obtained if the data is transformed.\n",
+    "Some algorithms are may take features with large values as the most important features in the predictions which creates bias within predictions since predictions are majorly based on that variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Seperating the predictor variables from the target variable. Transformation should not be applied on the target variable.\n",
+    "\n",
+    "Operations on data are carried out on numpy ndArrays. The pandas dataframe is first converted to an ndArray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataArray = data.to_numpy()\n",
+    "\n",
+    "# seperating the predictor and response variables\n",
+    "target = dataArray[:,11]\n",
+    "predictors = dataArray[:,0:11]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numeric data may be rescaled (all values are put between a specified range) or standardized to have a mean of zero for normally distributed data.\n",
+    "Transformations have the advantage of improving performance of machine learning classifiers.\n",
+    "\n",
+    "Rescaling is done using the MinMaxScaler while standardising is done using the StandardScaler() functions. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using minMaxScaler to set all values between 0 and 1\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0,1))\n",
+    "rescaledPredictors = scaler.fit_transform(predictors)\n",
+    "                        \n",
+    "\n",
+    "# using StandardScaler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler1 = StandardScaler().fit(predictors)\n",
+    "standardizedPredictors = scaler1.transform(predictors)\n",
+    "                        \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Feature selection\n",
+    "This is done to be able to know which features contribute significantly to prediction of class. Once these features are known, they can be used to predict class. This has the advantage of reducing training time, preventing overfitting as well as improving accuracy of the model since redundant features are not used in the prediction process.\n",
+    "\n",
+    "On this dataset,univariate statistics such as F-test was used as an alternative to chi-squared test (some values after transformation are zero and chi2 returns an error)\n",
+    "\n",
+    "The F-test and feature selection were done on both the original and transformed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# untransformed data\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif\n",
+    "bestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "fit = bestFeatures.fit(predictors, target)\n",
+    "\n",
+    "scores = pd.DataFrame(fit.scores_) \n",
+    "pvalues = pd.DataFrame(fit.pvalues_)\n",
+    "columns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "featureValues = pd.concat([columns,scores, pvalues,], axis=1) # concatenating dataframes\n",
+    "featureValues.columns = ['predictor', 'score', 'pvalue'] # naming the columns\n",
+    "\n",
+    "print(featureValues.nlargest(7, 'score'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking the transformed data\n",
+    "\n",
+    "# rescaledPredictors\n",
+    "reBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "reFit = reBestFeatures.fit(rescaledPredictors, target)\n",
+    "\n",
+    "reScores = pd.DataFrame(reFit.scores_) \n",
+    "rePvalues = pd.DataFrame(reFit.pvalues_)\n",
+    "reColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "reFeatureValues = pd.concat([reColumns,reScores, rePvalues,], axis=1) # concatenating dataframes\n",
+    "reFeatureValues.columns = ['re_predictor', 're_score', 're_pvalue'] # naming the columns\n",
+    "\n",
+    "\n",
+    "\n",
+    "# standardizedPredictors\n",
+    "stBestFeatures = SelectKBest(score_func=f_classif, k=7)\n",
+    "stFit = stBestFeatures.fit(standardizedPredictors, target)\n",
+    "\n",
+    "stScores = pd.DataFrame(stFit.scores_) \n",
+    "stPvalues = pd.DataFrame(stFit.pvalues_)\n",
+    "stColumns = pd.DataFrame(data.columns[0:11])\n",
+    "\n",
+    "stFeatureValues = pd.concat([stColumns,stScores, stPvalues,], axis=1) # concatenating dataframes\n",
+    "stFeatureValues.columns = ['st_predictor', 'st_score', 'st_pvalue'] # naming the columns\n",
+    "\n",
+    "print(reFeatureValues.nlargest(7, 're_score')), print(stFeatureValues.nlargest(7, 'st_score'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The importance of different features can be ranked using the ExtraTressClassifier which is also known as the Extremely Randomized Trees. In this Extra Trees classifier, the features and splits are selected at random; hence, “Extremely Randomized Tree”. Since splits are chosen at random for each feature in the Extra Trees Classifier, it’s less computationally expensive than a Random Forest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using feature importance\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "model = ExtraTreesClassifier()\n",
+    "model.fit(predictors, target)\n",
+    "print(model.feature_importances_)\n",
+    "\n",
+    "# visualising feature importance\n",
+    "importances = pd.Series(model.feature_importances_, index=data.columns[0:11])\n",
+    "importances.nlargest(10).plot(kind='barh')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Building the classification model\n",
+    "Finding te right model for a given dataset is an iterative methods. It is advisable to pursue all combinations of data and algorithms to find the best algorithm that is able to offer satisfactory results on a given dataset.\n",
+    "It cannot be expected that an algorithm that performs well on a particular dataset will perform the same eay in another dataset.\n",
+    "Therefore, an exhaustive approach is necessary to find the best performing algorithm for a given dataset.\n",
+    "\n",
+    "Some of these combinations are (for original and transformed data):\n",
+    "- model + train/test sets\n",
+    "- model + kfold crossvalidation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Splitting the data_one dataset into training and test datasets and using a logit function to classify instances\n",
+    "from sklearn.model_selection import train_test_split # random split\n",
+    "from sklearn.linear_model import LogisticRegression # all machine learning models in Python are implemented as classes\n",
+    "p_train, p_test, t_train, t_test = train_test_split(predictors, target, \n",
+    "                                                    test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on untransformed data\n",
+    "logit.fit(p_train, t_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Measuring model performance\n",
+    "Logistic regression is a supervised learning algorithm. Performance of the algorithm is measured as the number of correct predictions made by the algorithm out the the number of true class values.\n",
+    "We can measure the performance of a classification problem using precison, F1 Score, Receiver Operating Curve (ROC) curve, accuracy and Matthews Correlation Coefficient.\n",
+    "\n",
+    "Other performance metric are;\n",
+    "- True Positives (TP) / True Positive Rate (TPR): Number of correct positive predictions / Probability of predicting positive given that the actual class is positive\n",
+    "- False Negatives (FN) / False Negative Rate (FNR): Number of wrong negative predictions / Probability of predicting negative given that the actual class is positive\n",
+    "- True Negatives (TN) / True Negative Rate (TNR): Number of correct negative predictions / Probability of predicting negative given that the actual class is negative\n",
+    "- False Positives (FP) / False Positive Rate (FPR): Number of wrong positive predictions / Probability of predicting positive given that the actual class is negative\n",
+    "- Precision (P): Proportion of predicted positives that are correct\n",
+    "- Recall (R): Proportion of actual positives captured"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict on test data\n",
+    "predictions = logit.predict(p_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the confusion matrix\n",
+    "from sklearn import metrics\n",
+    "cm = metrics.confusion_matrix(t_test, predictions)\n",
+    "cm\n",
+    "sb.heatmap(cm, annot=True, fmt='.3f', linewidths=.5,\n",
+    "          square=True, cmap='Blues') \n",
+    "plt.ylabel('Actual label'); plt.xlabel('Predicted label')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t_test, predictions)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t_test, predictions)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t_test, predictions)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t_test, predictions)) # takes into account true and false positives and negatives, \n",
+    "                                                      # higher values are better\n",
+    "# not affected by unbalanced classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Performance metrics are inter-related and can be visualised using an ROC curve where the true positive rate is plotted against the false positive rate. Essentially, the curve shows the tradeoff between sensitivity and specificity.\n",
+    "The maximum area under the curve (AUC) is one which represents 100% accuracy therefore higher values of AUC are desired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_probs = logit.predict_proba(p_test)[::,1] # start=0, stop=size of dimension, step=1\n",
+    "fpr, tpr,_ = metrics.roc_curve(t_test, pred_probs)\n",
+    "auc = metrics.roc_auc_score(t_test, pred_probs)\n",
+    "plt.plot(fpr, tpr, label = 'Untransformed+all Var, auc='+ str(auc))\n",
+    "plt.legend(loc=4)\n",
+    "plt.ylabel('tpr'), plt.xlabel('fpr')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The confusion matrix above shows the number of true positives, true negatives, false positives as well as false negatives.\n",
+    "The model made a total of 75 wrong class predictions. It can be seen that logistic regression has a good performance of the original dataset where all features are utilised in the prediction of the class.\n",
+    "\n",
+    "Something to keep in mind is that a good performance of the training set by an algorithm is not an indicator of how the model will performance when new data is presented to it. However, a good performance is desirable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance on new data\n",
+    "new_pred = logit.predict(new.values)\n",
+    "\n",
+    "pred_df = pd.DataFrame(new_pred) \n",
+    "pred_df.columns=[\"CLASS\"]\n",
+    "pred_df.index.name=\"Index\" \n",
+    "pred_df[\"CLASS\"] = pred_df[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df.to_csv(\"ilujumba.csv\") \n",
+    "print(pred_df['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the csv file was submitted to the competition, the model had an 83.33% accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Machine learning is an iterative process\n",
+    "\n",
+    "Different combinations of logistic regression with the data were tried.\n",
+    "- train_test split\n",
+    "\n",
+    "    model + rescaled data\n",
+    "    \n",
+    "    model + standardised data\n",
+    "\n",
+    "\n",
+    "- kfold crossvalidation\n",
+    "\n",
+    "    model + rescaled data\n",
+    "    \n",
+    "    model + standardised data\n",
+    "\n",
+    "#### Logistic regression on rescaled data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p1_train, p1_test, t1_train, t1_test = train_test_split(rescaledPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit1 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit1.fit(p1_train, t1_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions1 = logit1.predict(p1_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t1_test, predictions1)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t1_test, predictions1)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t1_test, predictions1)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t1_test, predictions1))\n",
+    "\n",
+    "# rescaling new data\n",
+    "newArray = new.to_numpy()\n",
+    "rescaledNew = scaler.fit_transform(newArray)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# performance on new data (rescaled)\n",
+    "new_pred1 = logit1.predict(rescaledNew)\n",
+    "\n",
+    "pred_df1 = pd.DataFrame(new_pred1) \n",
+    "pred_df1.columns=[\"CLASS\"]\n",
+    "pred_df1.index.name=\"Index\" \n",
+    "pred_df1[\"CLASS\"] = pred_df1[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df1.to_csv(\"ilujumba1.csv\") \n",
+    "print(pred_df1['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df1.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df1.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This combination returned an 88.11% accuracy after submission."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic regression on standardized data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p2_train, p2_test, t2_train, t2_test = train_test_split(standardizedPredictors, target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit2 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit2.fit(p2_train, t2_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions2 = logit2.predict(p2_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t2_test, predictions2)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t2_test, predictions2)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t2_test, predictions2)*100)\n",
+    "print('MCC: ',matthews_corrcoef(t2_test, predictions2))\n",
+    "\n",
+    "# standardizing new data\n",
+    "standardizedNew = scaler1.transform(newArray)\n",
+    "\n",
+    "# performance on new data (standaridized)\n",
+    "new_pred2 = logit2.predict(standardizedNew)\n",
+    "\n",
+    "pred_df2 = pd.DataFrame(new_pred2) \n",
+    "pred_df2.columns=[\"CLASS\"]\n",
+    "pred_df2.index.name=\"Index\" \n",
+    "pred_df2[\"CLASS\"] = pred_df2[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df2.to_csv(\"ilujumba2.csv\") \n",
+    "print(pred_df2['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df2.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df2.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model streategy returned an 86.03% accuracy after submission which is better than when the original data is used but slightly lower than when rescaled features are used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using selected features, rescaled data and Logistic regression\n",
+    "\n",
+    "An attempt was made to manually select for features that showed the highest importance in the prediction of class. All further strategies on data using Logistic Regression were done using rescaled data with the assumption that it could provide better model performance results when compared to other transformations of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p3_train, p3_test, t3_train, t3_test = train_test_split(rescaledPredictors[:,(0,1,2,3,7)], target, \n",
+    "                                                        test_size=0.30,random_state=42)\n",
+    "\n",
+    "logit3 = LogisticRegression() # making instance of model\n",
+    "\n",
+    "# fitting the model on rescaled data\n",
+    "logit3.fit(p3_train, t3_train)\n",
+    "\n",
+    "# predict on test data\n",
+    "predictions3 = logit3.predict(p3_test)\n",
+    "\n",
+    "# performance metrics\n",
+    "print(\"Accuracy: \",metrics.accuracy_score(t3_test, predictions3)*100)\n",
+    "print(\"Precision: \",metrics.precision_score(t3_test, predictions3)*100)\n",
+    "print(\"Recall: \",metrics.recall_score(t3_test, predictions3)*100)\n",
+    "\n",
+    "from sklearn.metrics import matthews_corrcoef\n",
+    "print('MCC: ',matthews_corrcoef(t3_test, predictions3))\n",
+    "\n",
+    "# rescaling new data\n",
+    "# newArray = new.to_numpy()\n",
+    "# rescaledNew = scaler.fit_transform(newArray)\n",
+    "\n",
+    "# performance on new data (rescaled)\n",
+    "new_pred3 = logit3.predict(rescaledNew[:,(0,1,2,3,7)])\n",
+    "\n",
+    "pred_df3 = pd.DataFrame(new_pred3) \n",
+    "pred_df3.columns=[\"CLASS\"]\n",
+    "pred_df3.index.name=\"Index\" \n",
+    "pred_df3[\"CLASS\"] = pred_df3[\"CLASS\"].map({0:'False',1.0:'True'})\n",
+    "\n",
+    "#csv file output\n",
+    "pred_df3.to_csv(\"ilujumba3.csv\") \n",
+    "print(pred_df3['CLASS'].unique())\n",
+    "\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(pred_df3.groupby('CLASS').size()[0].sum())\n",
+    "print(pred_df3.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using cross-validation and Logistic Regression\n",
+    "\n",
+    "During cross-validation, the data is split into kfolds. k-1 folds are used in the training are used while the kth fold is used for testing. The process is repeated for k times until each fold has been used as a testing set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42)\n",
+    "model6 = LogisticRegression()\n",
+    "model6.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model6, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model6_pred = model6.predict(rescaledNew)\n",
+    "df6 = pd.DataFrame(model6_pred)\n",
+    "df6.columns = ['CLASS']\n",
+    "df6.index.name = 'Index'\n",
+    "df6['CLASS'] = df6['CLASS'].map({0.0:False, 1.0:True})\n",
+    "\n",
+    "df6.to_csv('ilujumba7.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another thing to keep in mind is to exhaustive use of simple models on the data. The reason for this is that they have a good reputation in terms of performance on different types of data and their method of prediction is well understood. \n",
+    "\n",
+    "\n",
+    "Another simple algorithm for classification is the Naive Bayes classifier. This algorithm assumes that all features are independent of each other and each feature contributes equally to the resulting class. For this reason, it is called 'naive'.\n",
+    "\n",
+    "#### Naive Bayes classifier with kfold cross-validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model7 = GaussianNB()\n",
+    "model7.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model7, predictors, target)\n",
+    "print(results.mean())\n",
+    "\n",
+    "model7_pred = model7.predict(newArray)\n",
+    "df7 = pd.DataFrame(model7_pred)\n",
+    "df7.columns = ['CLASS']\n",
+    "df7.index.name = 'Index'\n",
+    "df7['CLASS'] = df7['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df7.to_csv('ilujumba7.csv')\n",
+    "print(df7['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df7.groupby('CLASS').size()[0].sum())\n",
+    "print(df7.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes classifier on rescaled features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model8 = GaussianNB()\n",
+    "model8.fit(rescaledPredictors, target)\n",
+    "\n",
+    "results1 = cross_val_score(model8, rescaledPredictors, target)\n",
+    "print(results1.mean())\n",
+    "\n",
+    "model8_pred = model8.predict(rescaledNew)\n",
+    "df8 = pd.DataFrame(model8_pred)\n",
+    "df8.columns = ['CLASS']\n",
+    "df8.index.name = 'Index'\n",
+    "df8['CLASS'] = df8['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df8.to_csv('ilujumba8.csv')\n",
+    "print(df8['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df8.groupby('CLASS').size()[0].sum())\n",
+    "print(df8.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Bayes and kfold validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "kfold = KFold(n_splits=10, random_state=42, shuffle=True)\n",
+    "model9 = GaussianNB()\n",
+    "model9.fit(predictors, target)\n",
+    "\n",
+    "results = cross_val_score(model9, predictors, target, cv =10) # ten-fold cross validation\n",
+    "print('mean for results', results.mean())\n",
+    "\n",
+    "predic = cross_val_predict(model9, predictors, target, cv =10)\n",
+    "accuracy = metrics.r2_score(target, predic)\n",
+    "print('cross-predicted accuracy ', accuracy)\n",
+    "\n",
+    "model9_pred = model9.predict(newArray)\n",
+    "df9 = pd.DataFrame(model9_pred)\n",
+    "df9.columns = ['CLASS']\n",
+    "df9.index.name = 'Index'\n",
+    "df9['CLASS'] = df9['CLASS'].map({0.0:'False', 1.0:'True'})\n",
+    "\n",
+    "df9.to_csv('ilujumba9.csv')\n",
+    "print(df9['CLASS'].unique())\n",
+    "\n",
+    "#printing the numbers of False and True\n",
+    "print(df9.groupby('CLASS').size()[0].sum())\n",
+    "print(df9.groupby('CLASS').size()[1].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model strategy returned the highest score after submission with a 99.599% accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing several algorithms to look at the nature of the decision boundaries created"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://medium.com/cascade-bio-blog/creating-visualizations-to-better-understand-your-data-and-models-part-2-28d5c46e956"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Algorithms define a set of hyperplanes that divide the datapoints to their respective classes and span the feature space trained on. Visualising enables one to understand the limitations of a given algorithm on a dataset given to it.\n",
+    "Thus decision boundaries enable one to understand to how the training data selected affects performance of the algorithm."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ten sklearn non-linear classifier algorithms were compared. KNearest Neighbors, Support Vector Machines (Linear and RBF kernels), Gaussian Process Classifier, Decision Trees, Random Forest, Neural Networks, AdaBoost, Naive Bayes, \"Quadratic Discriminant Analysis, Logistic Regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing classifiers from the sklearn library\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPClassifier #1\n",
+    "from sklearn.neighbors import KNeighborsClassifier #2\n",
+    "from sklearn.svm import SVC #3\n",
+    "from sklearn.gaussian_process import GaussianProcessClassifier #4\n",
+    "from sklearn.gaussian_process.kernels import RBF #5\n",
+    "from sklearn.tree import DecisionTreeClassifier #6\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier #7,8\n",
+    "from sklearn.naive_bayes import GaussianNB #9\n",
+    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #10\n",
+    "from sklearn.linear_model import LogisticRegression #11\n",
+    "\n",
+    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
+    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
+    "         \"Naive Bayes\", \"QDA\", \"Logistic Regression\"]\n",
+    "\n",
+    "classifiers = [\n",
+    "    KNeighborsClassifier(3), # holds no assumption on data distribution (non-parametric)\n",
+    "    SVC(kernel=\"linear\", C=0.025), # using a linear kernel\n",
+    "    SVC(gamma=2, C=1), # using radial basis function  kernel,C is low to enable a large decision margin\n",
+    "    GaussianProcessClassifier(1.0 * RBF(1.0)), # based on Laplace approximation\n",
+    "    DecisionTreeClassifier(),\n",
+    "    RandomForestClassifier(n_estimators=100), # 100 trees in the forest\n",
+    "    MLPClassifier(max_iter=1000), #iterations until converge\n",
+    "    AdaBoostClassifier(), # fits multiple classifiers on the same dataset\n",
+    "    GaussianNB(), #NaiveBayes\n",
+    "    QuadraticDiscriminantAnalysis(),\n",
+    "    LogisticRegression()]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dimensionality reduction\n",
+    "\n",
+    "https://stackabuse.com/dimensionality-reduction-in-python-with-scikit-learn/\n",
+    "\n",
+    "https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the data is multi-dimensional, it was reduced using Principal Component Analysis (PCA) to reduce it to two components.\n",
+    "Trial runs were done to check how much of the variation in the data is explained by the principal components.\n",
+    "\n",
+    "Another thing to keep in mind is that PCA works best on standardised/normalised data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preprocessing the dataset\n",
+    "dataArray = data.to_numpy()\n",
+    "X, y = dataArray[:,0:11], dataArray[0:,11]\n",
+    "X = StandardScaler().fit_transform(X)\n",
+    "\n",
+    "# reducing dimensions of the dataset using PCA  \n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA()\n",
+    "pca.fit_transform(X)\n",
+    "pca_variance = pca.explained_variance_\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(range(11), pca_variance, alpha=0.5, align='center', label='individual variance')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Variance ratio')\n",
+    "plt.xlabel('Principal components')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca2 = PCA(0.95) # keeping principal components that explain 95% of the variance\n",
+    "ninety_five = pca2.fit_transform(X)\n",
+    "ninety_five.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Explained variance: \", sum(pca2.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eight features explain 95% of the variance in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca2 = PCA(3) # keeping features three principal components\n",
+    "principalComponents = pca2.fit_transform(X)\n",
+    "\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "plt.figure(figsize=(10,6))\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.scatter(principalComponents[:,0], principalComponents[:,1], principalComponents[:,2], \n",
+    "           linewidths=1, alpha=.5,\n",
+    "           edgecolor='k', s= 200,\n",
+    "           c=data['CLASS'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The three pincipal components wete visualised using a 3D plot. The figure above shows clustering of the three components. Each component is not exactly independent of the others so the clusters overlap to some extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#converting principal component ndarrays to DataFrame format\n",
+    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2','PC3'])\n",
+    "finalDf = pd.concat([principalDf, data['CLASS']], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finalDf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('Variance explained by three PCs: ',sum(pca2.explained_variance_ratio_)*100,'%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualising the top 2 principal components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize = (6,6))\n",
+    "ax = fig.add_subplot(111) \n",
+    "ax.set(xlim=(-10,10), ylim=(-10,10))\n",
+    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
+    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
+    "ax.set_title('top 2 components', fontsize = 20)\n",
+    "\n",
+    "targets = [0, 1]\n",
+    "colors = ['r', 'g']\n",
+    "\n",
+    "for target, color in zip(targets,colors):\n",
+    "    indices = finalDf['CLASS'] == target\n",
+    "    ax.scatter(finalDf.loc[indices, 'PC1']\n",
+    "               , finalDf.loc[indices, 'PC2']\n",
+    "               , c = color\n",
+    "               , s = 50, alpha = 0.4)\n",
+    "ax.legend(targets)\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting the into training and test part\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A mesh grid is required. This can be thought of as a matrix of coordinates upon which the model will make decisions.\n",
+    "These are then visualised to reveal decision boundaries.\n",
+    "The mesh grip was created based on the data and a step size of 0.02"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating mesh for the contour plot\n",
+    "\n",
+    "h = .02  # step size in the mesh\n",
+    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
+    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two principal components were used to enable visualisation on a scatter plot.\n",
+    "\n",
+    "The parameters for the PCA were generated on the training data and these were applied on both the training and training sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca4 = PCA(n_components=2)\n",
+    "\n",
+    "# applying PCA on training set\n",
+    "pca4.fit(X_train)\n",
+    "\n",
+    "#applying transform on training and testing sets\n",
+    "train_ = pca4.transform(X_train)\n",
+    "test_ = pca4.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Explained variance: \", sum(pca4.explained_variance_ratio_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_.shape, test_.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After transforming the data and the creating the meshgrid, decision boundaries for the algorithms were created by iterating over the classifiers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "figure = plt.figure(figsize=(27, 15))\n",
+    "i = 1\n",
+    "\n",
+    "datasets=[data]\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
+    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "        # Plot the top 2 principal components for training data\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright,\n",
+    "                    edgecolors='k')\n",
+    "        # Plot the top 2 principal components for the testing data\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n",
+    "                    edgecolors='k')\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        i += 1\n",
+    "\n",
+    "        # iterate over classifiers\n",
+    "\n",
+    "    for name, clf in zip(names, classifiers):\n",
+    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "        clf.fit(train_, y_train)\n",
+    "        score = clf.score(test_, y_test)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) # confidence scores\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] # probability estimates\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        ax.scatter(train_[:, 0], train_[:, 1], c=y_train, cmap=cm_bright, edgecolors='k')\n",
+    "        # Plot the testing points\n",
+    "        ax.scatter(test_[:, 0], test_[:, 1], c=y_test, cmap=cm_bright, edgecolors='k', alpha=0.4)\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(name)\n",
+    "            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), size=15, horizontalalignment='right')\n",
+    "            i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Accuracies of the different algorithms are indicated on the lower right corner.\n",
+    "\n",
+    "The plots show training points in solid colors and testing points semi-transparent.Decision boundaries for GaussianProcessClassifier, RandomForest and AdaBoost are complicated while the decison boundaries for LogisticRegression, NaiveBayes, NeuralNetwork, and Linear SVM are simpler. GaussianProcess and RBF SVM have contoured decision boundaries which seperate points with similar characteristics.\n"
+   ]
+  }
+ ],
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+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
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+    "name": "ipython",
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