diff --git a/.Rhistory b/.Rhistory new file mode 100644 index 0000000..e69de29 diff --git a/.github/workflows/e_kariuki.yaml b/.github/workflows/e_kariuki.yaml deleted file mode 100644 index e54d08b..0000000 --- a/.github/workflows/e_kariuki.yaml +++ /dev/null @@ -1,17 +0,0 @@ -name: CI - -on: [push] - -jobs: - build: - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v2 - - name: Run a one-line script - run: echo Hello, world! - - name: Run a multi-line script - run: | - echo Add other actions to build, - echo test, and deploy your project. diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e69de29 diff --git a/.ipynb_checkpoints/Fezile Dlamini-checkpoint.ipynb b/.ipynb_checkpoints/Fezile Dlamini-checkpoint.ipynb new file mode 100644 index 0000000..2dd9fe7 --- /dev/null +++ b/.ipynb_checkpoints/Fezile Dlamini-checkpoint.ipynb @@ -0,0 +1,778 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "outputs": [], + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", + "# For example, here's several helpful packages to load in \n", + "\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "\n", + "# Input data files are available in the \"../input/\" directory.\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "for dirname, _, filenames in os.walk('/kaggle/input'):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "\n", + "# Any results you write to the current directory are saved as output." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" + }, + "outputs": [], + "source": [ + "###Importing libraries\n", + "import pandas\n", + "import scipy\n", + "import numpy\n", + "import matplotlib\n", + "import sklearn\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading some libraries\n", + "### These are some of the libraries I think I will need" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pandas import read_csv\n", + "from pandas.plotting import scatter_matrix\n", + "from matplotlib import pyplot\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.svm import SVC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading My Dataset\n", + "\n", + "##### I am going to first use my training dataset, then the test dataset last." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train = pandas.read_csv('/kaggle/input/ace-class-assignment/AMP_TrainSet.csv')\n", + "train\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test= pandas.read_csv('/kaggle/input/ace-class-assignment/Test.csv')\n", + "test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspecting my train dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.isnull().sum() ###this will show the number of null values in my data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.count() #returns number of non-null values in my data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### It seems i have no missing values in my data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.describe() #returns summary of the whole data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.info() #It returns range, column, number of non-null objects of each column, datatype and memory usage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### This shows that my dataset has 3038 rows (instances) and 12 columns (attributes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### I will also take a look at how many instances i have for each class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.groupby('CLASS').size()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.groupby('CLASS').size().plot(kind='bar')\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### I have two groups of classes, each with 1519 instances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DATA VISUALISATION" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### I will start with univariate plots to see each indiviadual variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.hist(figsize=(16,16))\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.corr(method='pearson')['CLASS'] #Here I tried to see the correlation of all attributes with the class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multivariate " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "correlations = train.corr()\n", + "# plot correlation matrix\n", + "fig = pyplot.figure()\n", + "ax = fig.add_subplot(111)\n", + "cax = ax.matshow(correlations, vmin=-1, vmax=1)\n", + "fig.colorbar(cax)\n", + "ticks = np.arange(0,9,1)\n", + "ax.set_xticks(ticks)\n", + "ax.set_yticks(ticks)\n", + "ax.set_xticklabels(train.columns)\n", + "ax.set_yticklabels(train.columns)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### i did this plot in order to see which features are highly correlated as this can be a problem in soome models if some features are used together whereas they are highly correlated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# EVALUATING ALGORITHMS\n", + "#### I will now evaluate some algorithms and estimate their accuracy on unseen data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Building models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "test_size = 0.32\n", + "seed = 3\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "random_state=seed)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "models = []\n", + "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('SVM', SVC(gamma='auto')))\n", + "# evaluate each model in turn\n", + "results = []\n", + "names = []\n", + "for name, model in models:\n", + " kfold = StratifiedKFold(n_splits=10)\n", + " cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare Algorithms\n", + "pyplot.boxplot(results, labels=names)\n", + "pyplot.title('Algorithm Comparison')\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### from here, NB is the best performing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make predictions on validation dataset\n", + "model = GaussianNB()\n", + "model.fit(X_train, Y_train)\n", + "predictions = model.predict(X_test)\n", + "print(predictions)\n", + "\n", + "#from sklearn.metrics import matthews_corrcoef\n", + "#print('MCC', matthews_corrcoef(model.predict(X_test), Y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#with all features\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.naive_bayes import GaussianNB\n", + "\n", + "array = train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "test_size = 0.32\n", + "seed = 3\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "random_state=seed)\n", + "model = GaussianNB()\n", + "model.fit(X_train, Y_train)\n", + "result = model.score(X_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate predictions\n", + "print(accuracy_score(Y_test, predictions))\n", + "print(confusion_matrix(Y_test, predictions))\n", + "print(classification_report(Y_test, predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Y=train.CLASS\n", + "X=train.drop(\"CLASS\",axis=1)\n", + "OUTPUT=model.fit(X, Y).predict(test.values)\n", + "OUTPUT_1=pd.DataFrame(OUTPUT)\n", + "OUTPUT_1.columns=[\"CLASS\"]\n", + "OUTPUT_1.index.name=\"Index\"\n", + "OUTPUT_1[\"CLASS\"]=OUTPUT_1[\"CLASS\"].map({0.0:False,1.0:True})\n", + "OUTPUT_1.to_csv(\"output\")\n", + "print(OUTPUT_1[\"CLASS\"].unique())\n", + "print(OUTPUT_1[\"CLASS\"].nunique())\n", + "print(OUTPUT_1.groupby(\"CLASS\").size()[0].sum())\n", + "print(OUTPUT_1.groupby(\"CLASS\").size()[1].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#feature selection using feature importance\n", + "X = train.iloc[:,0:11] #independent columns\n", + "y = train.iloc[:,-1] #target column\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "import matplotlib.pyplot as plt\n", + "model = ExtraTreesClassifier()\n", + "model.fit(X,y)\n", + "print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers\n", + "#plot graph of feature importances for better visualization\n", + "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n", + "feat_importances.nlargest(10).plot(kind='barh')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "newtrain=train.drop([ 'FULL_AURR980107', 'FULL_OOBM850104', 'NT_EFC195', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "newtest=test.drop([ 'FULL_AURR980107', 'FULL_OOBM850104', 'NT_EFC195', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "newtrain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = newtrain.values\n", + "X = array[:,0:5]\n", + "Y = array[:,-1]\n", + "test_size = 0.32\n", + "seed = 3\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "random_state=seed)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "models = []\n", + "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('SVM', SVC(gamma='auto')))\n", + "# evaluate each model in turn\n", + "results = []\n", + "names = []\n", + "for name, model in models:\n", + " kfold = StratifiedKFold(n_splits=23, shuffle=True)\n", + " cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = GaussianNB()\n", + "model.fit(X_train, Y_train)\n", + "result = model.score(X_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate predictions\n", + "print(accuracy_score(Y_test, predictions))\n", + "print(confusion_matrix(Y_test, predictions))\n", + "print(classification_report(Y_test, predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Y=newtrain.CLASS\n", + "X=newtrain.drop(\"CLASS\",axis=1)\n", + "OUTPUT=model.fit(X, Y).predict(newtest.values)\n", + "OUTPUT_1=pd.DataFrame(OUTPUT)\n", + "OUTPUT_1.columns=[\"CLASS\"]\n", + "OUTPUT_1.index.name=\"Index\"\n", + "OUTPUT_1[\"CLASS\"]=OUTPUT_1[\"CLASS\"].map({0.0:False,1.0:True})\n", + "OUTPUT_1.to_csv(\"out1\")\n", + "print(OUTPUT_1[\"CLASS\"].unique())\n", + "print(OUTPUT_1[\"CLASS\"].nunique())\n", + "print(OUTPUT_1.groupby(\"CLASS\").size()[0].sum())\n", + "print(OUTPUT_1.groupby(\"CLASS\").size()[1].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#checking accuracy using data with selected features from feature importance\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "array = train.values\n", + "X = array[:,[0,1,3,5,7]]\n", + "Y = array[:,11]\n", + "test_size = 0.32\n", + "seed = 3\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "random_state=seed)\n", + "model = GaussianNB()\n", + "model.fit(X_train, Y_train)\n", + "result = model.score(X_test, Y_test)\n", + "prediction=model.predict(X_test)\n", + "matrix=matthews_corrcoef(Y_test,prediction)\n", + "print(\"Accuracy: \", (result*100.0))\n", + "print(matrix)\n", + "\n", + "# making predictions on the test data\n", + "\n", + "#output=model.predict(test.values)\n", + "#output1=pd.DataFrame(output)\n", + "#output1.columns=['CLASS']\n", + "#output1.index.name='Index'\n", + "#output1['CLASS']=output1['CLASS'].map({0.0:False, 1.0:True})\n", + "\n", + "#print(output1['CLASS'].unique())\n", + "#print(output1['CLASS'].nunique())\n", + "\n", + "#print(output1.groupby('CLASS').size()[0].sum())\n", + "#print(output1.groupby('CLASS').size()[1].sum())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array2 = np.array(test)\n", + "X = array2[:,[0,1,3,5,7]]\n", + "x_t=array[:,[0,1,3,5,7]]\n", + "y_t=array[:,11]\n", + "\n", + "#array3 = train.values\n", + "#X_t = array3[:,[0,1,3,5,7]]\n", + "#Y_t=array3[:,10]\n", + "test_size = 0.32\n", + "seed = 3\n", + "#X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "#random_state=seed)\n", + "test=array3[:,[0,1,3,5,7]]\n", + "model = GaussianNB()\n", + "\n", + "#result = model.score(X_t, Y_t)\n", + "model.fit(x_t, y_t)\n", + "prediction=model.predict(X)\n", + "\n", + "#matrix=matthews_corrcoef(Y_t,prediction)\n", + "#print(\"Accuracy: \", (result*100.0))\n", + "#print(matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FEATURE SELECTION" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#feature selection using Logistic regression\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array_1 = train.values\n", + "X = array_1[:,0:11]\n", + "Y = array_1[:,11]\n", + "# feature extraction\n", + "model = LogisticRegression()\n", + "rfe = RFE(model, 5)\n", + "fit = rfe.fit(X, Y)\n", + "print(\"Num Features: \", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking: \", fit.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#checking accuracy using data with selected features from feature importance\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.naive_bayes import GaussianNB\n", + "\n", + "array = train.values\n", + "X = array[:,[0,2,3,5,7]]\n", + "Y = array[:,11]\n", + "test_size = 0.32\n", + "seed = 3\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "random_state=seed)\n", + "model = GaussianNB()\n", + "model.fit(X_train, Y_train)\n", + "result = model.score(X_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#CHECKING ACCURACY WITH ALL FEATURES\n", + "array = train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "test_size = 0.32\n", + "seed = 3\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "random_state=seed)\n", + "model = GaussianNB()\n", + "model.fit(X_train, Y_train)\n", + "result = model.score(X_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train.head(5)" + ] + } + ], + "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/.ipynb_checkpoints/KAYAGA NASHIM MILVAT-checkpoint.ipynb b/.ipynb_checkpoints/KAYAGA NASHIM MILVAT-checkpoint.ipynb new file mode 100644 index 0000000..60d7103 --- /dev/null +++ b/.ipynb_checkpoints/KAYAGA NASHIM MILVAT-checkpoint.ipynb @@ -0,0 +1,2767 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/kaggle/input/ace-class-assignment/Test.csv\n", + "/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\n" + ] + } + ], + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", + "# For example, here's several helpful packages to load in \n", + "\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "# Input data files are available in the \"../input/\" directory.\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "for dirname, _, filenames in os.walk('/kaggle/input'):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "\n", + "# Any results you write to the current directory are saved as output." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "\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": [ + "#Generate descriptive statistics that summarize the central tendency, dispersion, and shape of a dataset’s distribution, excluding NaN values\n", + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FULL_Charge 0\n", + "FULL_AcidicMolPerc 0\n", + "FULL_AURR980107 0\n", + "FULL_DAYM780201 0\n", + "FULL_GEOR030101 0\n", + "FULL_OOBM850104 0\n", + "NT_EFC195 0\n", + "AS_MeanAmphiMoment 0\n", + "AS_DAYM780201 0\n", + "AS_FUKS010112 0\n", + "CT_RACS820104 0\n", + "CLASS 0\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#number of null values in each column\n", + "data.isnull().sum()\n", + "#since my data has no null values then its good to go" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104CLASS
FULL_Charge1.000000-0.612996-0.490977-0.434603-0.058725-0.2837580.0880680.355477-0.365374-0.0905700.2329290.534602
FULL_AcidicMolPerc-0.6129961.0000000.7947960.5414810.1152010.513344-0.143168-0.4315900.4496210.002334-0.213543-0.598816
FULL_AURR980107-0.4909770.7947961.0000000.5482530.3461390.462712-0.169540-0.4260970.4562600.032958-0.403599-0.584111
FULL_DAYM780201-0.4346030.5414810.5482531.0000000.0101180.334778-0.090058-0.4087930.8941910.055915-0.326792-0.554838
FULL_GEOR030101-0.0587250.1152010.3461390.0101181.0000000.319157-0.230417-0.160269-0.0290850.040480-0.151935-0.260470
FULL_OOBM850104-0.2837580.5133440.4627120.3347780.3191571.000000-0.230561-0.3362970.275640-0.4527690.155304-0.453287
NT_EFC1950.088068-0.143168-0.169540-0.090058-0.230417-0.2305611.0000000.178683-0.0368440.1459240.0808980.260702
AS_MeanAmphiMoment0.355477-0.431590-0.426097-0.408793-0.160269-0.3362970.1786831.000000-0.3223780.0255800.1715240.693552
AS_DAYM780201-0.3653740.4496210.4562600.894191-0.0290850.275640-0.036844-0.3223781.0000000.045562-0.256060-0.437168
AS_FUKS010112-0.0905700.0023340.0329580.0559150.040480-0.4527690.1459240.0255800.0455621.000000-0.4452840.033432
CT_RACS8201040.232929-0.213543-0.403599-0.326792-0.1519350.1553040.0808980.171524-0.256060-0.4452841.0000000.267652
CLASS0.534602-0.598816-0.584111-0.554838-0.260470-0.4532870.2607020.693552-0.4371680.0334320.2676521.000000
\n", + "
" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 \\\n", + "FULL_Charge 1.000000 -0.612996 -0.490977 \n", + "FULL_AcidicMolPerc -0.612996 1.000000 0.794796 \n", + "FULL_AURR980107 -0.490977 0.794796 1.000000 \n", + "FULL_DAYM780201 -0.434603 0.541481 0.548253 \n", + "FULL_GEOR030101 -0.058725 0.115201 0.346139 \n", + "FULL_OOBM850104 -0.283758 0.513344 0.462712 \n", + "NT_EFC195 0.088068 -0.143168 -0.169540 \n", + "AS_MeanAmphiMoment 0.355477 -0.431590 -0.426097 \n", + "AS_DAYM780201 -0.365374 0.449621 0.456260 \n", + "AS_FUKS010112 -0.090570 0.002334 0.032958 \n", + "CT_RACS820104 0.232929 -0.213543 -0.403599 \n", + "CLASS 0.534602 -0.598816 -0.584111 \n", + "\n", + " FULL_DAYM780201 FULL_GEOR030101 FULL_OOBM850104 \\\n", + "FULL_Charge -0.434603 -0.058725 -0.283758 \n", + "FULL_AcidicMolPerc 0.541481 0.115201 0.513344 \n", + "FULL_AURR980107 0.548253 0.346139 0.462712 \n", + "FULL_DAYM780201 1.000000 0.010118 0.334778 \n", + "FULL_GEOR030101 0.010118 1.000000 0.319157 \n", + "FULL_OOBM850104 0.334778 0.319157 1.000000 \n", + "NT_EFC195 -0.090058 -0.230417 -0.230561 \n", + "AS_MeanAmphiMoment -0.408793 -0.160269 -0.336297 \n", + "AS_DAYM780201 0.894191 -0.029085 0.275640 \n", + "AS_FUKS010112 0.055915 0.040480 -0.452769 \n", + "CT_RACS820104 -0.326792 -0.151935 0.155304 \n", + "CLASS -0.554838 -0.260470 -0.453287 \n", + "\n", + " NT_EFC195 AS_MeanAmphiMoment AS_DAYM780201 \\\n", + "FULL_Charge 0.088068 0.355477 -0.365374 \n", + "FULL_AcidicMolPerc -0.143168 -0.431590 0.449621 \n", + "FULL_AURR980107 -0.169540 -0.426097 0.456260 \n", + "FULL_DAYM780201 -0.090058 -0.408793 0.894191 \n", + "FULL_GEOR030101 -0.230417 -0.160269 -0.029085 \n", + "FULL_OOBM850104 -0.230561 -0.336297 0.275640 \n", + "NT_EFC195 1.000000 0.178683 -0.036844 \n", + "AS_MeanAmphiMoment 0.178683 1.000000 -0.322378 \n", + "AS_DAYM780201 -0.036844 -0.322378 1.000000 \n", + "AS_FUKS010112 0.145924 0.025580 0.045562 \n", + "CT_RACS820104 0.080898 0.171524 -0.256060 \n", + "CLASS 0.260702 0.693552 -0.437168 \n", + "\n", + " AS_FUKS010112 CT_RACS820104 CLASS \n", + "FULL_Charge -0.090570 0.232929 0.534602 \n", + "FULL_AcidicMolPerc 0.002334 -0.213543 -0.598816 \n", + "FULL_AURR980107 0.032958 -0.403599 -0.584111 \n", + "FULL_DAYM780201 0.055915 -0.326792 -0.554838 \n", + "FULL_GEOR030101 0.040480 -0.151935 -0.260470 \n", + "FULL_OOBM850104 -0.452769 0.155304 -0.453287 \n", + "NT_EFC195 0.145924 0.080898 0.260702 \n", + "AS_MeanAmphiMoment 0.025580 0.171524 0.693552 \n", + "AS_DAYM780201 0.045562 -0.256060 -0.437168 \n", + "AS_FUKS010112 1.000000 -0.445284 0.033432 \n", + "CT_RACS820104 -0.445284 1.000000 0.267652 \n", + "CLASS 0.033432 0.267652 1.000000 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Its a good idea to review all the pairwise correlations of the attributes in the dataset because some machine learning algorithm like linear and logistic regression can suffer poor performance if there are highly c orrelated attributes in the dataset\n", + "data.corr(method='pearson')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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S90XEYoD0c4v8TkkbpPO6ttLBkiZKmi1p9mtvuvBjZmatpdELlJelKZa8aqlhrVLGNmBKej0FGA9c18O4P0zrbrYA9inrd5WkDYF+wIcBIuIlSV8CrgE6gT+QVXtK5zE5Is6X9FHgl5J2BdTNOayJTwC/rzaFFRGTgEkA2w7erXlTczMzqx0vUG6oF4FNy9oGAy/0duC09uXTwBmSFgL/ARwmaaMexv0m8AGy9TZXlPX9PLA98N/ARaXGiLgpIvaOiI8CjwFPpF3HAVNTn3uA9YDNySo5Q3PjDuHdKa5qlkjaKl3jVsDzZfvH4SksMzMrqGZMdmYB+0raEiDdhTWQlad21tTBwMMRMTQihkXEdmRTO0dExGvA4tIC4nTX0hiyhczviIhO4KfAOvk7pdK+t8kSoX0kfSiNs0X6uSnwZeDS1P0ZoBTrQ2TJzl+AacA4SQMlbQ8MB+7v5rqmAcem18cCN5Z2SNoY+Pt8m5mZmRcoN1BELAFOBqZLmgP8BGhLSUbJXEkdaft/qW1Crq1D0pAKw7cB5WuDrgU+l14fA5ye4v4O+F5a21N+jgH8APhWhX3LgPOBU1LTTyU9AvweOCciHk/t3wBOkPQwWdVlQmQWkFV8HgFuAU6MiBUAkq4mWwC9U7rG49JY5wAfl/QE8PH0vuRI4NaIeL3C52FmZtbyGrpmJyIGVWm/kSqViIgYVmW4yT2IN6FC2zSyyggR8QhwQE+OjYhrSQt+I2J02b7zc68r3t6eYu1bZd/ZwNkV2quN9SKpSlRh32R68NmYmVnBNPHdU7XW6AXKZmZm1ggFWqDcssmOpPvI1vrkjY+IeY04HzMzM2uMlk12ImLvRp+DmZlZ0ypQZafpFiibmZmZ1VLLVnbMzMysC+EFymZmZtbKPI1lZmZm1hqc7JiZmRVRZ9R+6wFJYyQ9Jqld0qlV+nxW0iOSFkj6795eqqexzMzMrC7SMyovIvu2/w5glqRp6Yt2S32GA6cB+6YHam/R27hOdszMzIqoMc+y2gtoj4inACRNAcaSPSKp5ATgooh4CSAiyh9uvdo8jWVmZlZEjZnG2oaVH+zdkdrydgR2lPR7SfdKGtPbS3Vlx1ay0YAN6h5zxczb6h4ToPP2mQ2Ju+7Xzm1I3JuuOKMhcf9h+XN1j7l5NOaftgHq15C4632z4rKHPjfw1sl1jzm4/4Z1jwkwsP+AhsRd20iaCEzMNU2KiEn5LhUOK8+S+gPDgdHAEOAuSbtGxMtrel5OdszMzAoo+uDW85TYTOqiSwcwNPd+CLCoQp97I+Jt4GlJj5ElP7PW9Lw8jWVmZmb1MgsYLml7SesC44BpZX1uAA4AkLQ52bTWU70J6sqOmZlZEfXwVvFaiojlkk4CZgD9gMsiYoGks4DZETEt7TtE0iPACuCbEfFib+I62TEzM7O6iYjpwPSytjNyrwP4etpqwsmOmZlZETXm1vOGcLJjZmZWRA2YxmoUL1A2MzOzlubKjpmZWRH5qedmZmZmrcGVHTMzsyIq0JodJztmZmZFVKC7sTyNZWZmZi3NlR0zM7MiKtA0lis7ZmZm1tJc2TEzMyugvnjqebNqWGVH0gpJc3LbMEkTJF1Y1u8OSaPS64XpCaj5/asc00XMhZLmpe0RST+QNLCsz9ck/U3Sxun9FpKelrRlrs/Fkk6VNFpSSDout29kajslvb8md40LJc1J7QMkXZHO5VFJp+XGGCPpMUntkk7NtV+V2udLukzSgNQuSRek/nMlfTh3zC2SXpb0m558RmZmVhCdUfutSTVyGmtZRIzIbQvrFPeAiNgN2At4PzCpbH8b2SPojwSIiOeBc4EfAaREYj/g/NR/HnB07vhxwMOlNxFxdOkagWuB69KuzwAD07nsCXwxJXz9gIuAw4CdgTZJO6djrgI+COwGrA8cn9oPA4anbSLws9z5/BAY39MPx8zMrNUUds1ORLwG/AtwhKTBAJJ2AAYBp5MlPSWTgB0kHQBcCJwUEW+nfc8A60l6nyQBY4Cby+OlfZ8Fri6dArChpP5kictbwKtkSVh7RDwVEW8BU4Cx6ZynRwLcDwxJY40Frky77gU2kbRVOmYm8NfefFZmZtaCXNmpi/Vz0zvXN+IEIuJV4GmyighkCc7VwF3ATpK2SP06gS+RVWYej4g7y4b6NVml5u+AB4E3K4TbH1gSEU/kjnkdWEyWMP0oIpYC2wDP5o7rSG3vSNNX44FbUlO3x3RF0kRJsyXNfmnZ8z09zMzMbK3QyAXKy9LUTl61tLAv00XlXo8DjoyITknXkSUwFwFExBxJ84GLK4wxFbiGbIrparKkp1wpkSrZC1gBbA1sCtwl6bdl51NSfv0XA3dGxF0VrqHaMVVFxCTSdN4u79u7eVNzMzOrnQJ9qWCz3Y31Itkv/rzBwAt9EUzSRsAw4HFJu5NVeG7LZpxYF3iKlOwknWlbSUT8WdLbwMeBkylLdtJU1afI1uaUfA64JU2HPS/p98AosgrN0Fy/IcCi3FjfBd4LfDHXp6OrY8zMzIqs2dbszAL2Ld35lO7CGsjKUzQ1IWkQWYXkhoh4iazycmZEDEvb1sA2krbr4ZBnAN+OiBUV9h0M/DEiOnJtzwAHpjupNgT2Af5I9hkMl7S9pHXJqk3T0jkfDxwKtKWptZJpwDFprH2AVyJicQ/P28zMiqhAa3aaqrITEUsknQxMl7QO8Bqr/mKfK6n0fiowF5gg6Yhcn33KEou829Ni4XWA64Hvp/ZxZHc15V2f2s/twbn/oYvd41h5CguyitHlwHyyaajLI2IugKSTgBlAP+CyiFiQjrkE+BNwT6o+XRcRZwHTgcOBduAN4J9KQSTdRTa9NkhSB3BcRMzo7nrMzKy1RRMnJ7XWsGQnIgZVab8RuLHKvmFVhpvcw5jVjicitq/Q9vWy96PL3t8B3FHhuDPL3k+o0Oc1sjVBlc5lOlkCU95e8c8r3Z11YpV9+1dqNzMzK4qmquyYmZlZnbiys3aTdB/ZWp+88RExrxHnY2ZmZo3TkslOROzd6HMwMzNragV6NlZLJjtmZmbWjQJNYzXbredmZmZmNeXKjpmZWRG5smNmZmbWGlzZMTMzK6DsK9qKwcmOmZlZEXkay8zMzKw1uLJjZmZWRAWq7DjZsZWMX2943WPefdnyuscEGLHTnxsS96YrzmhI3AlzzmpI3D8fdkLdY7azrO4xAfZfb0hD4l5y+BUNibtZ9kDiutqt36Z1jwmwyWa7NCSu1YaTHTMzswIq0lPPvWbHzMzMWporO2ZmZkVUoMqOkx0zM7MiKs5zQD2NZWZmZq3NlR0zM7MC8gJlMzMzsxbhyo6ZmVkRFaiy42THzMysiLxA2czMzKw1uLJjZmZWQF6gbGZmZtYiXNkxMzMrIq/ZMTMzs1YWnVHzrSckjZH0mKR2Sad20e8oSSFpVG+v1cmOmZmZ1YWkfsBFwGHAzkCbpJ0r9NsI+CpwXy3iNjTZkbRC0pzcNkzSBEkXlvW7o5TZSVooafOy/asc00XMQZJ+JulJSQ9JekDSCWnfMEnLys7pmLRvY0lXpuOeTK83rnDcI2nfgFzM01IG+5ikQ1PbepLul/SwpAWSvpfrv72k+yQ9IekaSeum9o9JelDScklHlV3Xsan/E5KOzbWfLelZSa/15PMxM7OC6OyDrXt7Ae0R8VREvAVMAcZW6Pd94Dzgb2t0bWUaXdlZFhEjctvCOsS8FHgJGB4RI4ExwODc/ifLzunK1P4L4KmI2CEidgCeTmOtdBywGzAE+CxAyljHAbukWBenzPZN4MCI2AMYAYyRtE8a61zgxxExPJ3rcan9GWAC8N/5C5I0GPgusDfZX6TvSto07b4ptZmZmTXaNsCzufcdqe0dkkYCQyPiN7UK2uhkp64k7UD2i//0iOgEiIi/RMS53Rz3AWBPskyz5CxgVBrzHRGxArifd//wxgJTIuLNiHgaaAf2ikyp2jIgbSFJwIHAr9O+K4Aj0tgLI2Iuq+bPhwK3RcTSiHgJuI0ssSIi7o2Ixd19NmZmVizRWftN0kRJs3PbxLKwqnQq7+yU1gF+DHyjltfa6GRn/dx00fV1iLcL8HAp0alih7JprP3J5hXnpEQGeCepmZPGfIek9cgqLLekpqpZrKR+kuYAz5MlK/cBmwEvR8Ty8v5d6DZT7kr+L+f9rz3R08PMzGxt1gfTWBExKSJG5bZJZVE7gKG590OARbn3GwG7AndIWgjsA0zr7SLlRic7+WmsI1NbteXcNf/2I0nfSQlN/oMun8a6iywTrRQ/375DSlxeBJ5JFZhSn3IBWcKUpr6GAHtJ2rWr/l1dyhoc827H3F/OvQYN7+lhZmZmq2sWMDytTV2XbJnHtNLOiHglIjaPiGERMQy4F/hkRMzuTdBGJzuVvAhsWtY2GHihBmM/AuyRymRExNkp2XhPN8ctAEaWjoN3Sm17AI+mptKanQ8A+0j6ZGrvLoslIl4G7iCbenoB2ERS/2r9K+g2hpmZWV5fTGN1GzObtTgJmEH2+3NqRCyQdFbu92bNNWOyMwvYV9KWAKl0NZCVp2nWSES0A7OBH6RFwqVpp0qVkfLjHgJOzzWfDjyY9uX7LgZOBU5LTdOAcZIGStoeGA7cL+m9kjZJ57A+cDDwx4gI4HagdLfVscCN3VzaDOAQSZumhcmHpDYzM7OmEhHTI2LHdMPP2antjIiYVqHv6N5WdaAJk52IWAKcDExP00I/AdrK1tnMldSRtv+X2ibk2jokDakS4niydTHtkh4Afgt8O7e/fM3OV1P7ccCO6RbyJ4EdefcuqXI3ABtI2j8iFgBTyapKtwAnpvU+WwG3S5pLluDdllt5/m3g65La07n+AkDSRyR1AJ8B/lPSgvSZLSVbPD0rbWelNiSdl47ZIH0uZ1Y5ZzMzK5LG3HreEA19XEREDKrSfiNVqhlpDq+SyT2M+SrwxSr7FgLrV9n3EvCFLo7bNfc+yKa4Su/PBs4uO2YuMLLKeE9R4XbxiJhFNkVV6ZjLgMsqtH8L+FalY8zMzIrAz8YyMzMroJ6ssWkVLZvsSLqPbK1P3viImNeI8zEzM2smTnZaQETs3ehzMDMzs8Zr2WTHzMzMqitSZafp7sYyMzMzqyVXdszMzIoouvyKuZbiZMfMzKyAPI1lZmZm1iJc2TEzMyug6CzONJYrO2ZmZtbSXNkxMzMroCKt2XGyY2ZmVkDhu7GsqO6Jl+se88S2xvwfrvOVxvz1/4flzzUk7p8PO6Ehcbe8+ef1D7rnKfWPCcxbvrQhcU/epDErEg5b8mLdY17auX3dYwIcv2JRQ+JabTjZMTMzK6AiTWN5gbKZmZm1NFd2zMzMCsi3npuZmZm1CFd2zMzMCiii0WdQP052zMzMCsjTWGZmZmYtwpUdMzOzAnJlx8zMzKxFuLJjZmZWQF6gbGZmZi3N01hmZmZmLcKVHTMzswIq0lPPXdkxMzOzllb3ZEfSCklzctswSRMkXVjW7w5Jo9LrhZI2L9u/yjFdxNxY0pWSnkzblZI2zu3fRdLvJD0u6QlJ/yZJuTh/See6QNKvJW2Q9p0pKSR9IDfW11Jb6dzbJM2TNFfSLaXrSMc+l/scDs+NcZqkdkmPSTo0136ZpOclzS+7vsGSbkvnfpukTcv2fyR97kf15PMyM7PWF52135pVIyo7yyJiRG5bWIeYvwCeiogdImIH4GngUgBJ6wPTgHMiYkdgD+DvgC/njr8mnesuwFvA0bl984BxufdHAY+ksfsDPwUOiIjdgbnASbm+P859DtPTMTun8XYBxgAXS+qX+k9ObeVOBWZGxHBgZnpPGq8fcC4wo9tPyczMCqMzVPOtWbX8NFaquuwJfD/XfBYwStIOwOeA30fErQAR8QZZQnJqhbH6AxsCL+WabwDGpv3vB14B/lI6JG0bpkrRe4BF3ZzyWGBKRLwZEU8D7cBe6dzuBJZWOeaK9PoK4Ijcvq8A1wLPdxPXzMysJTUi2Vk/N3VzfR3i7QzMiYgVpYb0eg5Z9WQX4IH8ARHxJDBI0ntS09GS5gDPAYOBm3LdXwWelbQr0AZckxvnbeBLZNWfRelcfpE79qQ0vXVZbuppG+DZXJ+O1NaV90XE4hRzMbAFgKRtgCMIullZAAAgAElEQVSBS7o6WNJESbMlzV742jPdhDIzs1YQoZpvzarR01hHprZqX21Ui688UpVxSu3V9ufjXxMRI4AtyRKXb5b1m0I29XQE8E4CJ2kAWbIzEtiabBrrtLT7Z8AOwAhgMXB+7ryqncfq+gnw7XyiV0lETIqIURExatigbdcwlJmZWXNqlmmsF4FNy9oGAy/UYOwFwEhJ71xrer0H8GjaPyp/QJqOei0i/ppvj4ggq+p8rCzGTcB44JmIeDXXPiId92Q6dirZeiAiYklErIiITuDnpKkqskrO0NwYQ+h+6muJpK3SuW/Fu1NWo4ApkhaSrSW6WNIRlYcwM7MiiU7VfGtWzZLszAL2lbQlQLqTaSArT+eskYhoBx4CTs81nw48mPZdBewn6eAUe33gAuC8KkPuBzxZFmMZ8G3g7LK+zwE7S3pvev9xsgSrlJSUHAmU7rCaBoyTNFDS9sBw4P5uLnMacGx6fSxwYzqv7SNiWEQMA34NfDkibuhmLDMzs5bSFF8qGBFLJJ0MTE9Vl9eAtlT1KJkrqfR+KtmU0ISySsU+EdFRIcRxwH9IaiebJrontRERyySNTfsvAvoBvwTyt7UfLWk/suSwA5hQ4RqmVGhbJOl7wJ2S3gb+lDv2PEkjyKaoFgJfTMcskDSV7I6u5cCJpWkoSVcDo4HNJXUA342IXwDnAFMlHQc8A3ymwmdgZmb2Dj8bqw9FxKAq7TeSKhIV9g2rMtzkHsZ8CfhCF/vnkSURlfZNrhYnIs6s0j469/oSKiwQjojxXZzP2axaJSIi2qr0fxE4qNp4qc+ErvabmVmxNPO0U601yzSWmZmZWZ9oimmsWpF0H9lan7zxqXJjZmZmSTN/CWCttVSyExF7N/oczMzMrLm0VLJjZmZmPdPMXwJYa052zMzMCqhId2N5gbKZmZm1NFd2zMzMCqhIC5Rd2TEzM7O6kTRG0mOS2iWdWmH/1yU9kh6UPVPSdr2N6WTHzMysgBrx1HNJ/YCLgMOAnYE2STuXdXsIGBURu5M96qja45t6zMmOmZlZAUXUfuuBvYD2iHgqIt4CpgBjVz6vuD0i3khv7yV7IHavONkxMzOzetmGlR/y3ZHaqjkOuLm3Qb1A2czMrID6YoGypInAxFzTpIiYlO9S4bCKNSFJXwBGAX/f2/NysmMr+ee33lP3mN+9obP7Tn3gLTZoSNzNozH/t2tnWUPisucpdQ95+QM/qntMgAd2r/+1Akx7tTF/lyetU/9/L5YyoO4xAW7a7L0Nibu2SYnNpC66dABDc++HAIvKO0k6GPgO8PcR8WZvz8vJjpmZWQE16BuUZwHDJW0PPAeMAz6X7yBpJPCfwJiIeL4WQb1mx8zMzOoiIpYDJwEzgEeBqRGxQNJZkj6Zuv0QGAT8StIcSdN6G9eVHTMzswJq1JcKRsR0YHpZ2xm51wfXOqaTHTMzswIq0KOxPI1lZmZmrc2VHTMzswLys7HMzMzMWoQrO2ZmZgXUoFvPG8LJjpmZWQE15utcG8PTWGZmZtbSXNkxMzMroKj4mKrW5MqOmZmZtTRXdszMzAqos0DfKuhkx8zMrIA6PY1lZmZm1hqc7FQhKSSdn3t/iqQzJX0nPYV1jqQVuddfrTLOmZKey/WbI2kTSaMlvZJr+23umGMkzZe0QNIjkk5J7Z9JbZ2SRuX6ryvpcknzJD0saXRu3x2SHsvF2aJPPjAzM1urBKr51qw8jVXdm8CnJP17RLxQaoyIs4GzASS9FhEjejDWjyPiR/kGSQB3RcQ/lrUfBvwf4JCIWCRpPWB82j0f+BTwn2Xjn5DObbeUzNws6SMRUfoahc9HxOwenKeZmVnLcWWnuuXAJOBrdY57GnBKRCwCiIi/RcTP0+tHI+KxCsfsDMxMfZ4HXgZGVehXkaSJkmZLmj3jjfZeX4CZmTW/zj7YmpWTna5dBHxe0sa9HOdruWmk23Pt++fav5PadgUeWM3xHwbGSuovaXtgT2Bobv/lKca/KZWU8iJiUkSMiohRh27wgdUMbWZm1tw8jdWFiHhV0pXAV4FlvRhqlWmsZJVprDV0GfAhYDbwJ+APZJUpyKawnpO0EXAt2ZTYlTWIaWZma7FmXmNTa67sdO8nwHHAhnWKt4CsMtNjEbE8Ir4WESMiYiywCfBE2vdc+vlX4L+BvWp8vmZmthbyNJa9IyKWAlPJEp56+HfgPElbAkgaWO1OrxJJG0jaML3+OLA8Ih5J01qbp/YBwD+SLXI2MzMrDE9j9cz5wEm9OP5rkr6Qe39EtY4RMV3S+4DfpvU1QTZNhaQjgf8A3gv8j6Q5EXEosAUwQ1In8Bzv3r01MLUPAPoBvwV+3ovrMDOzFtHMlZhac7JTRUQMyr1eAmzQVZ8uxjkTOLPCroXAHVWOuRy4vEL79cD1FdoXAjtVaH+d1ZwSMzMzazVOdszMzAqoSAuUnezUSLp1/DNlzb9KX0JoZmbWVDqLk+s42amV/Dcrm5mZWfNwsmNmZlZAfuq5mZmZWYtwZcfMzKyAotEnUEdOdszMzAqoSN+z42ksMzMza2mu7JiZmRVQp7xA2czMzKwluLJjZmZWQF6gbIW1zwcX1T3mvz66rO4xAdSg75gYoH4Nibv/ekMaEnfe8qV1j/nA7qfUPSbAnnN/1JC4Z4z8ckPibtp/87rH3LGzMf9ebPuvIxoS12rDyY6ZmVkBFeluLCc7ZmZmBVSkZ2N5gbKZmZm1NFd2zMzMCsjPxjIzMzNrEa7smJmZFZBvPTczM7OW5gXKZmZmZi3ClR0zM7MCKtL37LiyY2ZmZi3NlR0zM7MC8gJlMzMza2leoGxmZmbWBySNkfSYpHZJp1bYP1DSNWn/fZKG9Tamkx0zM7MC6uyDrTuS+gEXAYcBOwNtknYu63Yc8FJEfAD4MXDuGl9k4mTHzMzM6mUvoD0inoqIt4ApwNiyPmOBK9LrXwMHSerVpFuPkh1JR0oKSR9M79eRdIGk+ZLmSZolafsujl8o6a6ytjmS5vfm5LuI11/SC5L+vcbjvlal/V8kHZNeT5b0hqSNcvt/mj6/zWt5Pj0l6V8bEdfMzJpXIyo7wDbAs7n3HamtYp+IWA68Amy2WhdXpqeVnTbgbmBcen80sDWwe0TsBhwJvNzNGBtJGgog6UNrcK6r4xDgMeCzvc0GeyIiLomIK3NN7aRMVdI6wAHAc319Hl1wsmNmZn1O0kRJs3PbxPIuFQ4rvzGsJ31WS7fJjqRBwL5kc2ilZGcrYHFEdAJEREdEvNTNUFPJkiTIkqerczH6SfphqhDNlfTFUmxJMyU9mCpIpQRimKRHJf1c0gJJt0paPxerDfgp8AywTy7OQkn/V9I96Q/hw5JmSHpS0r+kPqMl3SnpekmPSLokJSylMc6W9LCkeyW9L7WdKemUXPyrc9c6Gvg9sDw3xtdTVWy+pP+Tu6Y/Sro0tV8l6WBJv5f0hKS9Ur8NJV2WPquHcp/JBEnXSbol9T8vtZ8DrJ8qaVd182dkZmYFEeqDLWJSRIzKbZPKwnYAQ3PvhwCLqvWR1B/YGFjam2vtSWXnCOCWiHgcWCrpw2SJyyfSL9DzJY3swTi/Bj6VXn8CuCm37zjglYj4CPAR4IQ0LfY34MiI+DBZdeT8XKVmOHBRROxCVlX6NEBKeg4CfkOWdLSVncezEfFR4C5gMnAUWUJ0Vq7PXsA3gN2AHXLnvSFwb0TsAdwJnFDlWp8A3itp0xR/SmmHpD2BfwL2TnFPyH1+HyBL0nYHPgh8DtgPOIV3qzPfAX6XPqsDgB9K2jDtG0GWZO0GHC1paEScCiyLiBER8flKJ5vPxH+5qPzvnJmZtaIGTWPNAoZL2l7SumRFlGllfaYBx6bXR5H9zuvbyg4r/7KeArRFRAewE3Aa2fXNlHRQN+MsBV6SNA54FHgjt+8Q4BhJc4D7yObmhpOVsv6vpLnAb8nm8d6Xjnk6Iuak1w8Aw9LrfwRuj4g3gGuBI9Pq75LShzoPuC8i/hoRfwH+JmmTtO/+tHhqBVnCtF9qf4ssiSqPWcl1ZH+Ie5MlViX7AddHxOsR8Vrqt3/umualitkCYGb6A56Xi3UIcGr6rO4A1gO2TftmRsQrEfE34BFguy7O7x35THz81lv35BAzM7PVltbgnATMIMsFpkbEAklnSfpk6vYLYDNJ7cDXgVVuT19dXX6poKTNgAOBXSUF0A8ISd+KiDeBm4GbJS0hqwDN7CbeNWS3nE0oDwV8JSJmlMWfALwX2DMi3pa0kOyXO8Cbua4rgNI0Vhuwb+oLWeJ0AFmylD+us2yMTt79PMozyNL7t3PZ5Qq6/vymAA8CV0REZ27pUFdriMrPJ3+upVgCPh0Rj+UPlLQ3q34m/tJIMzOrqFHPxoqI6cD0srYzcq//BnymljG7q+wcBVwZEdtFxLCIGAo8DXxM0tbwzgLc3YE/9SDe9cB5ZBld3gzgS5IGpDF3TFMzGwPPp0TnALqpVEh6D1nlZNt0vsOAE1l1Kqs7e6US2zpk00J3r+bxRMQzZFNOF5ftuhM4QtIG6RqPZOXKT3dmAF8pTef1cArx7dJna2ZmVjTd/Zd/G3BOWdu1ZGtdlkoamNruBy7sLlhE/JX05UBlN0ldSjZN82D6Jf4XskrRVcBNkmYDc4A/dhPiU2Rze/kKx43Aeblz7Yl7yK57N7Lk5PrVOPYdEfGfFdoelDSZ7DMDuDQiHlLPvyHy+8BPgLnps1pINnXXlUmp/4PV1u2YmVmxFOnZWOrlmp+WI2k0cEpEdJdAtKQlo0fX/S/EgY8uq3dIANTljGLfGbDSErL62X+9IQ2JO295r26iWCP/3rlR9536wJ5zf9SQuP8w8ssNifu5zvp/ddiOnY3592LkuTs2JO76x/x7n/1D9dNtv1Dzf+9Pfua/mvKJW/4GZTMzM2tpNV3AKuk+oHy6aHxEzKtlnL4UEXeQ3eVkZmbWshq1QLkRaprsRMTetRzPzMzMrLd8a7KZmVkBubJjZmZmLa1Ityd5gbKZmZm1NFd2zMzMCqizKW8S7xuu7JiZmVlLc2XHzMysgIq0QNmVHTMzM2tpruyYmZkVUJHuxnKyYyv51BP1L/b94ZBBdY8JNKyuud43T21I3EsOv6IhcU/epP4f9LRXN6h7TIAzGvSMqv956OKGxJ26+xl1j/njdZfXPSbA/FNmNiTuo8f03didBUp3PI1lZmZmLc2VHTMzswLyAmUzMzOzFuHKjpmZWQEVZ8WOkx0zM7NC8jSWmZmZWYtwZcfMzKyA/GwsMzMzsxbhyo6ZmVkBFelLBZ3smJmZFVBxUh1PY5mZmVmLc2XHzMysgHzruZmZmVmLcGXHzMysgLxA2czMzFpacVKdtWgaS9KRkkLSB9P7dSRdIGm+pHmSZknavovjF6Z+8yQ9IukHkgaW9fmapL9J2ji930LS05K2zPW5WNKpkkan8zkut29kajslvb9G0py0LZQ0J7UPkHRFOpdHJZ2WG2OMpMcktUs6Ndd+VWqfL+kySQNSu9Ln0C5prqQP5465RdLLkn6z5p+8mZnZ2m2tSXaANuBuYFx6fzSwNbB7ROwGHAm83M0YB6S+ewHvByZViDErjUVEPA+cC/wIICUS+wHnp/7z0nmUjAMeLr2JiKMjYkREjACuBa5Luz4DDEznsifwRUnDJPUDLgIOA3YG2iTtnI65CvggsBuwPnB8aj8MGJ62icDPcufzQ2B8N5+JmZkVUGcfbM1qrUh2JA0C9gWO491kZytgcUR0AkRER0S81JPxIuI14F+AIyQNTjF2AAYBp5MlPSWTgB0kHQBcCJwUEW+nfc8A60l6nyQBY4CbK5y/gM8CV5dOAdhQUn+yxOUt4FWyJKw9Ip6KiLeAKcDYdM7TIwHuB4akscYCV6Zd9wKbSNoqHTMT+GtPPhMzM7NWtVYkO8ARwC0R8TiwNFVYpgKfSFNE50sauToDRsSrwNNkFRHIEpyrgbuAnSRtkfp1Al8iq8w8HhF3lg31a7JKzd8BDwJvVgi3P7AkIp7IHfM6sJgsYfpRRCwFtgGezR3XkdrekaavxgO3pKZuj+mOpImSZkua/efXF63OoWZmtpbqJGq+Nau1JdlpI6tykH62RUQHsBNwGln1bKakg1Zz3Pxj0MYBU1Jycx1ZAgNARMwB5gMXVxhjaupbSpaqnX9+317ACrJpuO2Bb0h6f9n5vBO+7P3FwJ0RcVeFa6h2TJciYlJEjIqIUVtuuPXqHGpmZtb0mv5uLEmbAQcCu0oKoB8Qkr4VEW+STRvdLGkJWQVoZg/H3QgYBjwuaXeyCs9t2YwT6wJPka2fKak4JRkRf5b0NvBx4GSyCk8+Tn/gU2Rrc0o+R1apeht4XtLvgVFkFZqhuX5DgEW5sb4LvBf4Yq5PR1fHmJmZVdK8dZjaWxsqO0eRrUnZLiKGRcRQsumnj0naGrI7s4DdgT/1ZMC0Buhi4Ia0zqcNODONPywitga2kbRdD8/xDODbEbGiwr6DgT+mSlTJM8CB6U6qDYF9gD+SLY4eLml7SeuSVZumpXM+HjiUrKqVT7qmAceksfYBXomIxT08bzMzK6giLVBu+soOWSJyTlnbtcBksvU7pdvH7ydbQNyV29Ni4XWA64Hvp/ZxZHc15V2f2s/t7gQj4g9d7B7HqtNbFwGXk02NCbg8IuYCSDoJmEFWwbosIhakYy4hS+buSdWn6yLiLGA6cDjQDrwB/FMpiKS7yO7gGiSpAzguImZ0dz1mZmatpOmTnYgYXaHtAuCC1RxnWBf7Vvl+noj4elfnERF3AHdUOO7MsvcTKvR5jdyaoLJ908kSmPL2in9W6e6sE6vs279Su5mZWRRoImttmMYyMzMzW2NNX9lZXZLuAwaWNY+PiHmNOB8zM7Nm1MxrbGqt5ZKdiNi70edgZmbW7Jr5e3FqzdNYZmZm1tJarrJjZmZm3StOXceVHTMzM2txTnbMzMwKqNmejSVpsKTbJD2Rfm5aoc8ISfdIWiBprqSjezK2kx0zM7MCasJvUD4VmBkRw8ke/XRqhT5vAMdExC7AGOAnkjbpbmAnO2ZmZtYMxgJXpNdXkD3vciUR8XhEPJFeLwKeJ3tmZJec7JiZmRVQ9MH/JE2UNDu3TVyNU3pf6dmO6ecWXXWWtBfZg7uf7G5g341lZmZmNRERk4BJ1fZL+i2wZYVd31mdOJK2An4JHFv2cOyKnOyYmZkVUCO+QTkiDq62T9ISSVtFxOKUzDxfpd97gP8BTo+Ie3sS18mOreTj/beqe8wpt6vuMQHWb9CXTAy8dXJD4m6mxnzOhy15se4xJ63znrrHBNi0/+YNiTt19zMaEvezc8+qe8zv79yjm29q7l8H7NSQuAUzDTgWOCf9vLG8g6R1geuBKyPiVz0d2Gt2zMzMCqgv1uz00jnAxyU9AXw8vUfSKEmXpj6fBT4GTJA0J20juhvYlR0zM7MCarYHgUbEi8BBFdpnA8en1/8F/Nfqju3KjpmZmbU0V3bMzMwKqDOK83QsV3bMzMyspbmyY2ZmVkDFqes42TEzMyuk3j64c23iaSwzMzNraa7smJmZFVANvhdnreHKjpmZmbU0V3bMzMwKqNm+VLAvOdkxMzMrIC9QNjMzM2sRruyYmZkVkBcoNzlJR0oKSR9M79eRdIGk+ZLmSZolafsujl+Y+pWemPp3kkZL+k1Zv8mSjkqv75A0Kr0eJukJSYdK2kDSVWm8+ZLuljQo9Rsj6TFJ7ZJOzY17UmoLSZvn2pWuo13SXEkfzu27RdLLFc6x2lifT2PMlfQHSXus6edtZma2NltbKzttwN3AOOBM4Ghga2D3iOiUNAR4vZsxDoiIF0pvJI3uSeA09gzgGxExQ9JpwJKI2C3t3wl4W1I/4CKyx9R3ALMkTYuIR4DfA78B7igb/jBgeNr2Bn6WfgL8ENgA+GLZMdXGehr4+4h4SdJhwKTcWGZmVnBFWqC81lV2UtVkX+A4smQHYCtgcUR0AkRER0S81AfhtwRuBU6PiGm52M+VOkTEYxHxJrAX0B4RT0XEW8AUYGzq81BELKww/ljgysjcC2wiaat0zEzgr+UHVBsrIv6Q+wzuBYasyQWbmZmt7da6ZAc4ArglIh4HlqapnqnAJ9KU1PmSRvZgnNtT//tWI/aVwIUR8atc22XAtyXdI+kHkoan9m2AZ3P9OlJbV9bkmJ44Dri52k5JEyXNljT7gdfaaxDOzMyaXUTUfGtWa2Oy00ZWJSH9bIuIDmAn4DSyytxMSQd1M84BETEiIkpTO9X+lPLtvwXGS9rgnZ0Rc4D3k00zDSabrvoQoG7GqmRNjul6QOkAsmTn29X6RMSkiBgVEaP2HPSB3oQzM7O1RCdR861ZrVVrdiRtBhwI7CopgH5ASPpWmjq6GbhZ0hKyCtDM1Rj+RWDTsrbBwAu59+cBXwB+JWlsRCwHiIjXgOuA6yR1AocDfwCG5o4dAizq5hw61uCYqiTtDlwKHBYRL67pOGZmZmuzta2ycxTZmpbtImJYRAwlW4j7MUlbQ3ZnFrA78KfVHPsJYOtUlUHSdsAewJyyfl8DXgV+ke6e2lfSpumYdYGdU+xZwHBJ26f2ccA0ujYNOCaNuw/wSkQsXs3rIJ3LtmQJ2Pg05WdmZvaOzj7YmtXaluy0AdeXtV0LTAZukjQfmAssBy5cnYFTZegLwOWS5gC/Bo6PiFfK+gVwLNnC5POAHYD/lTQPeAiYDVybqj4nkd259SgwNSIWAEj6qqQOssrNXEmXpuGnA08B7cDPgS+X4kq6C/gVcJCkDkmHdjPWGcBmwMVpbdLs1fk8zMzMWsVaNY0VEaMrtF0AXLCa4wyr0v57YJ/uYqe7qw7J7b6yyjHTyRKY8vaK55wSqROrjLV/lfZqYx0PHF/pGDMzsyJ9qeBaleyYmZlZbTTzguJaa+lkJ91WPrCseXxEzGvE+ZiZmVn9tXSyk7ut3MzMzHKa+Xtxam1tW6BsZmZmtlpaurJjZmZmlTXzreK15mTHzMysgIp0N5ansczMzKylubJjZmZWQEW69dyVHTMzM2tpruyYmZkVkG89NzMzM2sRruyYmZkVUJHW7DjZsZUc1e+V7jvV2Pkqf6LH/2/vvOPlqsq2fd0JRToEpYMBaQpCkAgoonRRQQSkBPVFpbx+WCiiAmJFEQQERJEXAY0NDChFlA4ioJTQCQHpHVEQkV5yf3+sNWQymTkn5Mza+5yZ58pvfpm99sy+18zsM/PsZz2lGm568R+16I6ZY75adN8+epFadE+ctnzlmk8yZ+WaACtPe74W3aPmeqUW3YPftmPlmrfc9tvKNQHu3WDPWnRLEqnnQRAEQRAEPUJ4doIgCIKgD5kWAcpBEARBEAS9QXh2giAIgqAP6R+/Thg7QRAEQdCX9FM2VixjBUEQBEHQ04RnJwiCIAj6kPDsBEEQBEEQ9Ajh2QmCIAiCPqSfemOFsRMEQRAEfUgsYwVBEARBEPQIYewEQRAEQR/iAv+GgqQxki6UdGf+v2NDP0kLSnpY0o9m5dhh7ARBEARBMBzYH7jY9krAxXm7EwcDl83qgcPYCYIgCII+xHbXb0Nka2Bivj8R+Ei7B0laG1gcuGBWDxzGThAEQRAEXUHSHpImN932eB1PX9z2owD5/8XaHH8UcCTwpdczrxFn7EhaQtKpku6WdJukSyU9J+lGSU9Kujffv6jD88dKej4/5jZJv5A0Z8tjjslrgaNaxj+QP7ypkm6XdEQeX0XSn/Mxp0o6IY/PKWmipFvy+AF5fNk876mSpkjaq0mj7ZqlpFUl/U3Si5L2a5nXFpLukHSXpJncfpKOlfTM7L3jQRAEQS8yDXf9ZvsE2+Obbic0a0q6SNKtbW5bz+K09wT+ZPvB1/NaR1TquSQBZwATbe+Ux8YBC9i+XNLPgXNsnz7Ioe62PU7SaOBCYAfg1/l4o4BtgAeB9wJ/zuOrAz8CPmT7dklzAA2L9YfAUbbPyo99ex7fHpjb9tslzQvcJukU4EXgi7avl7QAcJ2kC23fxvQ1y0Oz4bI/8BXgSeALtLj18mv4MbAZ8BBwraSz87GQNB5YeJbe4CAIgqBvqKPOju1NO+2T9A9JS9p+VNKSwONtHvYuYANJewLzA3NJesb2QPE9I86zsxHwsu3jGwO2b7R9+ewczParwDXA0i0atwI/ASY0jX8Z+K7t2/NzX7F9XN63JMnQaBz3lsZdYL5sGM0DvAQ8bftR29fnx/4XmNo0h7ZrlrYft30t8HLLy1gHuMv2PbZfAk7Nx2gYQofnuXek2e142tMPDPTQIAiCICjF2cAu+f4uwFmtD7D9MdvL2R4L7Af8YjBDB0aesbM6cF23DibpDcC6wHlNwxOAU0gepC2blrgG0j4KuETSuZL2kdTwpJwOPAs8CjwAHGH7yZY5jAXWAq7OQ4OuWbawNMkL1eAhphtOnwPObhyvE81ux+0XXG4QuSAIgqAXKLGMNUQOBTaTdCdpteJQSCsUkk4cyoFHmrHTLd4i6UbgCeAB2zcDSJoL+CBwpu2nSQbI5oMdzPbPgLcCpwEbAldJmpvkdXkVWApYHviipBUaz5M0P/A7YO+sNzuo3ZQkLUVaRjt2No8bBEEQBJVh+wnbm9heKf//ZB6fbHu3No//ue3PzcqxR5qxMwVYuwvHudv2OGBFYD1JH87jWwALAbdIug94D9OXsgbUtv2I7ZNtbw28QvIE7QycZ/tl248DVwLjIQUvkwydX9v+fdOh/pHXKhlgzbKZh4Blm7aXAR4heYtWBO7Kr2VeSXcNcqwgCIKgTxhuRQVLMtKMnUuAuSXt3hiQ9E5J75udg+Xlnf2BA/LQBGA322PzeuDywOY5uPhw4EBJK2fdUZL2zfe3aCx3SVoCWBR4mLR0tbES8wHrAbfnQOuTgKm2f9AyrUHXLFu4FlhJ0vLZM7UTaenqj7aXaHotz9le8XW/SVgktkAAACAASURBVEEQBEFPMs3u+m24MqKMHafQ8W1Ia3p3S5oCfJPkyZhdziR5Pd4HvB/4Y5Pes8AVwFZ5qWtv4BRJU0lBzEvmh24O3CrpJuB84Eu2HyNlSc2fH3st8LN8nPWBT5AMoRvz7YP5WJ3WLJeQ9BCwL3CQpIckLWj7FVJszvmkQOdJtqcM4f0IgiAIgp5iRKWeQ1ouIqWKt9v3yVl4/n2kJabGtoE18+aYNo/ftun+OcA5bR6zL8kIaR1/hhQ30zp+Be1jbbD9BLBJm/HHSEtU7Z7zJ+BP7fY1PWb+gfYHQRAE/cVwXnbqNiPKsxMEQRAEQfB6GXGenVklF/b7Zcvwi7bXrWM+QRAEQTCcGM4xNt2mZ42dXNhvXN3zCIIgCILhSCxjBUEQBEEQ9Ag969kJgiAIgqAz/bSMFZ6dIAiCIAh6mvDsBEEQBEEfEjE7QRAEQRAEPUJ4doIgCIKgD+mnmJ0wdoIgCIKgD4llrCAIgiAIgh4hPDvBDIzdfq7KNX999NWVawKMUj22/txzzFmL7sKLrlaL7m6vDqVP7+zxh0XfVLkmwHIH1lPH9Nb9Lq5F98A5V6lc894N9qxcE2D5y4+rRbck9rS6p1AZ4dkJgiAIgqCnCc9OEARBEPQh0/ooZieMnSAIgiDoQ9xH2VixjBUEQRAEQU8Tnp0gCIIg6EP6aRkrPDtBEARBEPQ04dkJgiAIgj6kn2J2wtgJgiAIgj6kn9pFxDJWEARBEAQ9TXh2giAIgqAPid5YQRAEQRAEPUJ4doIgCIKgD+mnAOXw7ARBEARB0NOEZycIgiAI+pAoKhhUgqQlJJ0q6W5Jt0n6k6SVJd3a4fFzSPqXpO+1jG8p6QZJN+Xj/G8eX0XSnyXdKGmqpBOqeF1BEATB8Md212/DlfDs1IQkAWcAE23vlMfGAYsP8LTNgTuAHSQdaNuS5gROANax/ZCkuYGx+fE/BI6yfVY+/tvLvJogCIIgGL6EZ6c+NgJetn18Y8D2jcCDAzxnAnAM8ACwXh5bgGS0PpGP8aLtO/K+JYGHmo5/S9dmHwRBEIxoptldvw1Xwtipj9WB62b1wZLmATYBzgFOIRk+2H4SOBu4X9Ipkj4mqfG5HgVcIulcSftIWrirryAIgiAIRgBh7IwctgQutf0c8DtgG0mjAWzvRjKErgH2A07O4z8D3gqcBmwIXJWXuWZA0h6SJkuafPKN91bxWoIgCIKa6aeYnTB26mMKsPbrePwEYFNJ95E8QouSlsKAtERl+yhgM2C7pvFHbJ9se2vgFZJHaQZsn2B7vO3xnx63/Gy9mCAIgmBkMQ13/TZcCWOnPi4B5pa0e2NA0juBN7c+UNKCwHuA5WyPtT0W+CwwQdL8kjZsevg44P78vC1yADOSliAZSA+XeTlBEARBMDyJbKyayJlU2wBHS9ofeAG4D9gbWEXSQ00PPwa4xPaLTWNnAd8H9gW+LOn/gOeBZ4FP5sdsDhwj6YW8/SXbjxV6SUEQBMEIYjgvO3WbMHZqxPYjwA5tds05C899EnhT3vxgh8fsSzKGgiAIgqBvCWMnCIIgCPqQ4Zwq3m3C2AmCIAiCPsTDOKC420SAchAEQRAEPU14doIgCIKgD+mnZazw7ARBEARB0NOEZycIgiAI+pB+Sj0Pz04QBEEQBLUjaYykCyXdmf9fpMPjlpN0gaSpkm6TNHawY4exEwRBEAR9iAv8GyL7AxfbXgm4OG+34xfA4bbfCqwDPD7YgWMZKwiCIAj6kGG4jLU1qWk1wETgz8BXmh8g6W3AHLYvBLD9zKwcODw7QRAEQRB0BUl7SJrcdNvjdTx9cduPAuT/F2vzmJWBpyT9XtINkg6XNHqwA4dnJwiCIAj6kBKeHdsnACd02i/pImCJNru+OosScwAbAGsBDwC/JfWDPGmwJwVBEARBEBTH9qad9kn6h6QlbT8qaUnax+I8BNxg+578nDOB9RjE2IllrCAIgiDoQ1zgNkTOBnbJ93cBzmrzmGuBRSQ1GmFvDNw22IE1DAOUghGKpD2yCzN0e0gzdHtXM3R7V3MkImlRYBKwHGmJanvbT0oaD3zG9m75cZsBRwICrgP2sP3SgMcOYyfoFpIm2x4fur2lGbq9qxm6vasZzEgsYwVBEARB0NOEsRMEQRAEQU8Txk7QTepak+4n3X56rf2m20+vtd90I16nZiJmJwiCIAiCniY8O0EQBEEQ9DRh7ARBEARB0NOEsRMEQRAEQU8Txk4wZJT4uKSv5+3lJK1TWPMQSQs3bS8i6TsF9RYe/FHVkgtr9TSSPlz3HEoi6ZezMlbRXHr+fKoDSXNKWktSu6aWQUWEsRN0g+OAdwET8vZ/gR8X1vyA7acaG7b/DXywoN6/JF0kaddhZPgM2AumFA2jtsBxt225bQec0NguoZl139iy/XFJP8zdm1VKN7Nai/ZoYO3Cmp0ocj5JerukqyQ9KOkESYs07bumhOYg8/l74eMfL2m1fH8h4CbgF8ANkiYM+OSgGNEINOgG69p+h6QbIBkekuYqrDla0ty2XwSQNA8wd0G9qcDRJIPu+5KuAE4BzrL9fClRSWd32gUsWkp3EHYDvl3guJOA80jN/xpGxnzAVqS2O78voAlwAfAOAEkHkToq/wbYEngrsE+3BSUdABwIzCPp6cYw8BIF05RrOp9+AnwTuIp07lwh6cO27wbmLKQJgKT/Mr1lU+OcmrcxbnvBArIb2P5Mvv8p4O+2PyJpCeBc0vdGUDFh7ATd4OV8RWqA3KBtWmHNXwEXS/pZ1v00MLGg3su2zwHOyYbVVsBOwI8lnW9750K6GwAfB55pGRdQbKmw6Qd4pl3APIVk3wUcSmr0d7xtS9rQ9qcK6TVo9t5sS/qxelbSb4DrSwja/h7wPUnfs31ACY0O1HE+zW/7vHz/CEnXAedJ+gRd6R05ID8HFgK+ZPsfAJLutb18Qc3mHk2bAacB2H6svKMw6EQYO0E3+CFwBrCYpO8CHwUOKilo+/uSbgY2JX1RH2z7/IKSr31LZU/OJGBSdlN/pKDuVcBzti+baULSHQV1nwLe2fiBaNF9sISg7Wtz3MjngUskfYXyP4aQvCtrkZb1R9t+Ns/nZUmvlhS2fYCkpYE30/R9bPsvhSTrOJ8kaSHb/wGwfWleovwdMKaQJlnr85LWBk6RdCbwI8qfU09J2hJ4GFgf2BVA0hyUu1AIBiGKCgZdQdKqwCYko+Bi21MLao0Gzre9aSmNNpr72T6iKr26ycHeZ9ueKaZC0mG2v1JYf2ngKGC87RUKa13aMrSz7UdzB+bzSzZwlHQoyUN4G9AwrGy7ZwKzJe0M3GP7qpbx5YCv2d69gjmMAj4HbA+8xfZSBbVWJl0ALgEcbfvnefz9wOa2v1hKO+hMGDvBkJHU7ursv7ZfLqh5NvCJxtViryNpcWBp0lXpI+08LkF3yUb13LafK6hxB7BGI/asKvr1fJK0JLCW7T/VPZegWmIZK+gG1wPLAv8meXYWBh6V9Diwu+3rCmi+ANwi6ULg2cag7S8U0GpkVRxAWrJ6Ux5+HDgLOLQ5M6zLumuRAjwXIrnFAZaR9BSwp+0iMSVZeyFgC5p+FEmejlKvtZb3uEl/POk8fgW40/btQDFDJ3MPKUi3EmOnzvOpw3xOsL1HYY1Vga1pOo9z3E4R77Ok3YE/274zZ/OdDGwH3AfsYvuGErrBwISxE3SD84AzGjEzkjYn/UhOIqWlr1tA84/5VhWTgEuADW0/BpCzK3YhBSCWqlHyM+B/bV/dPChpvbxvzRKikv4H+AYpU6nxo7gRcIikb9n+RQHZWt5jSe8DjiTFKa0NXAksIullkvewSIxS5jngRkkX02TwlDLaqeF86uD5hXRhVLJcBDnuawJwKtBYkl2GFMNzqu1DC8juRQqMJmuvASwPrEVa3tqggGYwCLGMFQwZSZNb4xoaY5JutD2ukO48wHK2SwbqNrTusL3K693XBd07ba/UYd9dtlcspHsHqaTAUy3jiwBX2165hGZN7/ENpFiKf0paHviB7W1ysPSXbG9eQjdr79Ju3HaRzMI6zqcc5H0/M2a9OW8vbbtYmYpcU2e11iX1XBpjSqf3Yoiar33n5Yy+q20fk7evt/2ObmsGgxOenaAbPJmvoE7N2zsC/84xD0VS0CVtBRwBzAUsL2kc8O2CgZ33S/oyMLEphXVx4JNAySv/cyX9kVSUrKGzLPA/JI9aKUT7rJVpzPij1U3qeo9H2/5nvv8AKTMK2xdKOrqgLrYnVmm0U8/5dA+wie0HWneUyuxrYhqwFMnYamZJypXHmJZjg/5NStr4btO+yMaqiTB2gm6wM2nJ48y8fUUeGw3sUEjzm6S6IH8GsH1jviovxY7A/sBlml72/R/A2ZR7jdj+gqQPMD3mQMBDwI8LB1l+F7he0gVM/1FcjrSUdHAhzVreY2CypJOAi0nv858BJM1LOoeLUbXRXtP5dDSwCMmQbOX7hTQb7E2qx3UnM57HK5Kys0rwdWAy6dw52/YUeG259J5CmsEgxDJWMCSy9+ZQ21+qWPdq2+tKusH2WnnsZttrVDmPXiYvWb2fGX8Uz3dqzdEzSJoT2B14G6m0/8m2X80el8Vst3oFuql9HbAxKaC1cR7fYvvtpTT7jZx2vg4znsfX2i5WQynX1Fmg+W+lYTzb/m8p3aAz4dkJhkT+Uaijl8+tuX7HaEkrAV8A/lrDPJD0Kds/K3Ts0aQS+8sA59r+a9O+g2wXa36av6hPHfSBFVDyPc7xHMe1GX+emZc/us0rtv+jGSvrFrsCrfN86jCfzWxfWFLD9jRSMcVW7fltt1aS7pbmK6RlLHJG1kYkb/dWwOIlNIOBiUagQTe4QdLZkj6hpkaOhTU/T2qi+CKpj9F/SC7rOvhWwWP/H/A+4AngWEk/aNpX+j1ui6RbapAt+R53RNK5hSVmMNolHUtZo324nU+1NLPN3Fby4JLWlXQMyWA+G7gcWLWkZtCZWMYKhoxSf6pWbPvThfTeRAoivat07ZUmzZs77QJWtl2kCWnz0lx2jR8HvJGU0npVY+mjgG6nHz6R+la9qcP+oWjW9R53yo4RcI7tJUvoZu15ga8Cm2e980mtT14opFf5+aSBm49ubHu+bms2ae87gPZXbXe9XYVSy5wdSDFKp5Ba6Ux22X5cwSCEsROMKCTtBhwC3E2qXbGH7U5fpt3U/QcpfqU1XkXAX0uVn5d0u+1VW8a+nueyWInU2azxMvBr2i+pfNT2AgU063qPXwUuo32W2Xq2eyaDpo7zSdK/6dx89Le2iy3rSHoBOJxUKLKVfWwvXEDzn8AdpMDsc2y/IOkeF257EgxMxOwEQ0bSG0jN7lYD3tAYL+TZ2ZtUN+OfklYg/SAXN3aAc0jdm29s3SHpzwV1J0vawtO7RmP725IeIVXCLcXNwBG2b23dIalUT7K63uOppEJ7d7bRLZoarVS1+UBgLDM2Ai0VaF/H+VRXM1tI1d3PdJsq7vnCqQRLkDx1E4CjlXqvzSNpjhzLE9RAeHaCISPpNOB2UgDet4GPAVNt71VAa4aiXFGkqwySNgDu71AbZbztyTVMqwiSPgrc0q7OjaSP2D6zzdO6pX0H8CXgFprqvpTMAOsnJK0CPNlUR6l53+Iu3BMsXwhuSTJ83kNqkrxzSc2gPWHsBEOmkf7diAfIqbzn2964gNbjzJghtFPztsuV2W9kVTRSWBu9oq5x4T8itentQ6rfUayzfF3U9R7XhaQrbL+nYs1azif1afPRBpIWJPUKPLLuufQjYewEQ0bSNbbXkfQXYE/gMdIPVNfXqNWhvH6DgmX2NycFc95JUwNFUnGyPW1fUEi3ubfPQ026OwGlevs0gld3BbYhVaBt/CieBZzUWn6/S5p1vcf7Av+xfVLL+OdJdVGKVVGWtAnp823tjfX7QnqVn0/q0HyU1Iusima2tTWXbTOfB2wvV6VmkAhjJxgyee37d6SGdz8D5ge+bvv4WifWRSRNBT5g+76W8eWBP9l+ayHdynv75OOfQvoxmsiMP4q7AGNs71hAs673+FbgHbZfahmfm1R8rlihSkm/IqUjT2H6MlbJTMZaekXRufno/9ku0sw2a5xPai470TM3l93UdqkGvp3m86DtZavUDBIRoBwMGdsn5ruXAUUzDiT9gQGKrrlcb6w5mP6j38zDwJyFNKGe3j6QfvxbG28+BFyVfzBLUNd77FZDJw++qJZqfwVY09VWS67jfJqv1dABsH2VpGJp55mxtg9r0X0MOExSEYNyEMK7UBNh7ARDJl8Bb8fMGSXfLiB3RIFjzgonA9dKOpUZe+zsSNnCaHX09oHUyHV74He5Am2j7P72zJwa3i3avcfLkpZYihafaxesmmNMSnOVpLfZLlrgrok6zqe6mtlCDc1lc9HNdkaNiOrJtRHLWMGQkXQeqYLxdcBr/WZKB+Jl1/vKefOOEnEkLXpvAz7MjD12zi79Q6V6evuMBQ4j9W36d9ZdmLQksL/tewvpvpWZm1QWfY8l/Q+p3cgXSanKAGuTmlT+uFQcWNaeCrwFuJcUsyOSp6nk0lkd51O75qNnu2wz20Z/t/2zdmtz2cNsP1lAcyWSUdNqTL2ZFJh9V7c1g8EJYycYMpJutb16xZobkuJJ7iN9eS4L7GL7LxXO4Y22/1WBznLA07afykbIeFJq/5TS2ll/UdJ3RfHXWhf5x3h/YHXSVfkUUgBr0XYRkt7cbrxk6nnd51OvI+kc4EDbN7eMjwe+YXurembW30RvrKAb/FVS1V2ajwQ2t/0+2+8lVYA9qpSYpA9IulfSFZLWkjQFuFrSQzmjppTu/qRYqKtyIPh5wAeASepcCr9b2utIeqftJ4DFJO2bjYJSels03V9I0omSbpb0m9JLSrbPzefSorbfmO+X7ovVMGoWJjWI3ApYuLChU9v51GE+J1SgsaqkTVrjg5rPty4zttXQAci1qcYW0gwGITw7wWzTtDY9B7AScA/VueJvbj1+u7Eu6t1IStldmFTp90M5wPKtwK9LFTbMRtV4YF6SF2sFp+rR8wFXl/KoSfoG6UdwDuBCYF3gz8CmpBpK3y2g+VqBSEknkkoY/JTUoPJ9tj/Sbc2sdYHtzfP9A2x/r4ROB+29gN2BRqr5NsAJto8tpFf5+SSpU/8pATfZXqbbmk3aXwA+S6qSPQ7Yy/ZZeV+RgqSS7rK94uvdF5QlApSDobBljdqTJZ0E/DJvf4wUM1SKaY2ia5Kes30VgO2pOQaiFK/afl7SS8DzpG7V2H62cKLQR0k/DnOTjI5lbD8t6XDgaqDrxk4L422Py/eP0iD1lYZIc1PT7YHKjB1SLaN1bT8LIOkw4G9AEWOHes6nf5Kyv5oFnLcXa/uM7rE7sLbtZ/KS3emSxto+pmU+3eRaSbvb/mnzoKRdKfsdFQxAGDvBUFgMeGOru1/SVqQCdCVL3v8/0hXbF0hfWn8hFaQrxVOS/hdYkJSptA8wieTpaG1w2E2ul/QbYD5S4bmJOSB8Y6BkYPQrOWD1OUl3234aIP9QlkpRXiwvpQhYUJI83fVc0qCs070tmoL68/2SVmwd59M9wCZu33qkaO8xUlHIZwBs35dj/U7PsVKl3ue9gTMkNV+AjQfmInnughoIYycYCoeTUjhbmQqcQPoCLYLtF4Ef5FsV7AIcRPphbDT5O59k0O1eUHc3krfBwOmkLJqdSV2Vf1xQ9yVJ89p+jpSZBLxWkbaUsfNToNFNfSLwRuCfSkXgZmoO2kVWkHQ26cevcf81CtZuglSE82pJZ+Ttj1A2zb6O8+loYBFgJmOHlPFWksckjXNuLps9PFuSyhwUiTPMKe7vlrQRKeAd4I+2LymhF8waEbMTzDaSbulUEE3STSUqo0qaKfCvmZJxQv2EpLmzQdk6/kZgSdu31DCtIkh630D73aZbd5f130FqEingL7ZvKKnXT0hahuSlfKzNvvVtX1nDtIIaCM9OMBTmGWBfqcqo00hXpb8B/kCKO6iEfKW2HSnN/RVSD6ef2r67oOb1pODVU0rqtNJUPbhdU84iKeg5kPVzWeck4EDgXSRP4SG2SxUzvKGxTNdmTkX6GLUE7d6Xb6/tK1H/JR+7lvNJNTUftf2QpOUkLdySan97GDr9RaSeB0PhIknfVUtko6RvkYrPdZ0ctDqB1H/rN6RA2dWAhwun7B5Kqvh6FfAyKQ7hbtL6//aldEnu/4WBSyVdI2kfSUsV1ANoNOW8E/gm8EHgQ8C3gDvzvhL8imQkrw1cCixBKmz4PPDzQpqQsswAkHRxy74zC2n+i7Q0Nznfrmu6TS6kCTWcT0rNR08lea6uAa7N90/JqfAltTul2v+2jlT7oD5iGSuYbXK66omkq/9GTMWapC/r3RqBgYXnsCMp1uAw24cX1HltyU6pI/hlttdXqtB6ecEU8OZ07A1Iht62JG/HKbaL1ClRDU05Jd1oe1w2nh+yvXTrvm5r5mPfYHut1vvttruoeQywIXAlcApwhSv4Mq7jfFJNzWyzRi2lG4LhR3h2gtnG9rO2JwCbka68f04q9LdTs6EjabVu6kpaWtIXJV0BfBzYB/hJNzXaMK1p6WEpYDRAXlop3SySrHW57T1JSwGHkZZ4SlFHU85R2XhcFpg/Lzk0KjjPVUgTZszGajU4ihggtvcipfafBnwCuEHS97MxWQkVnk+N5qOtlG5mCznVHniKllT7wrrBMCNidoIhY/se0rJOJ34JdKV4l6TLSBk7k0iZYI3YhrlKxjoAh5B+kO4AViWlviPpTcBNhTQBZuownlPCz6NsE8U6mnJ+D7g93/80cKIkA28jLaGVojnlvXGfvP2mzk8bGtmTc6mkG0jv68HkOLBSmtRzPtXVzBY6p9pvQtnSDcEwI5axguJ0cylA0n1Mv9puPnkbVZtX6IZOB+0xwArAXbafKqUzXFA9TTlHk76XXsnLheNI8ViPFtT8xkD7bXfd0MrLKFsDO5IMqt8Dv7Vduu5MLaiG5qNZdw5mTLVfl7R09wCpyWt4ePqEMHaC4qhQWfbhgKRVgP1sF6m1M1gQpe2q6gy9RqmU3ZyC3RHb1w+0vzTqYisJSc+SvDinAHfRslxm+/ftntcF3crPJ0nzAi83Ynby38wHgftsnzHgk7s/lzlJtW8etv14ldpBvcQyVjAikbQNcInt/+TthYENbRfJnpG0BnAEKfbgTFI5/+NIV4pHltDMHEEK/j6X6X3HipM9LDuQrsTPs31rLsZ2IKnkQNeDdkmB7VNI7QVg5vYCxYpUziLdbCVxGuk1rZpvzZjpvbK6TR3n03mkthh3SlqR1A7j18CWktaxfUApYUnHA8fanpILYv6NVKV6jKT9bJ9SSjsYXoRnJyiCpKVsP5LvX2V7vS4ff6bsnFKZM/nYV5OCoP8GbAF8mZT6/jXbL5TQzLrjSPEcW5DSkk8BLi6duSPp56QYnWtIBt39pADW/QsalPuQ6hj9h5SqfEYVGX2zSsnzqyrqOJ9aMhkPBsbY/mzOxrquU2HSLmlPsb1avr836YLoI0pVuc8d6Z9nMOuEsRMUQdIDtosUZMvHb9f1vGNF5y7ozWBcKfX0GVs65qBlDu8mxRtsCnzF9tmDPGUoWrcCa9ieJukNpLowK7arRFtAe3nS69yaZGQd0ij3XyfdXI6V9HHbv+q0rFTF8mRV51Pz36qkK4HDGwazClVab9JuLivwR+A02z9v3Rf0PrGMFZSitHt8sqQfkGrsGPg8ZTsKv0HSWkx/Xc8Aa+SaMMXjSXLW11qkfj4PAaXjDV6yPQ3A9guS/l6FoZP17pV0Fmm57BPAypTtjTWrdPOcblQYX2DARxWi4vPpZklHkMoWrAhckOewcEHNBk/l5deHgfVJy2mNwOWBKsAHPUZ4doIiVODZmQ/4GumqVKQv0O+Uyq6QdOkAu227SDyJpE+RMnbeQMommVRFYKWk50iBs5De37fk7UbWW9d7kElagbTEsjUpRflU4JySy4RZ93O2fzQLjzvQ9iEl51KaOs4nSfMAe5Hq6pxs+6Y8/m7gLbZ/WVB7ZeCHpGrcRzd5dd5Pqgn2xVLawfAijJ1gtpF0LO2LrgnYxfaCFU+p55A0DbiF6R2jW7N2inTklvTmgfa7QGuO/FpvBs4Cnmbm11pkaafObMHsYdkdGEuTp932pwvpVX4+SdrM9oUd9h1m+yvd1gyCVmIZKxgKA/XwKdLfR9LRtveW9AfaGFqlfvwHmM9mwJdtb1ZIYqNCxx2QhjGT42dWI73XU3MByVJ8m+mf6fwFdYYTZwGXAxeRsoRKU8f59GNJ+9j+Y2Mg1905meRxKYak7wP32D6+ZXwfYIkwtPqH8OwERZB0hO39Chx3bdvXSXpfu/22L+u2ZtbdGDie6annhwC/IHmxvluwLsqXgSOrDITOuguS+p6NJ8XLiNT37DpgV3foEj4SkfQK8Fy7XaQlu2IeynZZhSWp43xSavtxHnCg7d/ngPfTSd67XdzSM6vL2rcBqzfiz5rGRwE3O3pj9Q3RGysoxQ4lDmr7uvz/Ze1uJTQzRwJ7AIuSvqivAn5pe+1Shk7mzcB1ktYvqNGOH5LK6a9oe1vb25Didm4BBo1vmR0kvUHSLpI+rMSXJZ0j6RhJbyyhmbnF9oJtbgtUsBR7jqQPFtZopvLzyamZ7KbAwZI+Q2rb8HfbO5c0dKbLe6b+W3mskppVwfAgPDtBESQ9aHvZAse9hQGaM5YInM26M8R1SLrb9ltKaLXRfgepiOHtpFo/r315l8oCk3SnO3SjHmjfEDUnAS+TMpUWAW4F/gC8Bxhne8tua2bdylOQJf2XdB6L9HpfIr12KO9NqvR80vTK2EuSvKEXAt8vqdmkfS2ws+07W8ZXInV5H19KOxheRMxOMNtoehfwmXZR7qqp8YP32fx/I5PjY7RfiugWC0vatmlbzdslvTu2r5f0EFP/FAAAETdJREFUVeB3JO9Kc2+wUlWF67jqfZvt1XNa8EO2G0uV50kq2Wz1tILHbovtWlLOs3bV51NzhfGbgcWbxkpXxv46cK6k7zC9NMV44ABSg9KgTwjPTjDbSLqX6Venrdhlm3JeaXv9wca6qPezAXa7YPbMYqQfhhWAPRtpu6WRNBG4GzjYTV8Skr4GrGz7EwU0X/OetfGkFcuYUmoE2umL0LYPLqHbpL8tyXtl4HIXqlCdtWo5n+pE0urAl0g9sSC1JDnc9i31zSqomjB2ghGJpBuBz9m+Im+/GziuymDPKpB0D3Ao8FNX+MeaA5RPAt5BClA2qQjdDcBuLtD1XdLjpNo6ItWCObWxC9jB9uLd1sy67WqtzAvsBixqu1hmmKTjSIX2Gj2adgTutv3Zzs8akl7l51OLRxTSufQv4Ebb/61iDq3kIOmtbFfu1QvqIYydYLbRzF2qDfzL9oMVaK9NSl1dKA89BXy6YAxLa1n/xhf2FbbvLaGZdd9k+59txpcFdrJ9eCntrPMW4G0kg2OK7bsLau0y0H7bE0tpN81hAVIBvF2BSaTMpWJF9yRNIWULOW+PIgVMr1ZIr/LzqYNXdAywBimz75Jua3aYx2hgc1KLjPeTvGgfrUI7qJ+I2QmGQrtu32OUGvxNcMF+Rjkra83sgZBz9/OCtIuxGAt8VdI3bZ/aZv+Qaf5hyhlJ25O+rJcGziihmbU+bvtXtu+WtITtK5v2zVLF4ddLszEjaf40VKYidis5/mxfUuzXROAdtv9dgfQdwHKkHmCQmq/eXEqsjvPJ9qfajefClZNIjWaLIem9wM7Ah0iNbdcHlrddMsYvGGaEZyfoOpLGAz+w/d4Cx669gWLLfMYAFxWMJ1kA2Ib0Zb0y6QdpR9vLlNBr0q0rfub/kYJHG72jngEOs31cCb2seTiwLXAC8GNX2Gld0mXAO0k/wuT7fyMH27vLRTLrOp8GmE/R6tWSHiJVi/4JcKbt/0q61/bypTSD4Ul4doKuY3tyvjIvQa0NFFux/aSkkplLj5N+CA8iLZlZ0jYF9Rqow/12290RlA4C3g1s6FypWalf1jGSxtj+Tgld4IvAi6T3+KtNH2fxooKkbKEqqet8mglJq5De95L8DvgIKRbqVaUGs3GF34eEZyfoOpIWB/5ke+2651KaXFn5IJdrBLoPqTnmfMBvgN8CF5bMdMu6lXt2JN0BrOmWxp9KjSRvsr1ytzWHC3k5trk31pOFdCo/n9S+tcsYUt2dj9v+WyntrC9Sm4wJwAeBBUkxWX+q0osX1EsYO8Fso/aNQMeQrs73sv2HgtoTs8ZTeXsRUjBpqRTwdsUMxwCPkEreTy2h26S/AunLeidgJeAbwBm2/15Ir9H1vLnjOXl7BdvzdXruEDTvsL1Kh323216125p1I2kP4GDgeVJxv4Y3qbQxW9n5pJlbuxh4ArjT9kvd1htkLnMCW5Be++a2S1bmDoYRYewEs02b7JnGl9i1JTNYsvZMVW9LVsLVzF3ADTxRVQBty1zeToq52MGFqji3eb0z4DJdzy8GDrF9ccv4xsDXbNfSFLUkku4E3mX7XzXOoej5JGk921d1+7hDRdJ7bf+l7nkE1RDGTjDbSFrO9gM1ad9Eiu34d94eA1xm++0VzmE+UjzAzrY/VFhrYdIVOKS+QqWzzypH0mqkLuBXkKrdmhSwuz6wte0pNU6vCJLOA7atOjOoyvOpZUn0b7bfVUqrjfZoUp++pYHzbN8qaUvgQGCeUhdHwfAjApSDoXAmqegckn5ne7sKtY8E/irpdNKP4g6kTuRFyWn1HyRdCW9BCoA8vrDeCSSj6l7SMsebJZ0BfKbUMoCkXYExjborkh4mBYUL+LLtn3Rb0/aUXO12Z2C1rPUX4H9b43h6iANI5/HVNAXr2v5CCbGazqfmgPY3FDj+QJxESue/BvihpPuBdwH7u2Cl6mD4EcZOMBSav8SKxhi0YvsXkiaT+uqIdHV8Wyk9SZsxvRjZpaSeXOt0qiHSRQ4C5gSWbVSbzenDPwa+lm8l+AzJmGvwuO2lc+XZC0ipvF3H9guSLiVlDRmY2sOGDsD/AZeQusnP1J27AHWcT6NyTN2opvuvfXeUCsbOjAfWsD0tn7v/Ala0/VhBzWAYEstYwWwzUMZOxfOYj1Q7ZEKp5SRJ04DLgU86V0yWdE8FgaS3koyq51rG5weusr16+2cOWfe65mw6SQfaPiTfv9b2OwtoLgicCKxNalExCliTtKS1q+2nu61ZN5L+avvdFepVfj5Juo/pwdetFA3GrrJGVDC8Cc9OMBTWlPQ06UtsnnwfKqhPUvVyEukHeCfgIqX+QqcCowvqNZjWLp7D9jOSSl6pLNS80WTojAIWLaT5Q+A2UtuCaVlPJG/Dj4D/KaRbJ5fmjKw/MOMyVilvR+Xnk+2xs/I4SasViMtaVVKjIrWAt+TtxnfUGl3WC4Yp4dkJRhRtlpN+Cxw7q1+oXZrD+nkO25E8EGfYPqGQ1k3AhrS/Kr7U9pqFdI8DnrR9UMv4d4A32v5MAc07ba/0eveNZCS166tWzNtR1/k0K5TwutSRVRgMT8LYCUYUdS0ndZjLKGAzkifiU3msq1endS0B5KXBE0nZUDfl4TWByaSu510vxibpLtsrdtjXk8ZOOyTNVTDw/D5qWlIajJKlI2ZBu9IssaB6YhkrGGnUtZw0E3mp5fx8a/BLcoZalzTGzsrjum1k5fpBE3LxuUYH7tvc0vW8y7pXSvo6cLCbrsIkfQ0YdnVauklTld+dga2AxUvo1LykNBh1XnlXnSUWVEx4doIRS5XLSa9jTrVcndYVeNlN3RygfBLJWLyR9OO3FnADKUC5F2sLrUsycLYhVeT+LHC2q+m4PtC8Kj+fak5yiMDlHmdU3RMIgtnF9pW2P0cqGHY0qX4G8FqBulqmVZNuyWaklejaftr29sDmwM+BX5BK+n+02dCp8bPtGpK+m6snH0JKO18L+KftiXUbOpk6zqdKW0cE/UUsYwUjniqWk0YAdRlZXdfNS2V3D/CQXvhs9wDuINUrOifXFxpObvauzSUHCT/VMFglbUQqang/8KNGfJLt9bqlORvUdbEQVER4doJepbIvL0lLNW3G1Wl5euGHaQngu8CHgbsk/ZJUvqEXL0AnkbqsI2kccBrwACng/biSwpIumMWHfqLkPIL66cU/rCCAaj0dVwHLQbVXp5KWsv1I3qzMyKpLt4nh5AGZLWy/CpwLnJsr+24JzAs8LOli2zvXOsHufq7zNJ0vHwdOtn1kzma8sYs67XjTrDzI9q2F5xHUTBg7QTB06vI01GJk1ajbk+R2GKcDp+fWDduW0qppSan572NjUj8wcguH0n87C0nq+H7a/n1h/WCYEMZO0DPU6HGIoOTSgvV7k4ogaW5SNuFYqvk+nkTK/PpP05LS95i+pLRbAc1LJE0CHgUWIfUCQ9KSQOm+ZwuRvGZt6woBYez0CWHsBL1EMY+DpGNpb9QIWLibWq+DnglKngV61Zt0FvAfUv+vFwd5bDeoY0lpb2BHYEngPbZfzuMrkdLtS3K/7U8X1ghGAGHsBL1ESY/D5NncNyTqMrKGoXHXC0HJ7VjG9haDP6xrVL6klAtEngopQFnSXsAOwL2kkhElWUXS+ravbB6UtAHwSGuRzKB3CWMn6CWKeRxsT+y0T9IRpXSpyciqUbcTIz4ouQN/lfR227dUpFf5kpKklUlVzycAT5D62cn2RiX0Wrga+G+b8edJhtZWFcwhGAaEsROMKIahxwHSVep+JQ5cl5FVh+4w/WxL8x7gk7kh6IuU78Zdx5LS7aR+dlvZvgtA0j6FtFpZzPbNrYO2J0saW9EcgmFAGDvBSGO4eRygviWWYkZWTbrD8bMtzQeqFKtpSWk7kmfnUknnZf2q/mYG6nk1T0VzCIYBYewEI4q6PB2SOl31ij7KiiqpW+NSYW3Yvh9A0mJU0IyyjiUl22cAZ0iaj5Tmvg+wuKSfkPrZzWrhv9nhWkm72/5p86CkXUlB4UGfEI1Ag55B0gO2lyt07HtJSyxtU1htr1BIdyAj6ybby/SS7gDzKfbZ1omkDwNHAksBjwNvBqbaLtL/S9I00pLSrk1LSveUOn8HmMcYYHtgR9sbF9RZHDiDVK6gYdyMB+YCtrH9WCntYHgRxk7QM0h60Paydc+jm9RoZNWiO8B8eu6zBZB0Eykr6iLba+UifxNs71FIbxuSZ+fdQGNJ6UTby5fQGy7k93X1vDnF9iV1zieonjB2ghFFjZ6O1saTBv5l+8ESev3IcPMmVYGkybbHZ6NnrZwCfo3tdQrrNpaUJpCMrYmUX1IKgtoIYycYUdTo6bi0zfAYkjt8gu0iBdnqMrLq0B1u3qQqkHQRyeg4FFiUtJT1TtvvrnAOlSwpBUGdhLETBENA0njgB7bfW+j4dRlZtej2G9nD8jwwCvgYqb3Br20/UevEgqDHCGMnGFEMx+UkSdfbbp1Xac2iRlYdusPxs62C3JxzJdsXSZoXGG27XSG8IAhmk0g9D0YaR7YZGyOpFo9Dzvao/IohF0Wbv8d0h9VnWwWSdgf2IHnN3gIsDRwPbFLnvIKg1whjJxhRdKoHkj0OPwRKLSe1q+47hpTVslcJzUHmU4uRVVK3rs+2Zj4LrENqa4DtO3PNnSAIukgYO0FPUIGno7WCr0lF2fa1/Xgp0bqMrOFk3NXlxaqIF22/1OjBKWkOercPWBDURhg7QU9QgafjUtsPFDx+J2oxsmrUnYm6vFgVcZmkA4F5JG0G7An8oeY5BUHPEQHKwYhiMI+D7SI/FM1ByJJ+Z3u7EjptdJerw8iqQ7euz7ZOJI0CdgU2J6Xcn08q8hdfzEHQRcLYCUYUknZpGWp4HK4tvJx0g+21Wu+XpkYjq3Lduj7bIAh6n1jGCkYadS0nucP90jQX2KuyqF4dunV9tpUj6eaB9tteo6q5BEE/EMZOMNI4E6jc0wGsKelpkhEwT75P3rbtBQvp1mVk1aFb12dbB9NI7+tvSDE6z9c7nSDobcLYCUYatXg6bI+uSquFuoysOnTr8mJVju1xklYl9ab6DXBb/v8C26/UOrkg6EHC2AlGGnV5OmqhLiOrJt1++2xvB74BfEPSjsAvgMOAw2udWBD0IBGgHIwoJL0KPEv2OADPNXZR1tMRFKbfPltJSwM7AdsA/wYmkTqPP1PrxIKgBwljJwiCoGIkXQYsQDJwTgeebN5v+8l2zwuCYPYIYycIgqBiJN3H9KW65i/hhherp2OWgqBqwtgJgiAYpkhazfaUuucRBCOdUXVPIAiCIOjIL+ueQBD0AmHsBEEQDF80+EOCIBiMMHaCIAiGLxFnEARdIIydIAiCIAh6mjB2giAIhi8v1T2BIOgFIhsrCIKgYiS9GXjK9n/y9kbAR4D7gR/ZDiMnCLpIeHaCIAiqZxIwH4CkccBpwAPAmsBxNc4rCHqS6I0VBEFQPfPYfiTf/zhwsu0jJY0CbqxxXkHQk4RnJwiCoHqaU8o3Bi4GsD2NSDcPgq4Tnp0gCILquUTSJOBRYBHgEgBJSwIv1DmxIOhFwtgJgiConr2BHYElgffYfjmPrwSMqW1WQdCjRDZWEARBjeQA5Z2BHYB7gd/bPrbeWQVBbxGenSAIgoqRtDKwEzABeAL4Lenic6NaJxYEPUp4doIgCCpG0jTgcmBX23flsXtsr1DvzIKgN4lsrCAIgurZDngMuFTSTyVtQmRhBUExwrMTBEFQE5LmI1VOnkBKQZ8InGH7glonFgQ9Rhg7QRAEwwBJY4DtgR1tb1z3fIKglwhjJwiCIAiCniZidoIgCIIg6GnC2AmCIAiCoKcJYycIgiAIgp4mjJ0gCIIgCHqaMHaCIAiCIOhp/j8CrAVuH10W/gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot a heat map to show the correlation of the data\n", + "plt.figure(figsize=(8,8))\n", + "sns.heatmap(data.corr(method='pearson'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FULL_Charge 0.534602\n", + "FULL_AcidicMolPerc -0.598816\n", + "FULL_AURR980107 -0.584111\n", + "FULL_DAYM780201 -0.554838\n", + "FULL_GEOR030101 -0.260470\n", + "FULL_OOBM850104 -0.453287\n", + "NT_EFC195 0.260702\n", + "AS_MeanAmphiMoment 0.693552\n", + "AS_DAYM780201 -0.437168\n", + "AS_FUKS010112 0.033432\n", + "CT_RACS820104 0.267652\n", + "CLASS 1.000000\n", + "Name: CLASS, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#also checked the corelation in regards to the class since am trying to build a ML agorithm for that class\n", + "data.corr(method='pearson')['CLASS']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + " data.skew().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## understanding data with visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Histogram\n", + "### I want to understand each attribute of my dataset independently" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data pre-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,20))\n", + "data.hist()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Standardize data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 7.69712416e-01 -1.12341031e+00 -1.90047029e-01 1.37605977e-01\n", + " -6.06718766e-01 -7.20629784e-01 -3.11684110e-01 -1.33070267e+00\n", + " -2.25681314e-02 -3.60971652e-01 -9.25122883e-01]\n", + " [ 5.07884366e-01 -4.10857567e-01 -3.76275096e-01 -2.43225309e-01\n", + " -1.18128598e+00 -9.24503172e-01 3.20837658e+00 -1.30322672e+00\n", + " -5.92370366e-01 9.02050407e-01 1.03699430e+00]\n", + " [ 9.00626442e-01 -4.10857567e-01 -9.16336490e-01 -8.65106417e-03\n", + " -1.05360437e+00 -4.63244125e-02 -3.11684110e-01 -1.30383154e+00\n", + " -4.59030969e-01 -1.40916462e+00 2.31808537e+00]\n", + " [ 7.69712416e-01 -5.74065761e-01 -7.11485617e-01 -8.70124978e-01\n", + " 1.59370848e-01 6.27395116e-01 -3.11684110e-01 -1.30201709e+00\n", + " -7.01486075e-01 -2.30020073e+00 6.98862456e-01]\n", + " [ 1.42428254e+00 2.04070778e-03 -3.66963693e-01 -1.04957427e+00\n", + " -4.79037163e-01 2.00315519e-01 -3.11684110e-01 -1.30184429e+00\n", + " -7.71259861e-02 -1.97723618e+00 1.44656245e+00]]\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "array = data.values\n", + "#separate array into input and output components\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "scaler = StandardScaler().fit(X)\n", + "rescaledX = scaler.transform(X)\n", + "# summarize transformed data\n", + "#set_printoptions(precision=3)\n", + "print(rescaledX[0:5,:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature selection\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chose Recursive Feature Elimination because i dont have the library for my data in this case\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num Features: 8\n", + "Selected Features: [ True False True False True True True True False True True]\n", + "Feature Ranking: [1 3 1 2 1 1 1 1 4 1 1]\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array1 = data.values\n", + "X = array1[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = LogisticRegression()\n", + "rfe = RFE(model,8)\n", + "fit = rfe.fit(X,Y)\n", + "print(\"Num Features:\", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking:\", fit.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n", + " 'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n", + " 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n", + " 'CLASS'],\n", + " dtype='object')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " FULL_Charge FULL_AURR980107 FULL_GEOR030101 FULL_OOBM850104 \\\n", + "0 4.0 0.873 0.987 -4.833 \n", + "1 4.0 0.892 0.931 -0.584 \n", + "2 2.0 0.901 1.039 -5.664 \n", + "3 4.5 0.869 0.982 -5.423 \n", + "4 -4.0 1.061 0.976 -2.002 \n", + ".. ... ... ... ... \n", + "753 -1.5 1.100 0.991 -1.987 \n", + "754 -1.0 1.085 1.027 -0.745 \n", + "755 -1.0 1.108 1.033 -1.789 \n", + "756 -1.0 0.955 1.023 1.141 \n", + "757 -7.0 1.078 1.009 -0.066 \n", + "\n", + " NT_EFC195 AS_MeanAmphiMoment AS_FUKS010112 CT_RACS820104 \n", + "0 0 0.382 7.225 1.234 \n", + "1 0 0.320 4.942 1.853 \n", + "2 0 0.164 5.969 1.174 \n", + "3 0 2.010 5.462 1.138 \n", + "4 0 2.758 5.582 1.453 \n", + ".. ... ... ... ... \n", + "753 0 15.185 7.053 1.325 \n", + "754 0 16.550 6.729 1.132 \n", + "755 0 16.112 6.036 1.219 \n", + "756 0 20.630 5.669 1.111 \n", + "757 0 17.168 6.688 1.305 \n", + "\n", + "[758 rows x 8 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drop_test = test.drop(['FULL_AcidicMolPerc', 'FULL_DAYM780201', 'AS_DAYM780201'],axis=1)\n", + "drop_test" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "seleceted_train = X[:,fit.support_]\n", + "selected_test = test.values[:,fit.support_]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluate the Performance of Machine Learning Algorithms with Resampling¶\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split into Train and Test Sets" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 91.55701754385966\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array = data.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "test_size = 0.30\n", + "seed = 7\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "random_state=seed)\n", + "model = LogisticRegression()\n", + "model.fit(X_train, Y_train)\n", + "result = model.score(X_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## K-fold Cross Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: (83.5359128018065, 27.08521320979506)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "array = data.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "num_folds = 10 #number of folds to use\n", + "seed = 7 #reproducibility\n", + "\n", + "kfold = KFold(n_splits=num_folds, random_state=seed)\n", + "model = LogisticRegression()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "\n", + "print(f\"Accuracy:\", (results.mean()*100.0, results.std()*100.0))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Leave One Out Cross Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: (91.4417379855168, 27.974673416141517)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import LeaveOneOut\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "array = data.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "num_folds = 10\n", + "loocv = LeaveOneOut()\n", + "model = LogisticRegression()\n", + "results = cross_val_score(model, X, Y, cv=loocv)\n", + "print(\"Accuracy:\", (results.mean()*100.0, results.std()*100.0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Repeated Random Test-Train Splits" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: (91.30482456140349, 0.5803122202806698)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import ShuffleSplit\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "array = data.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "n_splits = 10\n", + "test_size = 0.30\n", + "seed = 7\n", + "kfold = ShuffleSplit(n_splits=n_splits, test_size=test_size, random_state=seed)\n", + "model = LogisticRegression()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(\"Accuracy: \" , (results.mean()*100.0, results.std()*100.0))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Algorithm Performance Metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Algorithms Overview\n", + "### linear machine learning algorithms:\n", + "\n", + " Logistic Regression.\n", + " Linear Discriminant Analysis.\n", + "### onlinear machine learning algorithms\n", + "\n", + " k-Nearest Neighbors.\n", + " Naive Bayes.\n", + " Classication and Regression Trees.\n", + " Support Vector Machines.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Machine Learning Algorithms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.835359128018065\n", + "MCC: 0.8342865299822478\n", + "[False True]\n", + "False: 383\n", + "True: 375\n" + ] + } + ], + "source": [ + "# Logistic Regression Classification\n", + "from pandas import read_csv\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array = data.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "num_folds = 10\n", + "kfold = KFold(n_splits=10, random_state=7)\n", + "model = LogisticRegression()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "my_report = pd.DataFrame(output)\n", + "my_report.columns = ['CLASS']\n", + "my_report.index.name = \"Index\"\n", + "my_report['CLASS']=my_report['CLASS'].map({0.0:False, 1.0:True})\n", + "my_report.to_csv(\"report_XGB.csv\")\n", + "\n", + "print(my_report['CLASS'].unique())\n", + "print('False: ',my_report.groupby('CLASS').size()[0].sum())\n", + "print('True: ',my_report.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"execute_result" + } + ], + "source": [ + "drop_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Discriminant Analysis¶\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "\n", + "\n", + "array = data.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "num_folds = 10\n", + "kfold = KFold(n_splits=10, random_state=7)\n", + "model = LinearDiscriminantAnalysis()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "lda_report = pd.DataFrame(output)\n", + "lda_report.columns = ['CLASS']\n", + "lda_report.index.name = \"Index\"\n", + "lda_report['CLASS']=lda_report['CLASS'].map({0.0:False, 1.0:True})\n", + "lda_report.to_csv(\"ldareport.csv\")\n", + "\n", + "print(lda_report['CLASS'].unique())\n", + "print('False: ',lda_report.groupby('CLASS').size()[0].sum())\n", + "print('True: ',lda_report.groupby('CLASS').size()[1].sum())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nonlinear Machine Learning Algorithms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### k-Nearest Neighbors" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8027933385443807\n", + "MCC: 0.8690586462107053\n", + "[False True]\n", + "False: 402\n", + "True: 356\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "array = data.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "num_folds = 10\n", + "kfold = KFold(n_splits=10, random_state=7)\n", + "model = KNeighborsClassifier()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "report_k = pd.DataFrame(output)\n", + "report_k.columns = ['CLASS']\n", + "report_k.index.name = \"Index\"\n", + "report_k['CLASS']=report_k['CLASS'].map({0.0:False, 1.0:True})\n", + "report_k.to_csv(\"report_k.csv\")\n", + "\n", + "\n", + "print(report_k['CLASS'].unique())\n", + "print('False: ',report_k.groupby('CLASS').size()[0].sum())\n", + "print('True: ',report_k.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Naive Bayes" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.880815746048289\n", + "MCC: 0.8407203694376205\n", + "[ True False]\n", + "False: 370\n", + "True: 388\n" + ] + } + ], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "array = data.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "kfold = KFold(n_splits=10, random_state=7)\n", + "model = GaussianNB()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "report_bayes = pd.DataFrame(output)\n", + "report_bayes.columns = ['CLASS']\n", + "report_bayes.index.name = \"Index\"\n", + "report_bayes['CLASS']=report_bayes['CLASS'].map({0.0:False, 1.0:True})\n", + "report_bayes.to_csv(\"report_bayes.csv\")\n", + "\n", + "\n", + "print(report_bayes['CLASS'].unique())\n", + "print('False: ',report_bayes.groupby('CLASS').size()[0].sum())\n", + "print('True: ',report_bayes.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classiffication and Regression Trees" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "array = data.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "kfold = KFold(n_splits=10, random_state=7)\n", + "model = DecisionTreeClassifier()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "report_tree = pd.DataFrame(output)\n", + "report_tree.columns = ['CLASS']\n", + "report_tree.index.name = \"Index\"\n", + "report_tree['CLASS']=report_tree['CLASS'].map({0.0:False, 1.0:True})\n", + "report_tree.to_csv(\"report_tree.csv\")\n", + "\n", + "\n", + "print(report_tree['CLASS'].unique())\n", + "print('False: ',report_tree.groupby('CLASS').size()[0].sum())\n", + "print('True: ',report_tree.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Support Vector Machines " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.svm import SVC\n", + "\n", + "array = data.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "kfold = KFold(n_splits=10, random_state=7)\n", + "model = SVC()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\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/.ipynb_checkpoints/Starter ACE_Assignment-checkpoint.ipynb b/.ipynb_checkpoints/Starter ACE_Assignment-checkpoint.ipynb new file mode 100644 index 0000000..8d2fdd4 --- /dev/null +++ b/.ipynb_checkpoints/Starter ACE_Assignment-checkpoint.ipynb @@ -0,0 +1,1283 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ACE-Class Assignment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#import the necessary libraries for the modeling.\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mAMP Data Sets\u001b[m\u001b[m\r\n", + "\u001b[34mAssignment Colab\u001b[m\u001b[m\r\n", + "\u001b[34mCurrent Platforms of AI and ML\u001b[m\u001b[m\r\n", + "\u001b[34mDataCamp_-_Track_-_Data_Scientist_with_R_-_Course_03_-_Introduction_to_the_Tidyverse\u001b[m\u001b[m\r\n", + "README.md\r\n", + "Starter ACE_Assignment.ipynb\r\n", + "\u001b[34mThur 20 Feb\u001b[m\u001b[m\r\n", + "handin_2.csv\r\n", + "sample_handin.csv\r\n" + ] + } + ], + "source": [ + "#let's see if we have the right files in this folder.\n", + "!ls" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "smp = pd.read_csv('sample_handin.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 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+ "\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": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#let's describe the data\n", + "#here we are looking for anomalies in the data\n", + "#since this data is already pre-prepared, I won't take much time in it and also its domain specific.\n", + "\n", + "train.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exportig...\n", + "Done\n" + ] + } + ], + "source": [ + "#i need to export the right test data\n", + "test.shape\n", + "print(\"Exportig...\")\n", + "#test.to_csv('Test.csv',index=False)\n", + "print(\"Done\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#let's draw a multivariate plot\n", + "sns.pairplot(train,hue='CLASS')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5NYNv429opaPAgap6eOW66oNn+h1KcvVK96B+JXk/g3/BEuAr7RbgFv8J4+nn1TsdSvJ4VZ2/0n2oT0m+A7ymqn46q34W8GBVbV2Zzvrg8s4ZKsn98w0B68fZizTLz4FXAI/Nqp/XxnQaGfpnrvXApcCJWfUA/z7+dqT/9x7gUJJHePYfMJ4PvBJ414p11QlD/8z1OeBlVfX12QNJvjT+dqSBqvp8kt9i8K/Whz/IvbeqfrZynfXBNX1J6ohX70hSRwx9SeqIoS9JHTH0Jakjhr4kdeT/AEpFttwDKShhAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#let's check how the class is distributed\n", + "train.CLASS.value_counts().plot(kind='bar')\n", + "#we see that the class is evenly distributed, that means we don't need SMOTE" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FULL_Charge 0.534602\n", + "FULL_AcidicMolPerc -0.598816\n", + "FULL_AURR980107 -0.584111\n", + "FULL_DAYM780201 -0.554838\n", + "FULL_GEOR030101 -0.260470\n", + "FULL_OOBM850104 -0.453287\n", + "NT_EFC195 0.260702\n", + "AS_MeanAmphiMoment 0.693552\n", + "AS_DAYM780201 -0.437168\n", + "AS_FUKS010112 0.033432\n", + "CT_RACS820104 0.267652\n", + "CLASS 1.000000\n", + "Name: CLASS, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check the correlation between variables and class\n", + "train.corr(method='pearson')['CLASS']\n", + "#the values seem to be fine.\n", + "#nothing out of the ordinary, for correlation against the final class." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#for inter-correlation\n", + "plt.figure(figsize=(8,8))\n", + "sns.heatmap(train.corr(method='pearson'))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#let's look at the skews\n", + "train.skew().plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104CLASS
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" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + "0 5.0 0.000 0.951 74.842 \n", + "1 4.0 5.405 0.931 71.595 \n", + "2 5.5 5.405 0.873 73.595 \n", + "3 5.0 4.167 0.895 66.250 \n", + "\n", + " FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n", + "0 0.975 -3.663 0 0.282 \n", + "1 0.957 -4.011 1 0.600 \n", + "2 0.961 -2.512 0 0.593 \n", + "3 0.999 -1.362 0 0.614 \n", + "\n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 CLASS \n", + "0 73.444 5.661 1.041 1 \n", + "1 68.222 6.537 1.453 1 \n", + "2 69.444 4.934 1.722 1 \n", + "3 67.222 4.316 1.382 1 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.head(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n", + " 'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n", + " 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n", + " 'CLASS'],\n", + " dtype='object')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#splite between X and Y\n", + "Y = train['CLASS']\n", + "X = train.drop(columns=['CLASS'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "#scale the data and onwards to modeling.\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "scaler = StandardScaler().fit(X)\n", + "sX = scaler.transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('LR', 0.8353580423831856, 0.27188952844394665)\n", + "('LDA', 0.8535044293903076, 0.2571395669719574)\n", + "('KNN', 0.8027933385443807, 0.2521136100771112)\n", + "('CART', 0.7345677001910718, 0.2823798053444265)\n", + "('NB', 0.880815746048289, 0.11642272449162755)\n", + "('SVM', 0.8350280093798853, 0.25836507020625044)\n", + "('XGB', 0.787307842626368, 0.2908368966924707)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Compare Algorithms\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.svm import SVC\n", + "from xgboost import XGBClassifier\n", + "\n", + "\n", + "# prepare models and add them to a list\n", + "models = []\n", + "models.append(('LR', LogisticRegression()))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('SVM', SVC()))\n", + "models.append(('XGB',XGBClassifier()))\n", + "\n", + "# evaluate each model in turn\n", + "results = []\n", + "names = []\n", + "scoring = 'accuracy'\n", + "\n", + "for name, model in models:\n", + " kfold = KFold(n_splits=10, random_state=7)\n", + " cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " msg = (name, cv_results.mean(), cv_results.std())\n", + " print(msg)\n", + "\n", + "# boxplot algorithm comparison\n", + "fig = plt.figure()\n", + "fig.suptitle('Algorithm Comparison')\n", + "ax = fig.add_subplot(111)\n", + "plt.boxplot(results)\n", + "ax.set_xticklabels(names)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#we now need to train-test-split\n", + "#then we need to train the model and test on MCC" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2,\n", + "random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The result is: 82.59 Mathew's Coef\n" + ] + } + ], + "source": [ + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "nv = GaussianNB()\n", + "nv.fit(X_train,Y_train)\n", + "pred = nv.predict(X_test)\n", + "\n", + "print(\"The result is: \",np.round(matthews_corrcoef(Y_test,pred) *100,2),\" Mathew's Coef\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "\n", + "# Input data files are available in the \"../input/\" directory.\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "for dirname, _, filenames in os.walk('/kaggle/input'):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "\n", + "# Any results you write to the current directory are saved as output." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing some of the needed packages and libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import pyplot\n", + "import seaborn as sns\n", + "\n", + "from sklearn.model_selection import KFold\n", + "from pandas import read_csv\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.svm import SVC\n", + "from sklearn.ensemble import AdaBoostClassifier\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Description of Libraries used.\n", + "* Pandas is a library in Python for data manipulation and analysis. It offers data structures and operations for manipulating numerical tables and time series.\n", + "* Matplotlib is a plotting library for the Python.\n", + "* Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n", + "* K-Fold cross validation is where a given data set is split into a K number of sections/folds where each fold is used as a testing set at some point.\n", + "* A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known.\n", + "* In Train/Test Split the data we use is usually split into training data and test data. The training set contains a known output and the model learns on this data in order to be generalized to other data later on\n", + "* Logistic Regression is used for classification problems, it is a predictive analysis algorithm and based on the concept of probability.\n", + "* A decision tree is a flowchart-like tree structure where an internal node represents feature(attribute), the branch represents a decision rule, and each leaf node represents the outcome.\n", + "* KNeighbours Classifier is a non-parametric algorithm whose purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point.\n", + "* The Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. It performs well in cases of categorical input variables compared to numerical variables. \n", + "* The linear Discriminant analysis estimates the probability that a new set of inputs belongs to every class.\n", + "* SVC (Support Vector Classifier) is to fit the data you provide, returning a \"best fit\" hyperplane that divides, or categorizes your data.\n", + "* The basic concept behind Adaboost is to set the weights of classifiers and training the data sample in each iteration such that it ensures the accurate predictions of unusual observations.\n", + "* AdaBoost works by weighting the observations, putting more weight on difficult to classify instances and less on those already handled well.\n", + "* ExtraTreesClassifier, like RandomForest, randomizes certain decisions and subsets of data to minimize over-learning from the data and overfitting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> *Now i need to get rid of some unnecessary warnings by ignoring them*" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#To avoid unnecessary warnings, I will ignore them in the code below\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the datasets \n", + "### There are two datasets given; \n", + "1. AMP_TrainSet.csv and \n", + "2. Test.csv " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "_cell_guid": "", + "_uuid": "" + }, + "outputs": [], + "source": [ + "#Loading the datasets, \n", + "\n", + "Train = pd.read_csv(\"../Data/AMP_TrainSet.csv\")\n", + "Test = pd.read_csv(\"../Data/Test.csv\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For training the model, I will first use the training set which I will further split into the train and validation set. \n", + "\n", + "
\n", + " @atwine: Did not explain why they are splitting, I need to see why they are doing this.\n", + "
\n", + "### Then I will test the model on the Test set provided to gauge its performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exploring my data\n", + "## Exploring data helps one understand what kind of data is given. That is to say knowing how much data it is, the shape, type, dimensions, missing values, data summary, correlation of attributes among others as am going to do in the following steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#here, am trying to find the type of data\n", + "type(Train)\n", + "type(Test)\n", + "Train.dtypes, Test.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# checking the dimensions of the data\n", + "# this retuns the number of rows and columns in the data\n", + "\n", + "Train.shape, Test.shape\n", + "\n", + "#this helps to know how big the data is in terms of rows and columns.\n", + "#also from here I can tell which data is labeled" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data description\n", + ">For now, I will focus more on the train dataset because its what I will use to train the model " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#getting a description of the train dataset\n", + "#description gives a summary of the data.\n", + "\n", + "Train.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#looking at the first 5 entries of my data\n", + "Train.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Class Distribution\n", + "> From the Train dataset, i see there is an extra 'CLASS' column which will be my validation set.\n", + "### I need to know how this class is distributed to guide me on what to do with it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#to see the class distribution, I will plot a bar graph\n", + "Train.groupby('CLASS').size().plot(kind='bar')\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> I would like to know how many instaces i have for each class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#getting the number of instances in each class\n", + "Train.groupby('CLASS').size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### There are 1519 intances for each class which is proof for even distribution.\n", + "### From the above barplot and class instances, I can see that the classes are evenly distributed so no need to use smote." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Visualisation\n", + "### Data visualization is the technique to present the data in a pictorial or graphical format. It enables one to analyze data visually. The data in a graphical format allows identification of new trends and patterns easily.\n", + "### Visualisation identifies the relationship between data points and variables." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Density plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> I chose to use density plots because they are better at determing the distribution shape as they are not affected by the number of bins as is the case with Histograms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#now plotting density subplots\n", + "#setting the figsize to 12 so that my graphs are not congested\n", + "Train.plot(kind='density', subplots = True, layout=(3,4), sharex= False, sharey= False, figsize=(12,12))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> From the above density subpots, I can see that most of the data follows a Gaussian distribution given some of the characteristics such as bell shaped curves and graphs being symmetrical about the mean." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Box and whisker plots\n", + "> A box plot is a type of graph that displays a summary of a large amount of data in terms of the median, upper quartile, lower quartile, minimum and maximum data values.\n", + "\n", + "> It handles large data easily, gives a clear summary and displays outliers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#plotting a box and whisker graph\n", + "#setting the figsize to 10 so that the plots are not congested.\n", + "Train.plot(kind='box', subplots = True, layout=(3,4), sharex= False, sharey= False, figsize=(10,10))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Looking at the correlation of the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Correlation is the relationship between two variables. The commonly used method is Pearson's correlation coefficient. It assumes a normal distribution of the attributes involved.\n", + "\n", + "> -1 shows full negative correlation, \n", + "\n", + "> +1 shows full negative correlation and \n", + "\n", + "> 0 shows no correlation at all." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#first I will checkfor the pairwise correlation of the attributes.\n", + "Train.corr(method='pearson')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Then now am reviewing the inter-correlation of attributes using heatmap\n", + "#graphical representation\n", + "plt.figure(figsize=(6,6))\n", + "sns.heatmap(Train.corr(method='pearson'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Ill also check the correlation in regards to the 'CLASS' attribute\n", + "Train.corr(method='pearson')['CLASS']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Skewnwess of the data\n", + "> knowing data skewness allows one to perform data preparation and improve a model\n", + "\n", + "> If Skewness value lies above +1 or below -1 then the data is highly skewed. \n", + "\n", + "> If skewness value lies between +0.5 and -0.5 then the data ids moderately skewed.\n", + "\n", + "> If skewness is 0 then data is symmetrical" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Checking out skewness of data\n", + "Train.skew().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparing Machine Learning Algorithms\n", + "> This helps to choose the best model for the problem at hand.\n", + "\n", + "> Using resampling methods like cross validation, gives an estimate for how accurate each model may be on unseen data.\n", + "\n", + "> It is important to ensure that each algorithm is evaluated in the same way on the same data to avoid bias." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#comparing different models to from which ill choose.\n", + "\n", + "\n", + "# load dataset\n", + "\n", + "array = Train.values\n", + "\n", + "#split the dataset \n", + "X = array[:,0:11] #X = Train.drop(columns=['CLASS'])\n", + "Y = array[:,11] #Y = Train['CLASS']\n", + "\n", + "# preparing models and adding them to a list\n", + "models = []\n", + "models.append(('LR', LogisticRegression()))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('SVM', SVC()))\n", + "models.append(('AB', AdaBoostClassifier()))\n", + "models.append(('GBC', GradientBoostingClassifier()))\n", + "models.append(('EXT', ExtraTreesClassifier()))\n", + "models.append(('RTC', RandomForestClassifier()))\n", + "#models.append(('XGB', XGBClassifier)) \n", + "#am commenting out XGB it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' methods.\n", + "\n", + "# evaluating each model in turn\n", + "results = []\n", + "names = []\n", + "\n", + "for name, model in models:\n", + " kfold = KFold(n_splits=50, random_state=42)\n", + " cv_results = cross_val_score(model, X, Y, cv=kfold, scoring='accuracy')\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " msg = (name, cv_results.mean(), cv_results.std())\n", + " print(msg)\n", + "\n", + "# boxplot algorithm comparison\n", + "fig = pyplot.figure()\n", + "fig.suptitle('Algorithm Comparison')\n", + "ax = fig.add_subplot(111)\n", + "pyplot.boxplot(results)\n", + "ax.set_xticklabels(names)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> ## From the above algorithm comparison, I can see that Naive Bayes(NB), ExtraTreesClassifiers(EXT) and RandomForestClassifiers(RTC) are some of the best performing algorithms. \n", + "> ## Am yet to find out the overall best valgorithm after prediction on the Test set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature selection \n", + "## Using recursive feature elimination\n", + "> Sometimes, you may asses a dataset and find out that you do not need to use all the given features. This can be because some of them are highly correlates or they are not in any way helpful in developing the model.\n", + "* It is therefore important to get rid of some features where applicable.\n", + "\n", + "> For this data, I will use Recursive Feature Elimination(RFE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#feature selection using RFE\n", + "#first i will start with choosing 4 features\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = LogisticRegression()\n", + "rfe = RFE(model, 4)\n", + "fit = rfe.fit(X, Y)\n", + "print(\"Num Features: \", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking: \", fit.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Calling out the column names so I can know which features am going to drop from RFE\n", + "Train.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import ExtraTreesClassifier\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = ExtraTreesClassifier()\n", + "model.fit(X, Y)\n", + "print(model.feature_importances_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## If am to use 4 features and drop the rest\n", + "> I will assign the new dataset a variable 'New_Train4' after choosing the 4 features and dropping the rest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# I will call the new Train data with selected features New_Train4.\n", + "Train\n", + "New_Train4 = Train.drop(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_DAYM780201', 'FULL_GEOR030101', 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112'], axis =1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#dropping the same features in the test dataset\n", + "New_Test4 = Test.drop(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_DAYM780201', 'FULL_GEOR030101', 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112'], axis =1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#viewing the selected features\n", + "New_Train4.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#now splitting data\n", + "#array = New_Train.values\n", + "X = New_Train4.values[:,0:4]\n", + "Y = New_Train4.values[:,4]\n", + "from sklearn.model_selection import train_test_split\n", + "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# also train and test the model on Matthews correlation coefficient.\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "GS = GaussianNB()\n", + "GS.fit(X_Train,Y_Train)\n", + "pred = GS.predict(X_Test)\n", + "\n", + "print(\"The result is: \",np.round(matthews_corrcoef(Y_Test,pred) *100,2),\" Mathew's Coef\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using four features gives me a very low MCC and low overall score.\n", + "## I'll therefore consider using 8 features and see what score I get." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = LogisticRegression()\n", + "rfe = RFE(model, 8)\n", + "fit = rfe.fit(X, Y)\n", + "print(\"Num Features: \", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking: \", fit.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# I will call the new Train data with selected features New_Train8.\n", + "Train\n", + "New_Train8 = Train.drop(['FULL_AcidicMolPerc', 'FULL_DAYM780201', 'AS_DAYM780201'], axis =1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#dropping the same features in the test dataset\n", + "New_Test8 = Test.drop(['FULL_AcidicMolPerc', 'FULL_DAYM780201', 'AS_DAYM780201'], axis =1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#now splitting data\n", + "#array = New_Train.values\n", + "\n", + "\n", + "X = New_Train8.values[:,0:8]\n", + "Y = New_Train8.values[:,8]\n", + "from sklearn.model_selection import train_test_split\n", + "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#now creating a model and training it on 8 features\n", + "model = LogisticRegression(solver='liblinear', C=0.05, multi_class='ovr', random_state=30)\n", + "model.fit(X_Train, Y_Train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_Train, X_Test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2,\n", + "random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "nv = GaussianNB()\n", + "nv.fit(X_Train,Y_Train)\n", + "pred = nv.predict(X_Test)\n", + "\n", + "print(\"The result is: \",np.round(matthews_corrcoef(Y_Test,pred) *100,2),\" Mathew's Coef\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Standardising data\n", + "> This is useful to transform attributes with a Gaussian distribution and it workd better with rescaled data(also known as normalistaion where attributes are scaled into a range between 0 and 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Below are some of the algorithms I will be using;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LINEAR ALGORITHMS\n", + "1. Linear Regression\n", + "2. Logistic Regression\n", + "3. Linear Discriminant Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression using 8 features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# here am using a dataset 'New_Train8' with 8 selected features\n", + "array = New_Train8.values\n", + "X = array[:,0:8]\n", + "Y = array[:,8]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42) #spliiting my data into 10 folds and random_state of 42 for reproducibility\n", + "model = LogisticRegression() #calling out the prediction algorithm\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(New_Test8.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_logistic = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_logistic.columns = ['CLASS'] #renaming the output column to 'CLASS'\n", + "maria_logistic.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_logistic['CLASS']=maria_logistic['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_logistic.to_csv(\"maria_logistic.csv\") #changing my output file as a 'csv' file\n", + "\n", + "print(maria_logistic['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria_logistic.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_logistic.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NOTE\n", + "> I have noticed that for this particular dataset, the more I drop features, the prediction scores also keep decreasing.\n", + "\n", + "> With 4 features selected, the score was low, with 8 features, the score improved and with all features, the prediction score was high.\n", + "\n", + "> I therefore decided to work with all the features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Linear Dicriminant Analysis (LDA) using all features " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "num_folds = 10\n", + "kfold = KFold(n_splits=10, random_state=42) #spliiting my data into 10 folds and random_state of 42 for reproducibility\n", + "model = LinearDiscriminantAnalysis() #calling out the prediction algorithm\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_LDA = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_LDA.columns = ['CLASS'] #renaming the output column to 'CLASS'\n", + "maria_LDA.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_LDA['CLASS']=maria_LDA['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_LDA.to_csv(\"maria_LDA.csv\") #changing my output file as a 'csv' file\n", + "\n", + "print(maria_LDA['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria_LDA.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_LDA.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NON LINEAR ALGORITHMS\n", + "1. Naive Bayes\n", + "2. Support Vector Machines\n", + "3. K-Nearest Neighbours\n", + "4. Classification and Regression Trees\n", + "5. Learning Vector Quantization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Naive Bayes and all the features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "test_size = 0.35 \n", + "\n", + "kfold = KFold(n_splits=10) #spliiting my data into 10 folds \n", + "\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=42) #random_state of 42 for reproducibility\n", + "\n", + "model = GaussianNB() #calling out the prediction algorithm\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria2_bayes = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria2_bayes.columns = ['CLASS'] #renaming the output column to 'CLASS'\n", + "maria2_bayes.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria2_bayes['CLASS']=maria2_bayes['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria2_bayes.to_csv(\"maria2_bayes.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria2_bayes['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria2_bayes.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria2_bayes.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Support Vector Machines algorithm using all features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "kfold = KFold(n_splits=10) #spliiting my data into 10 folds\n", + "\n", + "model = SVC() #calling out the prediction algorithm\n", + "scoring = 'acuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "maria_svc = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_svc.columns = ['CLASS'] #renaming the output column to 'CLASS'\n", + "maria_svc.index.name = 'Index' #naming the index column as 'Index'\n", + "maria_svc['CLASS'] = maria_svc['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "\n", + "maria_svc.to_csv('maria_svc.csv') #changing my output file as a 'csv' file\n", + "\n", + "print(maria_svc['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria_svc.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_svc.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Classification and Regression Trees" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "kfold = KFold(n_splits=10, random_state=42) #spliiting my data into 10 folds and random_state of 42 for reproducibility\n", + "model = DecisionTreeClassifier() #calling out the prediction algorithm\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_tree = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_tree.columns = ['CLASS'] #renaming the output column to 'CLASS'\n", + "maria_tree.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_tree['CLASS']=maria_tree['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_tree.to_csv(\"maria_tree.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria_tree['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria_tree.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_tree.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# K-Nearest Neighbours" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42) #spliiting my data into 10 folds and random_state of 42 for reproducibility\n", + "model = KNeighborsClassifier() #calling out the prediction algorithm\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "model.fit(X,Y) \n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_knn = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_knn.columns = ['CLASS'] #renaming the output column to 'CLASS' \n", + "maria_knn.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_knn['CLASS']=maria_knn['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_knn.to_csv(\"maria_knn.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria_knn['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria_knn.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_knn.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Esemble Algorithms\n", + "1. Boosting and Adaboost\n", + "2. Bagging and Random forest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Adaboost " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "\n", + "model = AdaBoostClassifier(n_estimators=200, random_state=42) \n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "\n", + "print(results.mean())\n", + "\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "maria_AB= pd.DataFrame(output)\n", + "maria_AB.columns = ['CLASS']\n", + "maria_AB.index.name = 'Index'\n", + "maria_AB['CLASS'] = maria_AB['CLASS'].map({0.0:False, 1.0:True})\n", + "\n", + "maria_AB.to_csv('maria_AB.csv')\n", + "\n", + "print(maria_AB['CLASS'].unique())\n", + "print('False: ',maria_AB.groupby('CLASS').size()[0].sum())\n", + "print('True: ',maria_AB.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Gradient Boosting Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "#num_trees = 200\n", + "#seed =42\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = GradientBoostingClassifier(n_estimators=200, random_state=42)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X,Y) \n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_GBC = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_GBC.columns = ['CLASS'] #renaming the output column to 'CLASS' \n", + "maria_GBC.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_GBC['CLASS']=maria_GBC['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_GBC.to_csv(\"maria_GBC.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria_GBC['CLASS'].unique()) #checking out the unique instances in the \n", + "print('False: ',maria_GBC.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_GBC.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Stochastic Gradient Boosting Classification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = XGBClassifier(n_estimators=200, random_state=42)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X,Y) \n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_XGB = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_XGB.columns = ['CLASS'] #renaming the output column to 'CLASS' \n", + "maria_XGB.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_XGB['CLASS']=maria_XGB['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_XGB.to_csv(\"maria_XGB.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria_GBC['CLASS'].unique()) #checking out the unique instances in the \n", + "print('False: ',maria_XGB.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_XGB.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Extra Trees Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = ExtraTreesClassifier(n_estimators=200) # (max_features=11)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + " \n", + "model.fit(X,Y) \n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_ETX = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_ETX.columns = ['CLASS'] #renaming the output column to 'CLASS' \n", + "maria_ETX.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_ETX['CLASS']=maria_ETX['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_ETX.to_csv(\"maria_ETX.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria_ETX['CLASS'].unique()) #checking out the unique instances in the \n", + "print('False: ',maria_ETX.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_ETX.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CONCLUSION\n", + "## From the algorithm comparisons and my prediction scores, Naive Bayes algorithm performed the best.\n", + "## I think this is because the data was following a Gaussian distribution(normally distributed) as seen earlier from the density subplots." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# REFERENCES\n", + "1. https://realpython.com/logistic-regression-python/\n", + "2. https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_extra_trees.htm\n", + "3. https://stackabuse.com/gradient-boosting-classifiers-in-python-with-scikit-learn/\n", + "4. https://machinelearningmastery.com/stochastic-gradient-boosting-xgboost-scikit-learn-python/\n", + "5. https://machinelearningmastery.com/stochastic-gradient-boosting-xgboost-scikit-learn-python/\n", + "6. https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/\n", + "7. https://machinelearningmastery.com/master-machine-learning-algorithms/\n", + "8. https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/\n", + "9. https://github.com/search?q=data-analysis-and-visualization-with-python&type=Repositories\n", + "10. https://stackabuse.com/applying-filter-methods-in-python-for-feature-selection/\n", + "11. https://towardsdatascience.com/decision-tree-in-machine-learning-e380942a4c96\n", + "12. https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/" + ] + } + ], + "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/.ipynb_checkpoints/MARIA MAGDALENE NAMAGANDA-checkpoint.ipynb b/Assignment Colab/.ipynb_checkpoints/MARIA MAGDALENE NAMAGANDA-checkpoint.ipynb new file mode 100644 index 0000000..8155bd1 --- /dev/null +++ b/Assignment Colab/.ipynb_checkpoints/MARIA MAGDALENE NAMAGANDA-checkpoint.ipynb @@ -0,0 +1,1769 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", + "# For example, here's several helpful packages to load in \n", + "\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "\n", + "# Input data files are available in the \"../input/\" directory.\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "for dirname, _, filenames in os.walk('/kaggle/input'):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "\n", + "# Any results you write to the current directory are saved as output." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#import the necessary libraries you are going to use\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "# -----> Put your code here below:\n", + "\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Working with AMP_Data_set\n", + "## `Training and testing data in Machine Learning`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "_cell_guid": "", + "_uuid": "" + }, + "outputs": [], + "source": [ + "#Load the datasets, there are two i.e the Train and Test datasets\n", + "\n", + "Train = pd.read_csv(\"../AMP Data Sets/AMP_TrainSet.csv\")\n", + "Test = pd.read_csv(\"../AMP Data Sets/Test.csv\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">Getting to know more about the data\n", + "\n", + "
(Atwine)\n", + "\n", + "When you run the code below, what did you learn?\n", + "\n", + "Please document those findings.
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\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", + "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\n" + ] + } + ], + "source": [ + "#here, am trying to find the kind of data am dealing with.\n", + "print(type(Train))\n", + "print(type(Test))\n", + "print(Train.dtypes)\n", + "print(Test.dtypes)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((3038, 12), (758, 11))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the dimensions of your data\n", + "# this retuns the number of rows and columns in the data\n", + "\n", + "Train.shape, Test.shape\n", + "\n", + "#this helps to know how big the data is in terms of rows and columns.\n", + "#it also informs one of which data is labeled" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> The data is pre-prepared so ill just continue to work with it, since the Train dataset is already labeled with a CLASS attribute.\n", + "\n", + "
\n", + " This is a good note\n", + "

Please highlight what you learnt from the describe function below, what does it tell you? \n", + " What did you notice from the data? Is it perfect?\n", + "

\n", + "

Or there are some things that bug you but you are deciding to leave them a lone for some reason?

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + "count 758.000000 758.000000 758.000000 758.000000 \n", + "mean 2.073879 8.945091 0.973046 73.821891 \n", + "std 4.230615 7.814449 0.110676 8.029524 \n", + "min -13.000000 0.000000 0.699000 47.000000 \n", + "25% -0.500000 2.721750 0.894000 68.740250 \n", + "50% 2.000000 7.500000 0.965000 74.069500 \n", + "75% 4.000000 14.230250 1.053500 79.284750 \n", + "max 30.000000 44.118000 1.431000 102.929000 \n", + "\n", + " FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n", + "count 758.000000 758.000000 758.000000 758.000000 \n", + "mean 0.994212 -2.397922 0.084433 15.570067 \n", + "std 0.032370 1.597138 0.278219 11.362589 \n", + "min 0.889000 -7.844000 0.000000 0.060000 \n", + "25% 0.973000 -3.457250 0.000000 5.709000 \n", + "50% 0.994000 -2.238000 0.000000 15.057000 \n", + "75% 1.013000 -1.306250 0.000000 25.290250 \n", + "max 1.182000 2.017000 1.000000 50.098000 \n", + "\n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 \n", + "count 758.000000 758.000000 758.000000 \n", + "mean 73.844204 5.904492 1.250189 \n", + "std 8.915193 0.656911 0.218102 \n", + "min 47.000000 3.843000 0.841000 \n", + "25% 68.346000 5.471250 1.096000 \n", + "50% 73.646000 5.935500 1.188000 \n", + "75% 80.069250 6.375250 1.378500 \n", + "max 102.929000 7.588000 2.283000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#getting a description of the data\n", + "#Train.describe, Test.describe\n", + "Train.describe()\n", + "Test.describe()\n", + "#description gives a summary of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104
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" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + "0 4.0 3.704 0.873 73.519 \n", + "1 4.0 4.444 0.892 62.444 \n", + "2 2.0 0.000 0.901 47.000 \n", + "3 4.5 0.000 0.869 69.222 \n", + "4 -4.0 21.591 1.061 71.682 \n", + "\n", + " FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n", + "0 0.987 -4.833 0 0.382 \n", + "1 0.931 -0.584 0 0.320 \n", + "2 1.039 -5.664 0 0.164 \n", + "3 0.982 -5.423 0 2.010 \n", + "4 0.976 -2.002 0 2.758 \n", + "\n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 \n", + "0 74.556 7.225 1.234 \n", + "1 56.056 4.942 1.853 \n", + "2 47.000 5.969 1.174 \n", + "3 69.222 5.462 1.138 \n", + "4 66.000 5.582 1.453 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#looking at the first 5 entries of my data\n", + "Train.head()\n", + "Test.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">Looking at the skewness of the data\n", + "\n", + "
\n", + " What did you learn?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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FULL_OOBM850104-0.2837580.5133440.4627120.3347780.3191571.000000-0.230561-0.3362970.275640-0.4527690.155304-0.453287
NT_EFC1950.088068-0.143168-0.169540-0.090058-0.230417-0.2305611.0000000.178683-0.0368440.1459240.0808980.260702
AS_MeanAmphiMoment0.355477-0.431590-0.426097-0.408793-0.160269-0.3362970.1786831.000000-0.3223780.0255800.1715240.693552
AS_DAYM780201-0.3653740.4496210.4562600.894191-0.0290850.275640-0.036844-0.3223781.0000000.045562-0.256060-0.437168
AS_FUKS010112-0.0905700.0023340.0329580.0559150.040480-0.4527690.1459240.0255800.0455621.000000-0.4452840.033432
CT_RACS8201040.232929-0.213543-0.403599-0.326792-0.1519350.1553040.0808980.171524-0.256060-0.4452841.0000000.267652
CLASS0.534602-0.598816-0.584111-0.554838-0.260470-0.4532870.2607020.693552-0.4371680.0334320.2676521.000000
\n", + "
" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 \\\n", + "FULL_Charge 1.000000 -0.612996 -0.490977 \n", + "FULL_AcidicMolPerc -0.612996 1.000000 0.794796 \n", + "FULL_AURR980107 -0.490977 0.794796 1.000000 \n", + "FULL_DAYM780201 -0.434603 0.541481 0.548253 \n", + "FULL_GEOR030101 -0.058725 0.115201 0.346139 \n", + "FULL_OOBM850104 -0.283758 0.513344 0.462712 \n", + "NT_EFC195 0.088068 -0.143168 -0.169540 \n", + "AS_MeanAmphiMoment 0.355477 -0.431590 -0.426097 \n", + "AS_DAYM780201 -0.365374 0.449621 0.456260 \n", + "AS_FUKS010112 -0.090570 0.002334 0.032958 \n", + "CT_RACS820104 0.232929 -0.213543 -0.403599 \n", + "CLASS 0.534602 -0.598816 -0.584111 \n", + "\n", + " FULL_DAYM780201 FULL_GEOR030101 FULL_OOBM850104 \\\n", + "FULL_Charge -0.434603 -0.058725 -0.283758 \n", + "FULL_AcidicMolPerc 0.541481 0.115201 0.513344 \n", + "FULL_AURR980107 0.548253 0.346139 0.462712 \n", + "FULL_DAYM780201 1.000000 0.010118 0.334778 \n", + "FULL_GEOR030101 0.010118 1.000000 0.319157 \n", + "FULL_OOBM850104 0.334778 0.319157 1.000000 \n", + "NT_EFC195 -0.090058 -0.230417 -0.230561 \n", + "AS_MeanAmphiMoment -0.408793 -0.160269 -0.336297 \n", + "AS_DAYM780201 0.894191 -0.029085 0.275640 \n", + "AS_FUKS010112 0.055915 0.040480 -0.452769 \n", + "CT_RACS820104 -0.326792 -0.151935 0.155304 \n", + "CLASS -0.554838 -0.260470 -0.453287 \n", + "\n", + " NT_EFC195 AS_MeanAmphiMoment AS_DAYM780201 \\\n", + "FULL_Charge 0.088068 0.355477 -0.365374 \n", + "FULL_AcidicMolPerc -0.143168 -0.431590 0.449621 \n", + "FULL_AURR980107 -0.169540 -0.426097 0.456260 \n", + "FULL_DAYM780201 -0.090058 -0.408793 0.894191 \n", + "FULL_GEOR030101 -0.230417 -0.160269 -0.029085 \n", + "FULL_OOBM850104 -0.230561 -0.336297 0.275640 \n", + "NT_EFC195 1.000000 0.178683 -0.036844 \n", + "AS_MeanAmphiMoment 0.178683 1.000000 -0.322378 \n", + "AS_DAYM780201 -0.036844 -0.322378 1.000000 \n", + "AS_FUKS010112 0.145924 0.025580 0.045562 \n", + "CT_RACS820104 0.080898 0.171524 -0.256060 \n", + "CLASS 0.260702 0.693552 -0.437168 \n", + "\n", + " AS_FUKS010112 CT_RACS820104 CLASS \n", + "FULL_Charge -0.090570 0.232929 0.534602 \n", + "FULL_AcidicMolPerc 0.002334 -0.213543 -0.598816 \n", + "FULL_AURR980107 0.032958 -0.403599 -0.584111 \n", + "FULL_DAYM780201 0.055915 -0.326792 -0.554838 \n", + "FULL_GEOR030101 0.040480 -0.151935 -0.260470 \n", + "FULL_OOBM850104 -0.452769 0.155304 -0.453287 \n", + "NT_EFC195 0.145924 0.080898 0.260702 \n", + "AS_MeanAmphiMoment 0.025580 0.171524 0.693552 \n", + "AS_DAYM780201 0.045562 -0.256060 -0.437168 \n", + "AS_FUKS010112 1.000000 -0.445284 0.033432 \n", + "CT_RACS820104 -0.445284 1.000000 0.267652 \n", + "CLASS 0.033432 0.267652 1.000000 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#first ill review the pairwise correlation of the attributes.\n", + "Train.corr(method='pearson')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " What did you learn from the correlation?\n", + " \n", + "

What does correlation server, in this instance? Why are we even doing it?\n", + " \n", + " Please write your report as if you are explaining to someone who has never done Machine learning\n", + "

\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Please draw better visuals.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Reviewing inter-correlation of attributes using heatmap\n", + "#graphical representation\n", + "plt.figure(figsize=(6,6))\n", + "sns.heatmap(Train.corr(method='pearson'))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FULL_Charge 0.534602\n", + "FULL_AcidicMolPerc -0.598816\n", + "FULL_AURR980107 -0.584111\n", + "FULL_DAYM780201 -0.554838\n", + "FULL_GEOR030101 -0.260470\n", + "FULL_OOBM850104 -0.453287\n", + "NT_EFC195 0.260702\n", + "AS_MeanAmphiMoment 0.693552\n", + "AS_DAYM780201 -0.437168\n", + "AS_FUKS010112 0.033432\n", + "CT_RACS820104 0.267652\n", + "CLASS 1.000000\n", + "Name: CLASS, dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Ill also check the correlation in regards to the 'CLASS' attribute\n", + "Train.corr(method='pearson')['CLASS']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Train['CLASS'].value_counts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " What did you learn?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AxesSubplot(0.125,0.125;0.775x0.755)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#I need to know the distribution of the class attribute of my data.\n", + "print(Train.groupby('CLASS').size().plot(kind='bar'))\n", + "#train.CLASS.value_counts().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### From the above bar graph, the Class distribution is even which means am dealing with balanced data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature selection \n", + "## Using recursive feature elimination\n", + "\n", + "\n", + "
\n", + " What is Feature Selection?\n", + " \n", + " What are you trying to achieve here? Please write your thoughts.\n", + " \n", + " How did you arrive at 4 features?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num Features: 4\n", + "Selected Features: [False False True False False True True False False False True]\n", + "Feature Ranking: [3 7 1 6 2 1 1 4 8 5 1]\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = LogisticRegression()\n", + "rfe = RFE(model, 4)\n", + "fit = rfe.fit(X, Y)\n", + "print(\"Num Features: \", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking: \", fit.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n", + " 'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n", + " 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n", + " 'CLASS'],\n", + " dtype='object')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Calling out the column names so I can know which features am going to drop from RFE\n", + "Train.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Feature importance\n", + "\n", + "\n", + "
\n", + " When you see \"???\" just know you haven't sufficiently explained your thoughts.\n", + " \n", + " ## ??\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.08652491 0.1297364 0.09323019 0.09956668 0.05401905 0.07223362\n", + " 0.03333241 0.30103142 0.05143828 0.03167747 0.04720957]\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import ExtraTreesClassifier\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = ExtraTreesClassifier()\n", + "model.fit(X, Y)\n", + "print(model.feature_importances_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Am going to use 4 features and drop the rest\n", + "\n", + "
\n", + " ## ??\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# I will call the new Train data with selected features New_Train.\n", + "Train\n", + "New_Train = Train.drop(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_DAYM780201', 'FULL_GEOR030101', 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112'], axis =1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#dropping the same features in the test dataset\n", + "New_Test = Test.drop(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_DAYM780201', 'FULL_GEOR030101', 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112'], axis =1)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_AURR980107', 'FULL_OOBM850104', 'NT_EFC195', 'CT_RACS820104',\n", + " 'CLASS'],\n", + " dtype='object')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#viewing tselected features\n", + "New_Train.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Rescaling data\n", + "\n", + "\n", + "
\n", + " ## ??\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.348 0.483 0. 0.182]\n", + " [0.322 0.458 1. 0.475]\n", + " [0.246 0.565 0. 0.666]\n", + " [0.275 0.647 0. 0.424]]\n" + ] + } + ], + "source": [ + "from numpy import set_printoptions\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "array = New_Train.values\n", + "\n", + "#seperating data into onput and output options\n", + "X = array[:,0:4]\n", + "Y = array[:,4]\n", + "scaler = MinMaxScaler(feature_range = (0,1))\n", + "rescaledX = scaler.fit_transform(X)\n", + "\n", + "#summarising transformed data\n", + "set_printoptions(precision = 3)\n", + "print(rescaledX[0:4,:])\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Standardising data\n", + "\n", + "\n", + "
\n", + " ## ??\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparing models to use\n", + "\n", + "\n", + "
\n", + " ## ??\n", + " You have less than 10 algorithms, why?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('LR', 0.798219298245614, 0.04658230982015574)\n", + "('LDA', 0.7955789473684212, 0.045052023551671226)\n", + "('KNN', 0.7985438596491228, 0.05940387146136468)\n", + "('CART', 0.7537807017543859, 0.04398756697084728)\n", + "('NB', 0.7996228070175438, 0.12333496511360942)\n", + "('SVM', 0.8071447368421053, 0.07573362330045245)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#comparing different models to from which ill choose.\n", + "from matplotlib import pyplot\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.svm import SVC\n", + "\n", + "\n", + "# load dataset\n", + "\n", + "array = New_Train.values\n", + "\n", + "#split the dataset \n", + "X = array[:,0:4] #X = Train.drop(columns=['CLASS'])\n", + "Y = array[:,4] #Y = Train['CLASS']\n", + "\n", + "# prepare models and add them to a list\n", + "models = []\n", + "models.append(('LR', LogisticRegression()))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('SVM', SVC()))\n", + "\n", + "# evaluate each model in turn\n", + "results = []\n", + "names = []\n", + "\n", + "for name, model in models:\n", + " kfold = KFold(n_splits=40, random_state=10)\n", + " cv_results = cross_val_score(model, X, Y, cv=kfold, scoring='accuracy')\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " msg = (name, cv_results.mean(), cv_results.std())\n", + " print(msg)\n", + "\n", + "# boxplot algorithm comparison\n", + "fig = pyplot.figure()\n", + "fig.suptitle('Algorithm Comparison')\n", + "ax = fig.add_subplot(111)\n", + "pyplot.boxplot(results)\n", + "ax.set_xticklabels(names)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "#now slitting data\n", + "#array = New_Train.values\n", + "X = New_Train.values[:,0:4]\n", + "Y = New_Train.values[:,4]\n", + "from sklearn.model_selection import train_test_split\n", + "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The result is: 62.28 Mathew's Coef\n" + ] + } + ], + "source": [ + "# also train and test the model on Matthews correlation coefficient.\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "GS = GaussianNB()\n", + "GS.fit(X_Train,Y_Train)\n", + "pred = GS.predict(X_Test)\n", + "\n", + "print(\"The result is: \",np.round(matthews_corrcoef(Y_Test,pred) *100,2),\" Mathew's Coef\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", + " multi_class='ovr', n_jobs=None, penalty='l2',\n", + " random_state=30, solver='liblinear', tol=0.0001, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#now creating a model and training it\n", + "model = LogisticRegression(solver='liblinear', C=0.05, multi_class='ovr', random_state=30)\n", + "model.fit(X_Train, Y_Train)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_Train, X_Test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2,\n", + "random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The result is: 62.28 Mathew's Coef\n" + ] + } + ], + "source": [ + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "nv = GaussianNB()\n", + "nv.fit(X_Train,Y_Train)\n", + "pred = nv.predict(X_Test)\n", + "\n", + "print(\"The result is: \",np.round(matthews_corrcoef(Y_Test,pred) *100,2),\" Mathew's Coef\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "#model = DecisionTreeClassifier()\n", + "\n", + "#model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + " ## ??\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ls: ../../kaggle/working/: No such file or directory\r\n" + ] + } + ], + "source": [ + "import os\n", + "os.listdir()\n", + "!ls ../../kaggle/working/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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/.ipynb_checkpoints/nsangi-olga-tendo-checkpoint.ipynb b/Assignment Colab/.ipynb_checkpoints/nsangi-olga-tendo-checkpoint.ipynb new file mode 100644 index 0000000..2d52262 --- /dev/null +++ b/Assignment Colab/.ipynb_checkpoints/nsangi-olga-tendo-checkpoint.ipynb @@ -0,0 +1,2441 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Please look out for these cells for corrections." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/kaggle/input/ace-class-assignment/Test.csv\n", + "/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\n" + ] + } + ], + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", + "# For example, here's several helpful packages to load in \n", + "\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "# Input data files are available in the \"../input/\" directory.\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "for dirname, _, filenames in os.walk('/kaggle/input'):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "# Any results you write to the current directory are saved as output.\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Naming the Training and test dataset using." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" + }, + "outputs": [], + "source": [ + "Train = pd.read_csv(\"../input/ace-class-assignment/AMP_TrainSet.csv\")\n", + "Test = pd.read_csv(\"../input/ace-class-assignment/Test.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Confirming that the data has been read in and assigned to variables Train and Test" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + " \n", + " ## ??\n", + " \n", + " When you see these: \n", + " - I want to know if you understand the concept\n", + " - Why are you applying the concept\n", + " - What did you learn?\n", + " - I want to see that you know what you are doing." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3038, 12)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Train.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Establishing the attributes in the train data set" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n", + " 'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n", + " 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n", + " 'CLASS'],\n", + " dtype='object')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Train.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Establishing the data types for each attribute in the train data set. This helps in finding out which column has data types like strings that may need to be converted to foating points or integers to represent categorical or ordinal values. This can be done using df.dtype method or the df.info() method as shown in the two cells below, tho the df.info() is more informative.\n", + "\n", + "
\n", + " \n", + " Why is everything so big? \n", + "

like this?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Train.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 3038 entries, 0 to 3037\n", + "Data columns (total 12 columns):\n", + "FULL_Charge 3038 non-null float64\n", + "FULL_AcidicMolPerc 3038 non-null float64\n", + "FULL_AURR980107 3038 non-null float64\n", + "FULL_DAYM780201 3038 non-null float64\n", + "FULL_GEOR030101 3038 non-null float64\n", + "FULL_OOBM850104 3038 non-null float64\n", + "NT_EFC195 3038 non-null int64\n", + "AS_MeanAmphiMoment 3038 non-null float64\n", + "AS_DAYM780201 3038 non-null float64\n", + "AS_FUKS010112 3038 non-null float64\n", + "CT_RACS820104 3038 non-null float64\n", + "CLASS 3038 non-null int64\n", + "dtypes: float64(10), int64(2)\n", + "memory usage: 284.9 KB\n" + ] + } + ], + "source": [ + "Train.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Statistical summary of the data in each column.\n", + "### This helps understand the distribution of each column as this method returns values like percentiles,standard deviation, minimum and maximum values of each attribute. This way you can also find out if you have negative values in any attribute. This also helps in understanding the scales at which each attribute is at." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104CLASS
count3038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.000000
mean2.0602378.5215200.97141073.6687600.994007-2.4329270.08854515.68323373.6508285.9113611.2352550.500000
std3.8199297.5866520.1074138.5274890.0313331.7072230.28413311.5756659.1660920.6936890.2100120.500082
min-16.0000000.0000000.68400042.7500000.866000-10.4320000.0000000.04100042.7780003.5330000.7850000.000000
25%0.0000002.5160000.89500068.2940000.974000-3.6060000.0000005.58750067.5560005.4592501.0820000.000000
50%2.0000007.1430000.96300074.0595000.994000-2.2965000.00000014.98850073.6970005.9255001.1840000.500000
75%4.00000013.1580001.04100079.3437501.011000-1.2832500.00000026.80775079.7780006.3820001.3510001.000000
max30.00000046.6670001.451000101.6820001.1960003.5760001.00000051.280000103.1670008.6620002.1920001.000000
\n", + "
" + ], + "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": [ + "Train.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From the values above, there are no missing values since all counts in each column is equal. We can also observe that there are negative values in some attributes have a minimum values in negatives.\n", + "\n", + "
\n", + " \n", + " ## ??" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Since this is a classification problem, its important to check out the propotions of the values in the class to be predicted as its possible one class may have more values as compared to the others.\n", + "\n", + "
\n", + " \n", + " ## ??" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Distirbution')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.groupby('CLASS').size().plot(kind='bar', color='Purple',edgecolor = 'black')\n", + "# Plot formatting\n", + "plt.legend(title = 'Class')\n", + "plt.title('Bar Plot to show distribution of class variable')\n", + "plt.xlabel('Class')\n", + "plt.ylabel('Distirbution')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CLASS\n", + "0 1519\n", + "1 1519\n", + "dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking propotions of attribute class\n", + "Train.groupby('CLASS').size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From above, the entries in the class attribute are equal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Establishing if there are any missing values in the attributes of the data set. This helps to give an insight on how to handle the missing values, whether to remove rows that have missing values or establishing a method like the mean,median ect to impute the NAs. \n", + "\n", + "
\n", + " \n", + " Good explanation." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FULL_Charge 0\n", + "FULL_AcidicMolPerc 0\n", + "FULL_AURR980107 0\n", + "FULL_DAYM780201 0\n", + "FULL_GEOR030101 0\n", + "FULL_OOBM850104 0\n", + "NT_EFC195 0\n", + "AS_MeanAmphiMoment 0\n", + "AS_DAYM780201 0\n", + "AS_FUKS010112 0\n", + "CT_RACS820104 0\n", + "CLASS 0\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Train.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Correlation between attributes.\n", + "## This is done to establish the relationship between variables and how they may change or may not change each other. The pearson's correlation method is going to be used since it assumes normal distribution on attributes involved. A value of 0 shows no correlation between the attributes, while a correlation of -1 or 1 shows full negative or positive correlation between variables." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CLASS0.534602-0.598816-0.584111-0.554838-0.260470-0.4532870.2607020.693552-0.4371680.0334320.2676521.000000
\n", + "
" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 \\\n", + "FULL_Charge 1.000000 -0.612996 -0.490977 \n", + "FULL_AcidicMolPerc -0.612996 1.000000 0.794796 \n", + "FULL_AURR980107 -0.490977 0.794796 1.000000 \n", + "FULL_DAYM780201 -0.434603 0.541481 0.548253 \n", + "FULL_GEOR030101 -0.058725 0.115201 0.346139 \n", + "FULL_OOBM850104 -0.283758 0.513344 0.462712 \n", + "NT_EFC195 0.088068 -0.143168 -0.169540 \n", + "AS_MeanAmphiMoment 0.355477 -0.431590 -0.426097 \n", + "AS_DAYM780201 -0.365374 0.449621 0.456260 \n", + "AS_FUKS010112 -0.090570 0.002334 0.032958 \n", + "CT_RACS820104 0.232929 -0.213543 -0.403599 \n", + "CLASS 0.534602 -0.598816 -0.584111 \n", + "\n", + " FULL_DAYM780201 FULL_GEOR030101 FULL_OOBM850104 \\\n", + "FULL_Charge -0.434603 -0.058725 -0.283758 \n", + "FULL_AcidicMolPerc 0.541481 0.115201 0.513344 \n", + "FULL_AURR980107 0.548253 0.346139 0.462712 \n", + "FULL_DAYM780201 1.000000 0.010118 0.334778 \n", + "FULL_GEOR030101 0.010118 1.000000 0.319157 \n", + "FULL_OOBM850104 0.334778 0.319157 1.000000 \n", + "NT_EFC195 -0.090058 -0.230417 -0.230561 \n", + "AS_MeanAmphiMoment -0.408793 -0.160269 -0.336297 \n", + "AS_DAYM780201 0.894191 -0.029085 0.275640 \n", + "AS_FUKS010112 0.055915 0.040480 -0.452769 \n", + "CT_RACS820104 -0.326792 -0.151935 0.155304 \n", + "CLASS -0.554838 -0.260470 -0.453287 \n", + "\n", + " NT_EFC195 AS_MeanAmphiMoment AS_DAYM780201 \\\n", + "FULL_Charge 0.088068 0.355477 -0.365374 \n", + "FULL_AcidicMolPerc -0.143168 -0.431590 0.449621 \n", + "FULL_AURR980107 -0.169540 -0.426097 0.456260 \n", + "FULL_DAYM780201 -0.090058 -0.408793 0.894191 \n", + "FULL_GEOR030101 -0.230417 -0.160269 -0.029085 \n", + "FULL_OOBM850104 -0.230561 -0.336297 0.275640 \n", + "NT_EFC195 1.000000 0.178683 -0.036844 \n", + "AS_MeanAmphiMoment 0.178683 1.000000 -0.322378 \n", + "AS_DAYM780201 -0.036844 -0.322378 1.000000 \n", + "AS_FUKS010112 0.145924 0.025580 0.045562 \n", + "CT_RACS820104 0.080898 0.171524 -0.256060 \n", + "CLASS 0.260702 0.693552 -0.437168 \n", + "\n", + " AS_FUKS010112 CT_RACS820104 CLASS \n", + "FULL_Charge -0.090570 0.232929 0.534602 \n", + "FULL_AcidicMolPerc 0.002334 -0.213543 -0.598816 \n", + "FULL_AURR980107 0.032958 -0.403599 -0.584111 \n", + "FULL_DAYM780201 0.055915 -0.326792 -0.554838 \n", + "FULL_GEOR030101 0.040480 -0.151935 -0.260470 \n", + "FULL_OOBM850104 -0.452769 0.155304 -0.453287 \n", + "NT_EFC195 0.145924 0.080898 0.260702 \n", + "AS_MeanAmphiMoment 0.025580 0.171524 0.693552 \n", + "AS_DAYM780201 0.045562 -0.256060 -0.437168 \n", + "AS_FUKS010112 1.000000 -0.445284 0.033432 \n", + "CT_RACS820104 -0.445284 1.000000 0.267652 \n", + "CLASS 0.033432 0.267652 1.000000 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Train.corr(method='pearson')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From the summary above, AS_DAYM780201 and FULL_DAYM780201 are the strongest positively correlated with a correlation of 0.894191 while FULL_AcidicMolPerc and FULL_Charge are the strongest negatively correlated attributes. This correlation can further be observed by plotting a heat map using seaborn to show the correlation of attributes with each other as shown below" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize= (10,10))\n", + "sns.heatmap(Train.corr(method = 'pearson'), cmap='Purples', robust=True, square=True, annot= True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using visualisation to understand the data at hand. Histograms, Density plots and Box and whisker plots are some of the plots used to understand the attribute distribution.\n", + "## Histograms were used.\n", + "\n", + "
\n", + " \n", + " ## ??" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using Histograms" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.hist(figsize=(10,10))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From the histograms above, we can observe that attributes AS_MeanAmphiMoment, AS_DAYM780201, AS_FUKS010112, FULL_DAYM780201, FULL_GEOR030101, FULL_AURR980107,FULL_CHARGE have a normal. We can also tell from these plots that attribute CT_RACS820104 and FULL_AcidicMolPerc are skewed to the right. We can also tell that attributes NT_EFC195 and CLASS are categorical attributes. This can also be observed using density plots as shown below.\n", + "\n", + "
\n", + " \n", + " ## ??" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using Density plots\n", + "### With the density plot, we can easily make comparisons between variables in each column because the plot is less cluttered." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.plot(kind='density', figsize=(10,10), subplots=True, layout=(4,3), sharex=False)\n", + "plt.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking for the skewness of the data. Since many machine learning algorithms assume normal distribution,this allows one to know what attributes need to be corrected of skewness to improve the accuracy of the model. Skewness can be observed with bar plots of .skew().\n", + "\n", + "
\n", + " Good explanation.\n", + " \n", + " So what did you findout?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.skew().plot(kind='bar', figsize=(10,6))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NT_EFC195\n", + "0 2769\n", + "1 269\n", + "dtype: int64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# From above, attribute NT_EFC195 shows that its, skewed.\n", + "Train.groupby('NT_EFC195').size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Separating the train data set into output and input components" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "A = Train.values\n", + "X = A[:, 0:11]\n", + "Y = A[:,11]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spot checking classification algorithms since its a classification problem at hand.\n", + "### This is done to know which algorithm is best suited for the problem at hand. Two linear machine learning algorithms(Logistic regression and Linear discriminant analysis) and four non-linear machine learning algorithm were used(k-nearest neighbors, naive bayes, support vector machines and classific regression tress. \n", + "\n", + "
\n", + " \n", + " ## Less than 10 algorithms!!!" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('LR', 0.8342865299822478)\n", + "('LDA', 0.8377162908048379)\n", + "('KNN', 0.8690586462107053)\n", + "('CART', 1.0)\n", + "('NB', 0.8407203694376205)\n", + "('SVM', 0.8187103751493263)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from pandas import read_csv\n", + "from matplotlib import pyplot\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "#split the dataset \n", + "#A = Train.values\n", + "# Separating A into output and input components\n", + "X = A[:, 0:11]\n", + "Y = A[:, 11]\n", + "# prepare models and add them to a list\n", + "models = []\n", + "models.append(('LR', LogisticRegression()))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('SVM', SVC()))\n", + "#print(models)\n", + "\n", + "# evaluate each model in turn\n", + "results = []\n", + "names = []\n", + "scoring = 'accuracy'\n", + "\n", + "for name, model in models:\n", + " kfold = KFold(n_splits=20, random_state=7)\n", + " cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " model.fit(X, Y)\n", + "\n", + " Prediction = model.predict(X)\n", + " msg = (name, matthews_corrcoef(Y, Prediction))\n", + " print(msg)\n", + "\n", + "# boxplot algorithm comparison\n", + "fig = pyplot.figure()\n", + "fig.suptitle('Algorithm Comparison')\n", + "ax = fig.add_subplot(111)\n", + "pyplot.boxplot(results)\n", + "ax.set_xticklabels(names)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From these results, it would suggest that both DecisionTreeClassifier, KNeighborsClassifier and GaussianNB are worthy of further study on this problem." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preparation.\n", + "### This is important because this finds a way to best expose the structure of the problem to machine learning algorithim intended to be used. Since the data has attributes of varying scales, its important to use one of the methods for data preparation like rescaling, standardization, normalization or binarization for better prediction. Here, rescaling is used to have all the attribute on the same scale." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.457 0. 0.348 0.545 0.33 0.483 0. 0.005 0.508 0.415 0.182]\n", + " [0.435 0.116 0.322 0.489 0.276 0.458 1. 0.011 0.421 0.586 0.475]\n", + " [0.467 0.116 0.246 0.523 0.288 0.565 0. 0.011 0.442 0.273 0.666]\n", + " [0.457 0.089 0.275 0.399 0.403 0.647 0. 0.011 0.405 0.153 0.424]\n", + " [0.511 0.183 0.323 0.373 0.342 0.595 0. 0.011 0.5 0.196 0.536]]\n" + ] + } + ], + "source": [ + "from numpy import set_printoptions\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "A = Train.values\n", + "# Separating A into output and input components\n", + "X = A[:, 0:11]\n", + "Y = A[:, 11]\n", + "scaler = MinMaxScaler(feature_range= (0,1))\n", + "rescaledX = scaler.fit_transform(X)\n", + "\n", + "# Summary of the transformed data\n", + "set_printoptions(precision = 3) # This sets the number of floating point output after rescaling the data\n", + "print(rescaledX[0:5, :]) # This is used as check to confirm that the data has been rescaled" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Standardization\n", + "### It is a useful technique to transform attributes with a Gaussian distribution and differing means and standard deviations to a standard Gaussian distribution with a mean of 0 and a standard deviation of 1" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 7.697e-01 -1.123e+00 -1.900e-01 1.376e-01 -6.067e-01 -7.206e-01\n", + " -3.117e-01 -1.331e+00 -2.257e-02 -3.610e-01 -9.251e-01]\n", + " [ 5.079e-01 -4.109e-01 -3.763e-01 -2.432e-01 -1.181e+00 -9.245e-01\n", + " 3.208e+00 -1.303e+00 -5.924e-01 9.021e-01 1.037e+00]\n", + " [ 9.006e-01 -4.109e-01 -9.163e-01 -8.651e-03 -1.054e+00 -4.632e-02\n", + " -3.117e-01 -1.304e+00 -4.590e-01 -1.409e+00 2.318e+00]\n", + " [ 7.697e-01 -5.741e-01 -7.115e-01 -8.701e-01 1.594e-01 6.274e-01\n", + " -3.117e-01 -1.302e+00 -7.015e-01 -2.300e+00 6.989e-01]\n", + " [ 1.424e+00 2.041e-03 -3.670e-01 -1.050e+00 -4.790e-01 2.003e-01\n", + " -3.117e-01 -1.302e+00 -7.713e-02 -1.977e+00 1.447e+00]]\n" + ] + } + ], + "source": [ + "from numpy import set_printoptions\n", + "from sklearn.preprocessing import StandardScaler\n", + "B= Train.values\n", + "#Separating the array into intput and output components\n", + "X = B[:, 0:11]\n", + "Y = B[:, 11]\n", + "scaler2 = StandardScaler()\n", + "standardizedX = scaler2.fit_transform(X)\n", + "# A portion of the transformed data\n", + "set_printoptions(precision = 3) # This sets the number of floating point output after rescaling the data\n", + "print(standardizedX[0:5,:]) # This is used as check to confirm that the data has been rescaled" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature selection\n", + "### Statistical tests can be used to select those features that have the strongest relationship with the output variable. Feature selection is done on non transformed data using select k best using he univariate statistic F was used since it can work best with data containg negative values as opposed to chi2 that doesnt take in negative values." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featuresscorespvalue
7AS_MeanAmphiMoment2813.8681780.000000e+00
1FULL_AcidicMolPerc1697.2495654.220004e-295
2FULL_AURR9801071572.2806771.887905e-277
3FULL_DAYM7802011350.3007436.997934e-245
0FULL_Charge1214.9028383.404241e-224
5FULL_OOBM850104785.1247987.441087e-154
8AS_DAYM780201717.3174084.911002e-142
10CT_RACS820104234.2741975.329249e-51
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" + ], + "text/plain": [ + " features scores 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", + "10 CT_RACS820104 234.274197 5.329249e-51" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest, f_classif\n", + "from numpy import set_printoptions\n", + "\n", + "#Feature Extraction\n", + "bestfeat = SelectKBest(score_func=f_classif, k=8)\n", + "fit = bestfeat.fit(X,Y)\n", + "\n", + "set_printoptions(precision=3) # This sets the number of floating point output after rescaling the data\n", + "selected_features = fit.transform(X)\n", + "\n", + "scores = pd.DataFrame(fit.scores_)\n", + "pvalues = pd.DataFrame(fit.pvalues_)\n", + "columns = pd.DataFrame(Train.columns[0:11])\n", + "\n", + "selected_features = pd.concat([columns,scores, pvalues,], axis=1)\n", + "selected_features.columns = ['features', 'scores', 'pvalue']\n", + "selected_features.nlargest(8, \"scores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SelectKBest using the f statistic, gives scores to each attribute and takes the specified number of attributes with the highest scores. Attributes AS_MeanAmphiMoment, FULL_AcidicMolPerc, FULL_AURR980107, FULL_DAYM780201, FULL_Charge, FULL_OOBM850104, AS_DAYM780201 and CT_RACS820104 were the selected features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chi2 statistical test was used to since there were no negative values in the attributes after rescaling with the minmax scaler." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featuresscorespvalue
7AS_MeanAmphiMoment244.2277284.708742e-55
6NT_EFC195188.1970267.868482e-43
1FULL_AcidicMolPerc157.6175133.751602e-36
2FULL_AURR98010754.2314481.782118e-13
3FULL_DAYM78020137.3117801.006747e-09
8AS_DAYM78020126.1562243.148806e-07
5FULL_OOBM85010416.2314335.605627e-05
0FULL_Charge15.2452499.441397e-05
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" + ], + "text/plain": [ + " features scores pvalue\n", + "7 AS_MeanAmphiMoment 244.227728 4.708742e-55\n", + "6 NT_EFC195 188.197026 7.868482e-43\n", + "1 FULL_AcidicMolPerc 157.617513 3.751602e-36\n", + "2 FULL_AURR980107 54.231448 1.782118e-13\n", + "3 FULL_DAYM780201 37.311780 1.006747e-09\n", + "8 AS_DAYM780201 26.156224 3.148806e-07\n", + "5 FULL_OOBM850104 16.231433 5.605627e-05\n", + "0 FULL_Charge 15.245249 9.441397e-05" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "#Feature Extraction\n", + "test = SelectKBest(score_func=chi2, k=8)\n", + "fit = test.fit(rescaledX,Y)\n", + "\n", + "# Summary of scores \n", + "set_printoptions(precision=3) # This sets the number of floating point output after rescaling the data\n", + "#print(fit.scores_)\n", + "selected_features1 = fit.transform(rescaledX)\n", + "\n", + "scores = pd.DataFrame(fit.scores_)\n", + "pvalues = pd.DataFrame(fit.pvalues_)\n", + "columns = pd.DataFrame(Train.columns[0:11])\n", + "\n", + "selected_features1 = pd.concat([columns,scores, pvalues,], axis=1)\n", + "selected_features1.columns = ['features', 'scores', 'pvalue']\n", + "selected_features1.nlargest(8, \"scores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature selection using the recursive feature selection method with LG" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'rescaledX2' 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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mrfe\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mRFE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrfe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrescaledX2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mselectedfeaturesRFE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrescaledX2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msupport_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'rescaledX2' is not defined" + ] + } + ], + "source": [ + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "model = LogisticRegression()\n", + "rfe= RFE(model,5)\n", + "fit = rfe.fit(rescaledX2, Y)\n", + "selectedfeaturesRFE = rescaledX2[:, fit.support_]\n", + "\n", + "print(\"Num_Feature:\", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking:\", fit.ranking_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating Machine learning Alogorithms\n", + "## K-fold Cross Validation\n", + "### Cross validation is an approach that you can use to estimate the performance of a machine learning algorithm with less variance than a single train-test set split. It works by splitting the dataset into k-parts (e.g. k = 5 or k = 10). Each split of the data is called a fold. The algorithm is trained on k1 folds with one held back and tested on the held back fold. This is repeated so that each fold of the dataset is given a chance to be the held back test set. The choice of k must allow the size of each test partition to be large enough to be a reasonable sample of the problem, whilst allowing enough repetitions of the train-test evaluation of the algorithm to provide a fair estimate of the algorithms performance on unseen data. For modest sized datasets in the thousands or tens of thousands of records, k values of 3, 5 and 10 are common. In the example below we use 10-fold cross validation." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'selected_features_train' 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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_folds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mmodel11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmodel11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselected_features_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselected_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'selected_features_train' is not defined" + ] + } + ], + "source": [ + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "num_folds = 10\n", + "seed = 11\n", + "kfold = KFold(n_splits=num_folds, random_state=seed)\n", + "model11 = LogisticRegression()\n", + "model11.fit(selected_features_train, Y_train)\n", + "\n", + "results = cross_val_score(model11, selected_features, Y, cv=kfold)\n", + "Prediction = model11.predict(selected_features_train)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (results.mean()*100.0))\n", + "#Assigning the preditions of the model to variable AssignmentOutPut\n", + "AssignmentOutPut11 = model11.predict(Test.values) \n", + "#Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut11 = pd.DataFrame(AssignmentOutPut11)\n", + "\n", + "AssignmentOutPut11.columns = ['Class'] # Naming the output column\n", + "AssignmentOutPut11.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut11['Class'] = AssignmentOutPut11['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut11.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut11.groupby('Class').size()[1].sum())\n", + "\n", + "AssignmentOutPut11.to_csv('AssignmentOutPut1.csv') # Writing AssignmentOutPut1 to a csv file" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'selected_features_train' 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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_folds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mmodel22\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGaussianNB\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmodel22\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselected_features_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel22\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselected_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'selected_features_train' is not defined" + ] + } + ], + "source": [ + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.naive_bayes import GaussianNB\n", + "\n", + "num_folds = 10\n", + "seed = 22\n", + "kfold = KFold(n_splits=num_folds, random_state=seed)\n", + "model22 = GaussianNB()\n", + "model22.fit(selected_features_train, Y_train)\n", + "\n", + "results = cross_val_score(model22, selected_features, Y, cv=kfold)\n", + "Prediction = model22.predict(selected_features_train)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (results.mean()*100.0))\n", + "\n", + "#Assigning the preditions of the model to variable AssignmentOutPut\n", + "AssignmentOutPut22 = model22.predict(Test.values) \n", + "#Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut22 = pd.DataFrame(AssignmentOutPut22)\n", + "\n", + "AssignmentOutPut22.columns = ['Class'] # Naming the output column\n", + "AssignmentOutPut22.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut22['Class'] = AssignmentOutPut22['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut22.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut22.groupby('Class').size()[1].sum())\n", + "\n", + "AssignmentOutPut22.to_csv('AssignmentOutPut22.csv') # Writing AssignmentOutPut1 to a csv file" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'selected_features_train' 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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_folds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mmodel33\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmodel33\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselected_features_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel33\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselected_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'selected_features_train' is not defined" + ] + } + ], + "source": [ + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "num_folds = 10\n", + "seed = 33\n", + "kfold = KFold(n_splits=num_folds, random_state=seed)\n", + "model33 = KNeighborsClassifier()\n", + "model33.fit(selected_features_train, Y_train)\n", + "\n", + "results = cross_val_score(model33, selected_features, Y, cv=kfold)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (results.mean()*100.0))\n", + "\n", + "#Assigning the preditions of the model to variable AssignmentOutPut\n", + "AssignmentOutPut33 = model33.predict(Test.values) \n", + "#Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut33 = pd.DataFrame(AssignmentOutPut33)\n", + "\n", + "AssignmentOutPut33.columns = ['Class'] # Naming the output column\n", + "AssignmentOutPut33.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut33['Class'] = AssignmentOutPut33['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut33.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut33.groupby('Class').size()[1].sum())\n", + "\n", + "AssignmentOutPut33.to_csv('AssignmentOutPut33.csv') # Writing AssignmentOutPut1 to a csv file" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'selected_features_train' 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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_folds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mmodel44\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmodel44\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselected_features_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel44\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselected_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'selected_features_train' is not defined" + ] + } + ], + "source": [ + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "num_folds = 10\n", + "seed = 44\n", + "kfold = KFold(n_splits=num_folds, random_state=seed)\n", + "model44 = DecisionTreeClassifier()\n", + "model44.fit(selected_features_train, Y_train)\n", + "\n", + "results = cross_val_score(model44, selected_features, Y, cv=kfold)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (results.mean()*100.0))\n", + "\n", + "#Assigning the preditions of the model to variable AssignmentOutPut\n", + "AssignmentOutPut44 = model44.predict(Test.values) \n", + "#Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut44 = pd.DataFrame(AssignmentOutPut44)\n", + "\n", + "AssignmentOutPut44.columns = ['Class'] # Naming the output column\n", + "AssignmentOutPut44.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut44['Class'] = AssignmentOutPut44['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut44.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut44.groupby('Class').size()[1].sum())\n", + "\n", + "AssignmentOutPut44.to_csv('AssignmentOutPut44.csv') # Writing AssignmentOutPut1 to a csv file" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'selected_features_train' 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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_folds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mmodel55\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmodel55\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselected_features_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel55\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselected_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'selected_features_train' is not defined" + ] + } + ], + "source": [ + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.svm import SVC\n", + "\n", + "num_folds = 55\n", + "seed = 6\n", + "kfold = KFold(n_splits=num_folds, random_state=seed)\n", + "model55 = SVC()\n", + "model55.fit(selected_features_train, Y_train)\n", + "\n", + "results = cross_val_score(model55, selected_features, Y, cv=kfold)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (results.mean()*100.0))\n", + "\n", + "#Assigning the preditions of the model to variable AssignmentOutPut\n", + "AssignmentOutPut55 = model55.predict(Test.values) \n", + "#Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut55 = pd.DataFrame(AssignmentOutPut55)\n", + "\n", + "AssignmentOutPut55.columns = ['Class'] # Naming the output column\n", + "AssignmentOutPut55.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut55['Class'] = AssignmentOutPut55['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut55.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut55.groupby('Class').size()[1].sum())\n", + "\n", + "AssignmentOutPut55.to_csv('AssignmentOutPut55.csv') # Writing AssignmentOutPut1 to a csv file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating Machine learning Alogorithms\n", + "## Split into Train and Test set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Non rescaled data" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MCC: 0.8283116428616907\n", + "Accuracy: 91.92422731804587\n", + "387\n", + "371\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import matthews_corrcoef\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "A = Train.values\n", + "# Separating A into output and input components\n", + "X = A[:, 0:11]\n", + "Y = A[:, 11]\n", + "test_size = 0.33\n", + "seed = 1\n", + "selected_features_train, selected_features_test, Y_train, Y_test = train_test_split(X, Y, test_size = test_size, random_state = seed)\n", + "my_model1 = LogisticRegression()\n", + "my_model1.fit(selected_features_train, Y_train)\n", + "result = my_model1.score(selected_features_test, Y_test)\n", + "\n", + "Prediction = my_model1.predict(selected_features_train)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (result*100.0))\n", + "\n", + "#Assigning the preditions of the model to variable AssignmentOutPut\n", + "AssignmentOutPut1 = my_model1.predict(Test.values) \n", + "#Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut1 = pd.DataFrame(AssignmentOutPut1)\n", + "\n", + "AssignmentOutPut1.columns = ['Class'] # Naming the output column\n", + "AssignmentOutPut1.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut1['Class'] = AssignmentOutPut1['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut1.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut1.groupby('Class').size()[1].sum())\n", + "\n", + "AssignmentOutPut1.to_csv('AssignmentOutPut1.csv') # Writing AssignmentOutPut1 to a csv file" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MCC: 0.8566012373350099\n", + "Accuracy: 90.62811565304088\n", + "397\n", + "361\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import matthews_corrcoef\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "A = Train.values\n", + "# Separating A into output and input components\n", + "X = A[:, 0:11]\n", + "Y = A[:, 11]\n", + "test_size = 0.33\n", + "seed = 2\n", + "selected_features_train, selected_features_test, Y_train, Y_test = train_test_split(X, Y, test_size = test_size, random_state = seed)\n", + "my_model2 = KNeighborsClassifier()\n", + "my_model2.fit(selected_features_train, Y_train)\n", + "result = my_model2.score(selected_features_test, Y_test)\n", + "\n", + "Prediction = my_model2.predict(selected_features_train)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (result*100.0))\n", + "\n", + "#Assigning the preditions of the model to variable AssignmentOutPut\n", + "AssignmentOutPut2 = my_model2.predict(Test.values) \n", + "#Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut2 = pd.DataFrame(AssignmentOutPut2)\n", + "# Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut2.columns = ['Class'] # Naming the out\n", + "AssignmentOutPut2.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut2['Class'] = AssignmentOutPut2['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut2.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut2.groupby('Class').size()[1].sum())\n", + "\n", + "AssignmentOutPut2.to_csv('AssignmentOutPut2.csv') # Writing AssignmentOutPut1 to a csv file" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MCC: 1.0\n", + "Accuracy: 90.72781655034895\n", + "387\n", + "371\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import matthews_corrcoef\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "A = Train.values\n", + "# Separating A into output and input components\n", + "X = A[:, 0:11]\n", + "Y = A[:, 11]\n", + "test_size = 0.33\n", + "seed = 3\n", + "selected_features_train, selected_features_test, Y_train, Y_test = train_test_split(X, Y, test_size = test_size, random_state = seed)\n", + "my_model3 = DecisionTreeClassifier()\n", + "my_model3.fit(selected_features_train, Y_train)\n", + "result = my_model3.score(selected_features_test, Y_test)\n", + "\n", + "Prediction = my_model3.predict(selected_features_train)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (result*100.0))\n", + "AssignmentOutPut3 = my_model3.predict(Test.values) #Assigning the preditions of the model to variable AssignmentOutPut\n", + "\n", + "AssignmentOutPut3 = pd.DataFrame(AssignmentOutPut3)\n", + "# Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut3.columns = ['Class'] # Naming the out\n", + "AssignmentOutPut3.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut3['Class'] = AssignmentOutPut3['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut3.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut3.groupby('Class').size()[1].sum())\n", + "\n", + "AssignmentOutPut1.to_csv('AssignmentOutPut3.csv') # Writing AssignmentOutPut1 to a csv file\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MCC: 0.8417648773342619\n", + "Accuracy: 91.92422731804587\n", + "369\n", + "389\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import matthews_corrcoef\n", + "from sklearn.naive_bayes import GaussianNB\n", + "\n", + "A = Train.values\n", + "# Separating A into output and input components\n", + "X = A[:, 0:11]\n", + "Y = A[:, 11]\n", + "test_size = 0.33\n", + "seed = 4\n", + "selected_features_train, selected_features_test, Y_train, Y_test = train_test_split(X, Y, test_size = test_size, random_state = seed)\n", + "my_model4 = GaussianNB()\n", + "my_model4.fit(selected_features_train, Y_train)\n", + "result = my_model4.score(selected_features_test, Y_test)\n", + "\n", + "Prediction = my_model4.predict(selected_features_train)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (result*100.0))\n", + "\n", + "AssignmentOutPut4 = my_model4.predict(Test.values) #Assigning the preditions of the model to variable AssignmentOutPut\n", + "\n", + "AssignmentOutPut4 = pd.DataFrame(AssignmentOutPut4)\n", + "# Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut4.columns = ['Class'] # Naming the out\n", + "AssignmentOutPut4.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut4['Class'] = AssignmentOutPut4['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "AssignmentOutPut4.to_csv('AssignmentOutPut4.csv') # Writing AssignmentOutPut1 to a csv file\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut4.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut4.groupby('Class').size()[1].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MCC: 0.8164151115601915\n", + "Accuracy: 91.12662013958126\n", + "405\n", + "353\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import matthews_corrcoef\n", + "from sklearn.svm import SVC\n", + "\n", + "A = Train.values\n", + "# Separating A into output and input components\n", + "X = A[:, 0:11]\n", + "Y = A[:, 11]\n", + "test_size = 0.33\n", + "seed = 5\n", + "selected_features_train, selected_features_test, Y_train, Y_test = train_test_split(X, Y, test_size = test_size, random_state = seed)\n", + "my_model5 = SVC()\n", + "my_model5.fit(selected_features_train, Y_train)\n", + "result = my_model5.score(selected_features_test, Y_test)\n", + "\n", + "Prediction = my_model5.predict(selected_features_train)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (result*100.0))\n", + "AssignmentOutPut5 = my_model5.predict(Test.values) #Assigning the preditions of the model to variable AssignmentOutPut\n", + "\n", + "AssignmentOutPut5 = pd.DataFrame(AssignmentOutPut5)\n", + "# Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut5.columns = ['Class'] # Naming the out\n", + "AssignmentOutPut5.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut5['Class'] = AssignmentOutPut5['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "AssignmentOutPut1.to_csv('AssignmentOutPut5.csv') # Writing AssignmentOutPut1 to a csv file\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut5.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut5.groupby('Class').size()[1].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MCC: 0.8376165100283083\n", + "Accuracy: 91.72482552342971\n", + "403\n", + "355\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "A = Train.values\n", + "# Separating A into output and input components\n", + "X = A[:, 0:11]\n", + "Y = A[:, 11]\n", + "test_size = 0.33\n", + "seed = 6\n", + "selected_features_train, selected_features_test, Y_train, Y_test = train_test_split(X, Y, test_size = test_size, random_state = seed)\n", + "my_model6 = LinearDiscriminantAnalysis()\n", + "my_model6.fit(selected_features_train, Y_train)\n", + "result = my_model6.score(selected_features_test, Y_test)\n", + "\n", + "Prediction = my_model6.predict(selected_features_train)\n", + "print(\"MCC:\", (matthews_corrcoef(Y_train, Prediction)))\n", + "print(\"Accuracy:\", (result*100.0))\n", + "AssignmentOutPut6 = my_model6.predict(Test.values) #Assigning the preditions of the model to variable AssignmentOutPut\n", + "\n", + "AssignmentOutPut6 = pd.DataFrame(AssignmentOutPut6)\n", + "# Converting AssignmentOutPut to a dataframe since its output is an array\n", + "AssignmentOutPut6.columns = ['Class'] # Naming the out\n", + "AssignmentOutPut6.index.name = \"Index\" #Creating a column called index\n", + "AssignmentOutPut6['Class'] = AssignmentOutPut6['Class'].map({0.0:False, 1.0:True}) # Converting 0.0 to false and 1.0 to true\n", + "\n", + "AssignmentOutPut6.to_csv('AssignmentOutPut6.csv') # Writing AssignmentOutPut1 to a csv file\n", + "#Printing the number of False and Trues\n", + "print(AssignmentOutPut6.groupby('Class').size()[0].sum())\n", + "print(AssignmentOutPut6.groupby('Class').size()[1].sum())" + ] + } + ], + "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/Jupiter Marina.ipynb b/Assignment Colab/Jupiter Marina.ipynb new file mode 100644 index 0000000..a2004e3 --- /dev/null +++ b/Assignment Colab/Jupiter Marina.ipynb @@ -0,0 +1,2467 @@ +<<<<<<< HEAD +{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport sklearn\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Importing data"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"# Importing data\ntest_data = pd.read_csv(\"/kaggle/input/ace-class-assignment/Test.csv\", header=0, sep=\",\")\namp_train = pd.read_csv(\"/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\", header=0, sep=\",\")\namp_train.head()\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_data.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## General data attributes and descriptive statistics"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Describing general data attributes\ntrain_dim=amp_train.shape #gets the dimensions of the tranining dataset\ntest_dim= test_data.shape #gets the dimensions of the tesing dataset\nprint(train_dim)\nprint(test_dim)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"We can see that the test data has one attribute less than the train data set"},{"metadata":{"trusted":true},"cell_type":"code","source":"atr_types=amp_train.dtypes #gets the data type for each column in training dataset\n\natr_types\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"amp_train.isnull().sum() # gets the sum of the missing values/observations in each column","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":" All attributes have complete observations"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Descriptive statistics \nstats=amp_train.describe()\nstats","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"From the summary statistics, its visible that some of the attributes like FULL_CHARGE, FULL_AcidicMolPerc and\tFULL_OOBM850104 have outlier values basing on the values of the 75% quartile and maximum values "},{"metadata":{},"cell_type":"markdown","source":"# Class distributions\n\nSometimes, observations from the different classes may not be equal, these may need special handling, so its important to note the class distributions, as if they are uneven, they may bias our model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# CLass distibutions \ncdists= amp_train.groupby('CLASS').size() # groups the observation by the column of class, and counts all the observations including null values\nprint(cdists)\n\n#plots to visualize the class distribution\namp_train.groupby('CLASS').size().plot(kind=\"pie\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The observations from each class seem8 to be equal"},{"metadata":{},"cell_type":"markdown","source":"# Correlation between the different attributes\nBasically, we need to examine our attributes for correlation as highly correlated attributes present the same data to the algorithm. Removing one may speed learning of the algorithm (less dimensions more speed) and also prevent overfitting of the algorithm"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Correlation between attributes \ncorrelations = amp_train.corr(method=\"pearson\") # calculates pairwaise correlation for the attributes \ncorrelations\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"FULL_AURR980107 and FULL_AcidicMolPerc have a correlation coefficent of 0.79, this might mean these two attributes may be some what correlated. Same goes for AS_DAYM780201 and FULL_DAYM780201 (correlation coefficient is 0.894191)"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Plots to visualize the correlations\nfrom matplotlib import rcParams\nsns.heatmap(correlations, annot=True)\nrcParams['figure.figsize'] = (11.7,8.27)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Data skewness\n\nMost machine learning algorithms assume that data from the attributes is normally distributed, so identifying attributes with data that has a left or right skew, can be important as this can be easily corrected"},{"metadata":{"trusted":true},"cell_type":"code","source":"sk = amp_train.skew() # calculates the skew for each attribute\nsk","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Negative values like those of FULL_DAYM780201, FULL_OOBM850104,AS_DAYM780201, AS_FUKS010112, indicate that these attributes have values skewed more to the left\nValues closer to zero are indicative of a less or no skew, while positive values would mean that that particular attribute is skewed more to the right"},{"metadata":{},"cell_type":"markdown","source":" # Visualising data distributions with plots"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Plot one, histogram to show the distributions\namp_train.hist(layout=(4,3), figsize=(16,16))\nplt.subplots_adjust(wspace=0.4, hspace=0.5)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Plot two density plots can show the distributions better\namp_train.plot(kind=\"density\",subplots=True,layout=(4,3),sharex=False,sharey=False, figsize=(16,16))\n#amp_train.plot(kind=\"density\",subplots=False,layout=(4,3),sharex=True,sharey=True)\nplt.show()\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Distributions for AS_MeanAmphiMoment,FULL_AcidicMolPerc and NT_EFC195 dont look so good"},{"metadata":{},"cell_type":"markdown","source":"# Data transformations\nUsing squaring to transform the attributes with negative values into postives"},{"metadata":{"trusted":true},"cell_type":"code","source":"\nneg1=amp_train.iloc[:,0]\ntneg1=neg1**2\ntneg1.plot(kind=\"density\")\n\namp_train.iloc[:,0]=tneg1\n\nneg2=amp_train.iloc[:,5]\ntneg2=neg2**2\ntneg2.plot(kind=\"density\")\namp_train.iloc[:,5]=tneg2\n\n## test\nneg3=test_data.iloc[:,0]\ntneg3=neg3**2\ntneg3.plot(kind=\"density\")\ntest_data.iloc[:,0]=tneg3\n\n\n\nneg4=test_data.iloc[:,5]\ntneg4=neg4**2\ntneg4.plot(kind=\"density\")\ntest_data.iloc[:,5]=tneg4","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Feature selection\n\n\n"},{"metadata":{"trusted":true},"cell_type":"code","source":" #test one selecting out features with low variance\nfrom sklearn.feature_selection import VarianceThreshold\nsel = VarianceThreshold(threshold=(.8 * (1 - .8)))\nk=sel.fit_transform(amp_train)\nnames=amp_train.columns[sel.get_support(indices=True)]\nprint(k.shape)\namp_train2=pd.DataFrame(k,columns=names)\namp_train2.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"After selecting out features with low variance, only 8 attribute remained "},{"metadata":{"trusted":true},"cell_type":"code","source":"#test two using sklearn with a chi square test\n\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import chi2\n\ntest_array = amp_train2.values\n\ntest_atr = test_array[:,0:7]\nclass_atr = test_array[:,7]\n\n\ntest = SelectKBest(score_func=chi2, k=5)\nfit = test.fit(test_atr, class_atr)\n\n\nprint(fit.scores_)\nfeatures = fit.transform(test_atr)\nnames2=amp_train2.columns[fit.get_support(indices=True)]\namp_train3=pd.DataFrame(features,columns=names2)\namp_train3.iloc[:5,]\nprint(names2)\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Test three Recursive feature elimination\n\nfrom sklearn.feature_selection import RFE\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\n\nmodel1 = LogisticRegression()\nmodel2=RandomForestClassifier()\n\nrfe = RFE(model1, 5)\nfit2 = rfe.fit(test_atr, class_atr)\nfeatures2 = fit2.transform(test_atr)\n\nrfe2= RFE(model2, 5)\nfit3 = rfe2.fit(test_atr, class_atr)\n\n\n\n\nnames3= set(amp_train2.columns[fit2.get_support(indices=True)])\n\nnames4= set(amp_train2.columns[fit3.get_support(indices=True)])\n\nnames2=set(names2)\n\ncomn=names2&names3&names4 ### identifies features common to all the feature selection methods used\ncomn\n\nimport collections\n\ncomn=collections.deque(comn)\ncomn.appendleft(\"FULL_Charge\")\ncomn\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Getting the indices of the chosen attributes"},{"metadata":{"trusted":true},"cell_type":"code","source":"\nindice=[]\n\nfor index,name in enumerate(amp_train.columns):\n for sub_name in comn:\n if name==sub_name:\n indice.append(index)\nindice\n ","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Subsetting the data for the chosen attributes "},{"metadata":{"trusted":true},"cell_type":"code","source":"amp_train = pd.read_csv(\"/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\", header=0, sep=\",\")\n\n\ntest_amp =amp_train.iloc[:,[0,1,3,5,7,11]]\n\ntest_amp.head()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import confusion_matrix\n\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom sklearn.neighbors import KNeighborsClassifier\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Logistic regression using cross validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"\nX=test_amp.iloc[:,0:5]\nY=test_amp.iloc[:,5]\nfolds=10\nkfold = KFold(n_splits=folds, random_state=7, shuffle=True,)\nmodel = LogisticRegression()\nresults = cross_val_score(model, X, Y, cv=kfold)\n\nprint((\"Accuracy: %.3f%% (%.3f%%)\") % (results.mean()*100.0, results.std()*100.0))\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model one\n ## Logistic regression with a test train split using 5 features\n "},{"metadata":{"trusted":true},"cell_type":"code","source":"test_amp =amp_train.values[:,[0,1,3,5,7,11]]\nfrom sklearn.metrics import matthews_corrcoef\nX=test_amp[:,0:5]\nY=test_amp[:,5]\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel1 = LogisticRegression()\nmodel1.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model1.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult1 = model1.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result1*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model1.predict(X_test),Y_test))\nmcc1=matthews_corrcoef(model1.predict(X_test),Y_test)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":" # Model two\n## Gausian/Naive Bayes using five features "},{"metadata":{"trusted":true},"cell_type":"code","source":"test_amp =amp_train.values[:,[0,1,3,5,7,11]]\nfrom sklearn.metrics import matthews_corrcoef\nX=test_amp[:,0:5]\nY=test_amp[:,5]\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel2 = GaussianNB()\nmodel2.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model2.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult2 = model2.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result2*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model2.predict(X_test),Y_test))\nmcc2=matthews_corrcoef(model2.predict(X_test),Y_test)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model three\n## Linear Discriminant Analysis using five features "},{"metadata":{"trusted":true},"cell_type":"code","source":"test_amp =amp_train.values[:,[0,1,3,5,7,11]]\nfrom sklearn.metrics import matthews_corrcoef\nX=test_amp[:,0:5]\nY=test_amp[:,5]\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel3=LinearDiscriminantAnalysis()\nmodel3.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model3.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult3 = model3.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result3*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model3.predict(X_test),Y_test))\n\nmcc3=matthews_corrcoef(model3.predict(X_test),Y_test)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model four \n ## Support Vector Machines with five features"},{"metadata":{"trusted":true},"cell_type":"code","source":"test_amp =amp_train.values[:,[0,1,3,5,7,11]]\nfrom sklearn.metrics import matthews_corrcoef\nX=test_amp[:,0:5]\nY=test_amp[:,5]\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel4=SVC()\nmodel4.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model4.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult4 = model4.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result4*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model4.predict(X_test),Y_test))\n\n\nmcc4=matthews_corrcoef(model4.predict(X_test),Y_test)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# model 5\n ## K-Nearest neighbours with five features\n "},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier\nmodel5 = KNeighborsClassifier(n_neighbors=5)\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel5.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model5.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult5 = model5.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result5*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model5.predict(X_test),Y_test))\n\nmcc5=matthews_corrcoef(model5.predict(X_test),Y_test)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model six\n ## Centroid clasifier "},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.neighbors import NearestCentroid\nmodel6 = NearestCentroid()\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel6.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model6.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult6 = model6.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result6*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model6.predict(X_test),Y_test))\n\nmcc6=matthews_corrcoef(model6.predict(X_test),Y_test)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model seven\n\n## Decision trees with five features \n"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn import tree\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel7=tree.DecisionTreeClassifier()\nmodel7.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model7.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult7 = model7.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result7*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model7.predict(X_test),Y_test))\nmcc7=matthews_corrcoef(model7.predict(X_test),Y_test)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model eight \n## Randomised tree"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nmodel8 =RandomForestClassifier()\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel8.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model8.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult8 = model8.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result8*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model8.predict(X_test),Y_test))\n\n\nmcc8=matthews_corrcoef(model8.predict(X_test),Y_test)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model nine \n ## Gradient tree boosting\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.ensemble import GradientBoostingClassifier\nmodel9 =GradientBoostingClassifier()\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel9.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model9.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult9 = model9.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result9*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model9.predict(X_test),Y_test))\n\nmcc9=matthews_corrcoef(model9.predict(X_test),Y_test)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model ten\n## Stochastic Gradient Descent "},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.linear_model import SGDClassifier\nmodel10 = SGDClassifier()\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel10.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model10.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult10 = model10.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result10*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model10.predict(X_test),Y_test))\n\n\nmcc10=matthews_corrcoef(model10.predict(X_test),Y_test)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"compare=[result1,result2,result3,result4,result5,result6,result7,result8,result9,result10]\n\ncompare=pd.DataFrame(compare).T\n\ncompare=compare.rename(columns={0:\"LGR\", 1:\"GNB\", 2:\"LDN\", 3:\"SVM\", 4:\"KNN\", 5:\"CCL\", 6:\"DCS\", 7:\"RDT\", 8:\"GBT\", 9:\"SGD\"})\n\ncompareMCC=[mcc1,mcc2,mcc3,mcc4,mcc5,mcc6,mcc7,mcc8,mcc9,mcc10]\ncompareMCC=pd.DataFrame(compareMCC).T\ncompareMCC=compareMCC.rename(columns={0:\"LGR\", 1:\"GNB\", 2:\"LDN\", 3:\"SVM\", 4:\"KNN\", 5:\"CCL\", 6:\"DCS\", 7:\"RDT\", 8:\"GBT\", 9:\"SGD\"})\n\ncompare=compare.append(compareMCC)\n#compare.plot(kind=\"box\")\n#plt.plot(\"Mean of MCC and Accuracy\",\"Different models\", data=compare)\n\n\ncompare=compare.values\n\nfig = plt.figure()\nax = fig.add_subplot(111)\nax.boxplot(compare)\nplt.xticks([1, 2, 3,4,5,6,7,8,9,10], [\"LGR\",\"GNB\",\"LDN\",\"SVM\",\"KNN\",\"CCL\",\"DCS\",\"RDT\",\"GBT\",\"SGD\"])\n\n\n\nax.set_title('A comparison of the different Accuracies and MCC from the different Models')\nax.set_xlabel('The different Models')\nax.set_ylabel('Mean of Accuracy and MCC')\nplt.show()\n\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Predicting from the test data"},{"metadata":{"trusted":true},"cell_type":"code","source":"\ntest_data = pd.read_csv(\"/kaggle/input/ace-class-assignment/Test.csv\", header=0, sep=\",\")\ntest_data2=test_data.values[:,[0,1,3,5,7]]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Predictions from logistic Regression"},{"metadata":{"trusted":true},"cell_type":"code","source":"model1.fit(X,Y) ## retrains the algorithm again on the entire train data set\nkj_results=model1.predict(test_data2)\n\ndf = pd.DataFrame(kj_results)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\ndf.to_csv(\"kjLGR.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score from the leader board using this prediction is 0.82503"},{"metadata":{},"cell_type":"markdown","source":"## Predicting using Gausssian"},{"metadata":{"trusted":true},"cell_type":"code","source":"model2.fit(X,Y) ## retrains the algorithm again on the entire train data set\nkj_results=model2.predict(test_data2)\n\ndf = pd.DataFrame(kj_results)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\ndf.to_csv(\"kjGNB.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score on leader board using this prediction was 0.84591"},{"metadata":{},"cell_type":"markdown","source":"# Predicting using Support Vector Machines "},{"metadata":{"trusted":true},"cell_type":"code","source":"model4.fit(X,Y) ## retrains the algorithm again on the entire train data set\nkj_results=model4.predict(test_data2)\n\ndf = pd.DataFrame(kj_results)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\ndf.to_csv(\"kjSVM.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score on leader board using this prediction was 0.82136"},{"metadata":{},"cell_type":"markdown","source":"## Predicting using the randomised tree classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"model8.fit(X,Y) ## retrains the algorithm again on the entire train data set\nkj_results=model8.predict(test_data2)\n\ndf = pd.DataFrame(kj_results)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\ndf.to_csv(\"kjRDT.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score on leader board using this prediction was 0.80561"},{"metadata":{},"cell_type":"markdown","source":"## Predicting using the Gradient tree boosting classifier "},{"metadata":{"trusted":true},"cell_type":"code","source":"model9.fit(X,Y) ## retrains the algorithm again on the entire train data set\nkj_results=model9.predict(test_data2)\n\ndf = pd.DataFrame(kj_results)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\ndf.to_csv(\"kjGBT.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score on leader board using this prediction was 0.79678"},{"metadata":{},"cell_type":"markdown","source":"Random forest and Gradient boosting classifiers had the highest accuracies during training but gave the least scores on the leader board\n\nGausian gave the highest score"},{"metadata":{},"cell_type":"markdown","source":"# More with the gausian "},{"metadata":{},"cell_type":"markdown","source":"## Using gausian with more features"},{"metadata":{},"cell_type":"markdown","source":"## Using gausian with eight features "},{"metadata":{"trusted":true},"cell_type":"code","source":"amp_train = pd.read_csv(\"/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\", header=0, sep=\",\")\ntest_data = pd.read_csv(\"/kaggle/input/ace-class-assignment/Test.csv\", header=0, sep=\",\")\n\n#selecting out features with low variance\nfrom sklearn.feature_selection import VarianceThreshold\nsel = VarianceThreshold(threshold=(.8 * (1 - .8)))\nk=sel.fit_transform(amp_train)\nnames=amp_train.columns[sel.get_support(indices=True)]\nprint(k.shape)\namp_train2=pd.DataFrame(k,columns=names)\n#print(amp_train2)\n\nn=sel.fit_transform(test_data)\nnames2=test_data.columns[sel.get_support(indices=True)]\ntest_data=pd.DataFrame(n,columns=names2)\ntest_data2=test_data.values\n\n#print(test_data)\n\n### data \ntest_amp =amp_train2.values\nX=test_amp[:,0:7]\nY=test_amp[:,7]\n\n\n\n\n#model\nmodel=GaussianNB()\nfrom sklearn.metrics import matthews_corrcoef\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\nrandom_state=seed, shuffle=\"True\")\nmodel.fit(X_train, Y_train)\npredicted = model.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult = model.score(X_test, Y_test)\nprint(\"Accuracy: \", (result*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model.predict(X_test),Y_test)) \nmodel.fit(X,Y)\nkj_resultsg8=model.predict(test_data)\n\n\n#### results \n\ndf = pd.DataFrame(kj_resultsg8)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\n\ndf.to_csv(\"kjGNB8.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"MCC value for the Gaussian with 8 features and that with 5 features are almost the same, but the leaderboard score using the gausian with 8 features was 0.86379, which is an improvement over the 0.84 score from the five features"},{"metadata":{},"cell_type":"markdown","source":"## Using gaussian with all attributes "},{"metadata":{"trusted":true},"cell_type":"code","source":"amp_train = pd.read_csv(\"/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\", header=0, sep=\",\")\ntest_data = pd.read_csv(\"/kaggle/input/ace-class-assignment/Test.csv\", header=0, sep=\",\")\n\n\n### data \ntest_amp =amp_train.values\nX=test_amp[:,0:11]\nY=test_amp[:,11]\n\n\n#model\nmodel=GaussianNB()\nfrom sklearn.metrics import matthews_corrcoef\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\nrandom_state=seed)\nmodel.fit(X_train, Y_train)\npredicted = model.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult = model.score(X_test, Y_test)\nprint(\"Accuracy: \", (result*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model.predict(X_test),Y_test)) \nmodel.fit(X,Y)\n\nkj_resultsg12=model.predict(test_data)\n\n\n#### results \n\ndf = pd.DataFrame(kj_resultsg12)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\n\ndf.to_csv(\"kjGNB11.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score from the leaderboard using all features was 0.99559"}],"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} +======= +{ + "cells": [ + { + "cell_type": "raw", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", + "# For example, here's several helpful packages to load in \n", + "\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "import matplotlib as plot\n", + "import seaborn as sns\n", + "import sklearn\n", + "\n", + "# Input data files are available in the \"../input/\" directory.\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "for dirname, _, filenames in os.walk('/kaggle/input'):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "\n", + "# Any results you write to the current directory are saved as output." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "import matplotlib as plot\n", + "import seaborn as sns\n", + "import sklearn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Importing data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + "0 5.0 0.000 0.951 74.842 \n", + "1 4.0 5.405 0.931 71.595 \n", + "2 5.5 5.405 0.873 73.595 \n", + "3 5.0 4.167 0.895 66.250 \n", + "4 7.5 8.537 0.932 64.720 \n", + "\n", + " FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n", + "0 0.975 -3.663 0 0.282 \n", + "1 0.957 -4.011 1 0.600 \n", + "2 0.961 -2.512 0 0.593 \n", + "3 0.999 -1.362 0 0.614 \n", + "4 0.979 -2.091 0 0.616 \n", + "\n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 CLASS \n", + "0 73.444 5.661 1.041 1 \n", + "1 68.222 6.537 1.453 1 \n", + "2 69.444 4.934 1.722 1 \n", + "3 67.222 4.316 1.382 1 \n", + "4 72.944 4.540 1.539 1 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Importing data\n", + "test_data = pd.read_csv(\"../AMP Data Sets/Test.csv\", header=0, sep=\",\")\n", + "amp_train = pd.read_csv(\"../AMP Data Sets/AMP_TrainSet.csv\", header=0, sep=\",\")\n", + "#print(amp_train)\n", + "\n", + "\n", + "amp_train.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n", + " 'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n", + " 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n", + " 'CLASS'],\n", + " dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "amp_train.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#test_amp =amp_train[['FULL_Charge', 'FULL_AcidicMolPerc','FULL_OOBM850104','FULL_DAYM780201', 'AS_MeanAmphiMoment','CLASS']]\n", + "#print(test_amp)\n", + "#test_data=test_data[['FULL_Charge', 'FULL_AcidicMolPerc','FULL_OOBM850104','FULL_DAYM780201', 'AS_MeanAmphiMoment']]\n", + "#X=test_amp.drop(\"CLASS\",axis=1)\n", + "#Y=test_amp.CLASS\n", + "#model=GaussianNB() \n", + "#model.fit(X,Y)\n", + "#kj_results=model.predict(test_data)\n", + "\n", + "#print(test_data2)\n", + "#kj_results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## General data attributes and descriptive statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3038, 12)\n", + "(758, 11)\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", + "CLASS int64\n", + "dtype: object\n" + ] + }, + { + "data": { + "text/plain": [ + "FULL_Charge 0\n", + "FULL_AcidicMolPerc 0\n", + "FULL_AURR980107 0\n", + "FULL_DAYM780201 0\n", + "FULL_GEOR030101 0\n", + "FULL_OOBM850104 0\n", + "NT_EFC195 0\n", + "AS_MeanAmphiMoment 0\n", + "AS_DAYM780201 0\n", + "AS_FUKS010112 0\n", + "CT_RACS820104 0\n", + "CLASS 0\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Describing general data attributes\n", + "train_dim=amp_train.shape #gets the dimensions of the tranining dataset\n", + "test_dim= test_data.shape #gets the dimensions of the tesing dataset\n", + "print(train_dim)\n", + "print(test_dim)\n", + "\n", + "atr_types=amp_train.dtypes #gets the data type for each column in training dataset\n", + "print(atr_types)\n", + "amp_train.isnull().sum() # gets the sum of the missing values/observations in each column\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Note!!\n", + "You can write the code below as\n", + "\n", + "```\n", + "amp_train.describe()\n", + "\n", + "```\n", + "\n", + "It renders properly when you write it like this, Try it!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104CLASS
count3038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.000000
mean2.0602378.5215200.97141073.6687600.994007-2.4329270.08854515.68323373.6508285.9113611.2352550.500000
std3.8199297.5866520.1074138.5274890.0313331.7072230.28413311.5756659.1660920.6936890.2100120.500082
min-16.0000000.0000000.68400042.7500000.866000-10.4320000.0000000.04100042.7780003.5330000.7850000.000000
25%0.0000002.5160000.89500068.2940000.974000-3.6060000.0000005.58750067.5560005.4592501.0820000.000000
50%2.0000007.1430000.96300074.0595000.994000-2.2965000.00000014.98850073.6970005.9255001.1840000.500000
75%4.00000013.1580001.04100079.3437501.011000-1.2832500.00000026.80775079.7780006.3820001.3510001.000000
max30.00000046.6670001.451000101.6820001.1960003.5760001.00000051.280000103.1670008.6620002.1920001.000000
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" + ], + "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": [ + "# Descriptive statistics \n", + "stats=amp_train.describe()\n", + "#print(stats)\n", + "\n", + "stats\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Class distributions\n", + "\n", + "Sometimes, observations from the different classes may not be equal, these may need special handling, so its important to note the class distributions, as if they are uneven, they may bias our model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CLASS\n", + "0 1519\n", + "1 1519\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# CLass distibutions \n", + "cdists= amp_train.groupby('CLASS').size() # groups the observation by the column of class, and counts all the observations including null values\n", + "print(cdists)\n", + "\n", + "#plots to visualize the class distribution\n", + "amp_train.groupby('CLASS').size().plot(kind=\"pie\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The observations from each class seem to be equal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Correlation between the different attributes\n", + "Basically, we need to examine our attributes for correlation as highly correlated attributes present the same data to the algorithm. Removing one may speed learning of the algorithm (less dimensions more speed)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104CLASS
FULL_Charge1.000000-0.612996-0.490977-0.434603-0.058725-0.2837580.0880680.355477-0.365374-0.0905700.2329290.534602
FULL_AcidicMolPerc-0.6129961.0000000.7947960.5414810.1152010.513344-0.143168-0.4315900.4496210.002334-0.213543-0.598816
FULL_AURR980107-0.4909770.7947961.0000000.5482530.3461390.462712-0.169540-0.4260970.4562600.032958-0.403599-0.584111
FULL_DAYM780201-0.4346030.5414810.5482531.0000000.0101180.334778-0.090058-0.4087930.8941910.055915-0.326792-0.554838
FULL_GEOR030101-0.0587250.1152010.3461390.0101181.0000000.319157-0.230417-0.160269-0.0290850.040480-0.151935-0.260470
FULL_OOBM850104-0.2837580.5133440.4627120.3347780.3191571.000000-0.230561-0.3362970.275640-0.4527690.155304-0.453287
NT_EFC1950.088068-0.143168-0.169540-0.090058-0.230417-0.2305611.0000000.178683-0.0368440.1459240.0808980.260702
AS_MeanAmphiMoment0.355477-0.431590-0.426097-0.408793-0.160269-0.3362970.1786831.000000-0.3223780.0255800.1715240.693552
AS_DAYM780201-0.3653740.4496210.4562600.894191-0.0290850.275640-0.036844-0.3223781.0000000.045562-0.256060-0.437168
AS_FUKS010112-0.0905700.0023340.0329580.0559150.040480-0.4527690.1459240.0255800.0455621.000000-0.4452840.033432
CT_RACS8201040.232929-0.213543-0.403599-0.326792-0.1519350.1553040.0808980.171524-0.256060-0.4452841.0000000.267652
CLASS0.534602-0.598816-0.584111-0.554838-0.260470-0.4532870.2607020.693552-0.4371680.0334320.2676521.000000
\n", + "
" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 \\\n", + "FULL_Charge 1.000000 -0.612996 -0.490977 \n", + "FULL_AcidicMolPerc -0.612996 1.000000 0.794796 \n", + "FULL_AURR980107 -0.490977 0.794796 1.000000 \n", + "FULL_DAYM780201 -0.434603 0.541481 0.548253 \n", + "FULL_GEOR030101 -0.058725 0.115201 0.346139 \n", + "FULL_OOBM850104 -0.283758 0.513344 0.462712 \n", + "NT_EFC195 0.088068 -0.143168 -0.169540 \n", + "AS_MeanAmphiMoment 0.355477 -0.431590 -0.426097 \n", + "AS_DAYM780201 -0.365374 0.449621 0.456260 \n", + "AS_FUKS010112 -0.090570 0.002334 0.032958 \n", + "CT_RACS820104 0.232929 -0.213543 -0.403599 \n", + "CLASS 0.534602 -0.598816 -0.584111 \n", + "\n", + " FULL_DAYM780201 FULL_GEOR030101 FULL_OOBM850104 \\\n", + "FULL_Charge -0.434603 -0.058725 -0.283758 \n", + "FULL_AcidicMolPerc 0.541481 0.115201 0.513344 \n", + "FULL_AURR980107 0.548253 0.346139 0.462712 \n", + "FULL_DAYM780201 1.000000 0.010118 0.334778 \n", + "FULL_GEOR030101 0.010118 1.000000 0.319157 \n", + "FULL_OOBM850104 0.334778 0.319157 1.000000 \n", + "NT_EFC195 -0.090058 -0.230417 -0.230561 \n", + "AS_MeanAmphiMoment -0.408793 -0.160269 -0.336297 \n", + "AS_DAYM780201 0.894191 -0.029085 0.275640 \n", + "AS_FUKS010112 0.055915 0.040480 -0.452769 \n", + "CT_RACS820104 -0.326792 -0.151935 0.155304 \n", + "CLASS -0.554838 -0.260470 -0.453287 \n", + "\n", + " NT_EFC195 AS_MeanAmphiMoment AS_DAYM780201 \\\n", + "FULL_Charge 0.088068 0.355477 -0.365374 \n", + "FULL_AcidicMolPerc -0.143168 -0.431590 0.449621 \n", + "FULL_AURR980107 -0.169540 -0.426097 0.456260 \n", + "FULL_DAYM780201 -0.090058 -0.408793 0.894191 \n", + "FULL_GEOR030101 -0.230417 -0.160269 -0.029085 \n", + "FULL_OOBM850104 -0.230561 -0.336297 0.275640 \n", + "NT_EFC195 1.000000 0.178683 -0.036844 \n", + "AS_MeanAmphiMoment 0.178683 1.000000 -0.322378 \n", + "AS_DAYM780201 -0.036844 -0.322378 1.000000 \n", + "AS_FUKS010112 0.145924 0.025580 0.045562 \n", + "CT_RACS820104 0.080898 0.171524 -0.256060 \n", + "CLASS 0.260702 0.693552 -0.437168 \n", + "\n", + " AS_FUKS010112 CT_RACS820104 CLASS \n", + "FULL_Charge -0.090570 0.232929 0.534602 \n", + "FULL_AcidicMolPerc 0.002334 -0.213543 -0.598816 \n", + "FULL_AURR980107 0.032958 -0.403599 -0.584111 \n", + "FULL_DAYM780201 0.055915 -0.326792 -0.554838 \n", + "FULL_GEOR030101 0.040480 -0.151935 -0.260470 \n", + "FULL_OOBM850104 -0.452769 0.155304 -0.453287 \n", + "NT_EFC195 0.145924 0.080898 0.260702 \n", + "AS_MeanAmphiMoment 0.025580 0.171524 0.693552 \n", + "AS_DAYM780201 0.045562 -0.256060 -0.437168 \n", + "AS_FUKS010112 1.000000 -0.445284 0.033432 \n", + "CT_RACS820104 -0.445284 1.000000 0.267652 \n", + "CLASS 0.033432 0.267652 1.000000 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Correlation between attributes \n", + "correlations = amp_train.corr(method=\"pearson\") # calculates pairwaise correlation for the attributes \n", + "\n", + "#print(correlations) #you don't always have to use print()\n", + "\n", + "correlations\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "FULL_AURR980107 and FULL_AcidicMolPerc have a correlation coefficent of 0.79, this might mean these two attributes may be some what correlated. Same goes for AS_DAYM780201 and FULL_DAYM780201 (correlation coefficient is 0.894191)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plots to visualize the correlations\n", + "from matplotlib import rcParams\n", + "sns.heatmap(correlations, annot=True)\n", + "rcParams['figure.figsize'] = 11.7,8.27\n", + "\n", + "\n", + "## Note!!\n", + "# import matplotlib.pyplot as plt\n", + "# You can use the function: plt.figure(figsize=(x,x))\n", + "# import matplotlib.pyplot as plt --> to do your plotting it gives more choices.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data skewness\n", + "\n", + "Most machine learning algorithms assume that data from the attributes is normally distributed, so identifying attributes with data that has a left or right skew, can be important as this can be easily corrected" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL_Charge 0.601716\n", + "FULL_AcidicMolPerc 0.994487\n", + "FULL_AURR980107 0.490291\n", + "FULL_DAYM780201 -0.216841\n", + "FULL_GEOR030101 0.883022\n", + "FULL_OOBM850104 -0.207124\n", + "NT_EFC195 2.898124\n", + "AS_MeanAmphiMoment 0.383682\n", + "AS_DAYM780201 -0.070879\n", + "AS_FUKS010112 -0.112632\n", + "CT_RACS820104 0.999487\n", + "CLASS 0.000000\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "sk = amp_train.skew() # calculates the skew for each attribute\n", + "print(sk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Negative values like those of FULL_DAYM780201, FULL_OOBM850104,AS_DAYM780201, AS_FUKS010112, indicate that these attributes have values skewed more to the left\n", + "Values closer to zero are indicative of a less or no skew, while positive values would mean that that particular attribute is skewed more to the right" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # Visualising data distributions with plots" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot one, histogram to show the distributions\n", + "import matplotlib as plot \n", + "from matplotlib import pyplot\n", + "amp_train.hist(layout=(4,3), figsize=(16,16))\n", + "plot.pyplot.subplots_adjust(wspace=0.4, hspace=0.5)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot two density plots can show the distributions better\n", + "amp_train.plot(kind=\"density\",subplots=True,layout=(4,3),sharex=False,sharey=False, figsize=(16,16))\n", + "#amp_train.plot(kind=\"density\",subplots=False,layout=(4,3),sharex=True,sharey=True)\n", + "pyplot.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data transformations\n", + "Using square roots to transform the attributes with negative values into postives\n", + "\n", + "\n", + "# Note!\n", + "\n", + "Is there a way to render these side by side such that we will be able to see how the transformation affects the originals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "neg1=amp_train.iloc[:,0]\n", + "print(neg1) #plot this so that we can see how it looks before\n", + "tneg1=neg1**2\n", + "tneg1.plot(kind=\"density\") #plot side by side to see what the transformation does.\n", + "amp_train.iloc[:,0]=tneg1\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 5.0\n", + "1 4.0\n", + "2 5.5\n", + "3 5.0\n", + "4 7.5\n", + "5 5.0\n", + "6 3.0\n", + "7 2.0\n", + "8 7.0\n", + "9 9.0\n", + "10 2.0\n", + "11 2.5\n", + "12 3.0\n", + "13 3.0\n", + "14 2.5\n", + "15 6.0\n", + "16 2.0\n", + "17 4.0\n", + "18 4.5\n", + "19 2.0\n", + "20 2.0\n", + "21 3.0\n", + "22 3.5\n", + "23 1.0\n", + "24 1.0\n", + "25 5.5\n", + "26 6.5\n", + "27 1.0\n", + "28 6.5\n", + "29 7.5\n", + " ... \n", + "3008 -6.0\n", + "3009 -1.0\n", + "3010 1.0\n", + "3011 0.0\n", + "3012 -3.0\n", + "3013 -1.0\n", + "3014 -1.0\n", + "3015 2.0\n", + "3016 -1.0\n", + "3017 0.0\n", + "3018 0.0\n", + "3019 -0.5\n", + 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FULL_ChargeFULL_AcidicMolPercFULL_DAYM780201FULL_OOBM850104AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CLASS
025.000.00074.84213.4175690.28273.4445.6611.0
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456.258.53764.7204.3722810.61672.9444.5401.0
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" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_DAYM780201 FULL_OOBM850104 \\\n", + "0 25.00 0.000 74.842 13.417569 \n", + "1 16.00 5.405 71.595 16.088121 \n", + "2 30.25 5.405 73.595 6.310144 \n", + "3 25.00 4.167 66.250 1.855044 \n", + "4 56.25 8.537 64.720 4.372281 \n", + "\n", + " AS_MeanAmphiMoment AS_DAYM780201 AS_FUKS010112 CLASS \n", + "0 0.282 73.444 5.661 1.0 \n", + "1 0.600 68.222 6.537 1.0 \n", + "2 0.593 69.444 4.934 1.0 \n", + "3 0.614 67.222 4.316 1.0 \n", + "4 0.616 72.944 4.540 1.0 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " #test one selecting out features with low variance\n", + "from sklearn.feature_selection import VarianceThreshold\n", + "sel = VarianceThreshold(threshold=(.8 * (1 - .8)))\n", + "k=sel.fit_transform(amp_train) #fit the algorithm\n", + "\n", + "names=amp_train.columns[sel.get_support(indices=True)] #mapping the names\n", + "print(k.shape) #dimensions of the new data\n", + "amp_train2=pd.DataFrame(k,columns=names)\n", + "\n", + "amp_train2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.53389981e+04 7.35553647e+03 9.22860060e+02 7.06525505e+03\n", + " 1.24812698e+04 6.62112299e+02 2.76320294e-01]\n", + " FULL_Charge FULL_AcidicMolPerc FULL_DAYM780201 FULL_OOBM850104 \\\n", + "5 25.00 7.692 78.949 9.554281 \n", + "6 9.00 6.897 78.586 12.559936 \n", + "7 4.00 5.882 76.588 34.012224 \n", + "8 49.00 2.632 60.447 0.085264 \n", + "9 81.00 0.000 65.808 15.116544 \n", + "10 4.00 3.448 71.172 13.778944 \n", + "11 6.25 0.000 60.579 1.852321 \n", + "12 9.00 0.000 70.875 12.981609 \n", + "13 9.00 7.143 71.357 18.748900 \n", + "14 6.25 7.692 60.769 13.256881 \n", + "15 36.00 4.348 67.870 2.050624 \n", + "16 4.00 0.000 73.842 24.285184 \n", + "17 16.00 0.000 64.083 24.770529 \n", + "18 20.25 11.111 77.185 0.481636 \n", + "19 4.00 4.167 68.667 9.778129 \n", + "20 4.00 6.667 68.233 11.068929 \n", + "21 9.00 10.345 72.000 22.127616 \n", + "22 12.25 2.703 72.919 13.162384 \n", + "23 1.00 0.000 52.769 21.696964 \n", + "24 1.00 0.000 63.231 16.483600 \n", + "25 30.25 4.348 58.913 22.619536 \n", + "26 42.25 0.000 63.917 11.703241 \n", + "27 1.00 5.556 74.833 15.038884 \n", + "28 42.25 0.000 66.500 17.926756 \n", + "29 56.25 8.333 69.375 3.115225 \n", + "30 0.25 11.765 79.882 9.084196 \n", + "31 2.25 0.000 73.313 21.196816 \n", + "32 49.00 8.108 74.838 19.140625 \n", + "33 4.00 0.000 71.300 29.408929 \n", + "34 81.00 0.000 63.625 13.616100 \n", + "... ... ... ... ... \n", + "3008 36.00 20.000 83.686 2.033476 \n", + "3009 1.00 22.500 77.700 0.045796 \n", + "3010 1.00 12.500 79.708 1.575025 \n", + "3011 0.00 5.263 73.079 2.334784 \n", + "3012 9.00 19.608 83.412 1.058841 \n", + "3013 1.00 12.245 76.122 2.849344 \n", + "3014 1.00 16.667 80.250 0.122500 \n", + "3015 4.00 10.811 67.514 5.017600 \n", + "3016 1.00 8.333 71.000 2.214144 \n", + "3017 0.00 10.256 80.077 1.153476 \n", + "3018 0.00 18.750 88.250 1.726596 \n", + "3019 0.25 16.667 77.729 1.334025 \n", + "3020 0.25 11.429 77.657 0.819025 \n", + "3021 1.00 11.538 78.038 0.040000 \n", + "3022 9.00 0.000 66.769 0.029929 \n", + "3023 4.00 12.000 75.867 2.005056 \n", + "3024 12.25 20.000 73.050 7.823209 \n", + "3025 6.25 10.714 78.071 8.334769 \n", + "3026 0.25 0.000 81.545 0.381924 \n", + "3027 81.00 7.547 77.283 11.682724 \n", + "3028 4.00 18.750 79.938 1.196836 \n", + "3029 20.25 14.286 69.143 1.177225 \n", + "3030 4.00 12.963 72.815 11.874916 \n", + "3031 0.00 17.021 76.234 5.143824 \n", + "3032 0.00 16.667 82.917 4.297329 \n", + "3033 1.00 5.263 67.947 4.626801 \n", + "3034 42.25 21.667 75.433 2.805625 \n", + "3035 2.25 12.500 76.542 0.842724 \n", + "3036 4.00 5.000 73.750 7.409284 \n", + "3037 1.00 15.789 66.158 4.326400 \n", + "\n", + " AS_MeanAmphiMoment \n", + "5 0.511 \n", + "6 0.385 \n", + "7 0.154 \n", + "8 0.188 \n", + "9 0.361 \n", + "10 0.510 \n", + "11 0.163 \n", + "12 0.263 \n", + "13 0.232 \n", + "14 0.402 \n", + "15 0.187 \n", + "16 0.536 \n", + "17 0.826 \n", + "18 1.100 \n", + "19 1.210 \n", + "20 1.304 \n", + "21 1.726 \n", + "22 1.717 \n", + "23 2.834 \n", + "24 2.980 \n", + "25 2.138 \n", + "26 2.435 \n", + "27 2.044 \n", + "28 2.704 \n", + "29 2.743 \n", + "30 2.898 \n", + "31 3.394 \n", + "32 3.282 \n", + "33 3.214 \n", + "34 3.020 \n", + "... ... \n", + "3008 15.452 \n", + "3009 15.625 \n", + "3010 15.502 \n", + "3011 15.676 \n", + "3012 15.813 \n", + "3013 15.774 \n", + "3014 23.502 \n", + "3015 15.974 \n", + "3016 23.550 \n", + "3017 15.781 \n", + "3018 17.666 \n", + "3019 15.838 \n", + "3020 15.867 \n", + "3021 16.209 \n", + "3022 22.477 \n", + "3023 16.444 \n", + "3024 16.495 \n", + "3025 16.513 \n", + "3026 26.931 \n", + "3027 16.517 \n", + "3028 18.119 \n", + "3029 20.625 \n", + "3030 16.256 \n", + "3031 16.365 \n", + "3032 24.497 \n", + "3033 16.706 \n", + "3034 16.897 \n", + "3035 16.918 \n", + "3036 17.131 \n", + "3037 17.151 \n", + "\n", + "[3033 rows x 5 columns]\n" + ] + } + ], + "source": [ + "#test two\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "test_array = amp_train2.values\n", + "\n", + "test_atr = test_array[:,0:7]\n", + "class_atr = test_array[:,7]\n", + "\n", + "\n", + "test = SelectKBest(score_func=chi2, k=5)\n", + "fit = test.fit(test_atr, class_atr)\n", + "\n", + "\n", + "print(fit.scores_)\n", + "features = fit.transform(test_atr)\n", + "names2=amp_train2.columns[fit.get_support(indices=True)]\n", + "amp_train3=pd.DataFrame(features,columns=names2)\n", + "print(amp_train3[5:])\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Test three Recursive feature elimination\n", + "\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "model1 = LogisticRegression()\n", + "model2=RandomForestClassifier()\n", + "\n", + "rfe = RFE(model1, 4)\n", + "fit2 = rfe.fit(test_atr, class_atr)\n", + "\n", + "rfe2= RFE(model2, 4)\n", + "fit3 = rfe2.fit(test_atr, class_atr)\n", + "\n", + "\n", + "\n", + "\n", + "names3=amp_train2.columns[fit2.get_support(indices=True)]\n", + "\n", + "names4=amp_train2.columns[fit3.get_support(indices=True)]\n", + "\n", + "#print(names3)\n", + "#print(names4)\n", + "#print(names2)\n", + "#print(amp_train.columns)\n", + "#print(amp_train2)\n", + "\n", + "#transformer functions\n", + "# When we use a fit function we normally folow with a transformation function.\n", + "# That way we can change our data the same way for the test and train dataset.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Note\n", + "\n", + "Why not use the transform method to change " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " FULL_Charge FULL_AcidicMolPerc FULL_OOBM850104 FULL_DAYM780201 \\\n", + "0 25.00 0.000 13.417569 74.842 \n", + "1 16.00 5.405 16.088121 71.595 \n", + "2 30.25 5.405 6.310144 73.595 \n", + "3 25.00 4.167 1.855044 66.250 \n", + "4 56.25 8.537 4.372281 64.720 \n", + "5 25.00 7.692 9.554281 78.949 \n", + "6 9.00 6.897 12.559936 78.586 \n", + "7 4.00 5.882 34.012224 76.588 \n", + "8 49.00 2.632 0.085264 60.447 \n", + "9 81.00 0.000 15.116544 65.808 \n", + "10 4.00 3.448 13.778944 71.172 \n", + "11 6.25 0.000 1.852321 60.579 \n", + "12 9.00 0.000 12.981609 70.875 \n", + "13 9.00 7.143 18.748900 71.357 \n", + "14 6.25 7.692 13.256881 60.769 \n", + "15 36.00 4.348 2.050624 67.870 \n", + "16 4.00 0.000 24.285184 73.842 \n", + "17 16.00 0.000 24.770529 64.083 \n", + "18 20.25 11.111 0.481636 77.185 \n", + "19 4.00 4.167 9.778129 68.667 \n", + "20 4.00 6.667 11.068929 68.233 \n", + "21 9.00 10.345 22.127616 72.000 \n", + "22 12.25 2.703 13.162384 72.919 \n", + "23 1.00 0.000 21.696964 52.769 \n", + "24 1.00 0.000 16.483600 63.231 \n", + "25 30.25 4.348 22.619536 58.913 \n", + "26 42.25 0.000 11.703241 63.917 \n", + "27 1.00 5.556 15.038884 74.833 \n", + "28 42.25 0.000 17.926756 66.500 \n", + "29 56.25 8.333 3.115225 69.375 \n", + "... ... ... ... ... \n", + "3008 36.00 20.000 2.033476 83.686 \n", + "3009 1.00 22.500 0.045796 77.700 \n", + "3010 1.00 12.500 1.575025 79.708 \n", + "3011 0.00 5.263 2.334784 73.079 \n", + "3012 9.00 19.608 1.058841 83.412 \n", + "3013 1.00 12.245 2.849344 76.122 \n", + "3014 1.00 16.667 0.122500 80.250 \n", + "3015 4.00 10.811 5.017600 67.514 \n", + "3016 1.00 8.333 2.214144 71.000 \n", + "3017 0.00 10.256 1.153476 80.077 \n", + "3018 0.00 18.750 1.726596 88.250 \n", + "3019 0.25 16.667 1.334025 77.729 \n", + "3020 0.25 11.429 0.819025 77.657 \n", + "3021 1.00 11.538 0.040000 78.038 \n", + "3022 9.00 0.000 0.029929 66.769 \n", + "3023 4.00 12.000 2.005056 75.867 \n", + "3024 12.25 20.000 7.823209 73.050 \n", + "3025 6.25 10.714 8.334769 78.071 \n", + "3026 0.25 0.000 0.381924 81.545 \n", + "3027 81.00 7.547 11.682724 77.283 \n", + "3028 4.00 18.750 1.196836 79.938 \n", + "3029 20.25 14.286 1.177225 69.143 \n", + "3030 4.00 12.963 11.874916 72.815 \n", + "3031 0.00 17.021 5.143824 76.234 \n", + "3032 0.00 16.667 4.297329 82.917 \n", + "3033 1.00 5.263 4.626801 67.947 \n", + "3034 42.25 21.667 2.805625 75.433 \n", + "3035 2.25 12.500 0.842724 76.542 \n", + "3036 4.00 5.000 7.409284 73.750 \n", + "3037 1.00 15.789 4.326400 66.158 \n", + "\n", + " AS_MeanAmphiMoment CLASS \n", + "0 0.282 1 \n", + "1 0.600 1 \n", + "2 0.593 1 \n", + "3 0.614 1 \n", + "4 0.616 1 \n", + "5 0.511 1 \n", + "6 0.385 1 \n", + "7 0.154 1 \n", + "8 0.188 1 \n", + "9 0.361 1 \n", + "10 0.510 1 \n", + "11 0.163 1 \n", + "12 0.263 1 \n", + "13 0.232 1 \n", + "14 0.402 1 \n", + "15 0.187 1 \n", + "16 0.536 1 \n", + "17 0.826 1 \n", + "18 1.100 1 \n", + "19 1.210 1 \n", + "20 1.304 1 \n", + "21 1.726 1 \n", + "22 1.717 1 \n", + "23 2.834 1 \n", + "24 2.980 1 \n", + "25 2.138 1 \n", + "26 2.435 1 \n", + "27 2.044 1 \n", + "28 2.704 1 \n", + "29 2.743 1 \n", + "... ... ... \n", + "3008 15.452 0 \n", + "3009 15.625 0 \n", + "3010 15.502 0 \n", + "3011 15.676 0 \n", + "3012 15.813 0 \n", + "3013 15.774 0 \n", + "3014 23.502 0 \n", + "3015 15.974 0 \n", + "3016 23.550 0 \n", + "3017 15.781 0 \n", + "3018 17.666 0 \n", + "3019 15.838 0 \n", + "3020 15.867 0 \n", + "3021 16.209 0 \n", + "3022 22.477 0 \n", + "3023 16.444 0 \n", + "3024 16.495 0 \n", + "3025 16.513 0 \n", + "3026 26.931 0 \n", + "3027 16.517 0 \n", + "3028 18.119 0 \n", + "3029 20.625 0 \n", + "3030 16.256 0 \n", + "3031 16.365 0 \n", + "3032 24.497 0 \n", + "3033 16.706 0 \n", + "3034 16.897 0 \n", + "3035 16.918 0 \n", + "3036 17.131 0 \n", + "3037 17.151 0 \n", + "\n", + "[3038 rows x 6 columns]\n" + ] + } + ], + "source": [ + "test_amp =amp_train.loc[:,['FULL_Charge', 'FULL_AcidicMolPerc',\"FULL_OOBM850104\",'FULL_DAYM780201', 'AS_MeanAmphiMoment','CLASS']]\n", + "print(test_amp)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logistic regression using cross validation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 91.442% (1.795%)\n" + ] + } + ], + "source": [ + "\n", + "X=test_amp.iloc[:,0:5]\n", + "Y=test_amp.iloc[:,5]\n", + "folds=10\n", + "kfold = KFold(n_splits=folds, random_state=7, shuffle=True,)\n", + "model = LogisticRegression()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "\n", + "print((\"Accuracy: %.3f%% (%.3f%%)\") % (results.mean()*100.0, results.std()*100.0))\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_data = pd.read_csv(\"/kaggle/input/ace-class-assignment/Test.csv\", header=0, sep=\",\")\n", + "amp_train = pd.read_csv(\"/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\", header=0, sep=\",\")\n", + "test_amp =amp_train.loc[:,['FULL_Charge', 'FULL_AcidicMolPerc',\"FULL_OOBM850104\",'FULL_DAYM780201', 'AS_MeanAmphiMoment','CLASS']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic regression with a test train split" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(test_data.loc[:,['FULL_Charge', 'FULL_AcidicMolPerc','FULL_DAYM780201', 'FULL_OOBM850104', 'AS_MeanAmphiMoment']])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 91.44736842105263\n", + "[[273 31]\n", + " [ 21 283]]\n", + "MCC: 0.8293962196513646\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 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1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import matthews_corrcoef\n", + "#test_amp=test_amp.values\n", + "#X=test_amp.iloc[:,0:5]\n", + "X=test_amp[:,0:5]\n", + "Y=test_amp[:,5]\n", + "\n", + "#Y=test_amp.iloc[:,5]\n", + "#print(Y)\n", + "test_size = 0.2\n", + "seed = 7\n", + "\n", + "\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n", + "\n", + "model = LogisticRegression()\n", + "\n", + "model.fit(X_train, Y_train)\n", + "\n", + "predicted = model.predict(X_test)\n", + "\n", + "matrix = confusion_matrix(Y_test, predicted)\n", + "\n", + "result = model.score(X_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))\n", + "print(matrix)\n", + "print('MCC: ', matthews_corrcoef(model.predict(X_test),Y_test))\n", + "#print(test_data)\n", + "\n", + "test_data2=test_data.loc[:,['FULL_Charge', 'FULL_AcidicMolPerc','FULL_DAYM780201', 'FULL_OOBM850104', 'AS_MeanAmphiMoment']]\n", + "#test_data2=test_data2.values \n", + "#model1 = LogisticRegression() \n", + "#model1.fit(X,Y)\n", + "kj_results=model.predict(test_data2)\n", + "\n", + "#print(test_data2)\n", + "kj_results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(kj_results)\n", + "df.columns = ['CLASS']\n", + "df.index.name = 'Index'\n", + "df['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\n", + "print(df)\n", + "#X=test_amp.iloc[:,0:5]\n", + "#=test_amp.iloc[:,5]\n", + "df.to_csv(\"kj.csv\")\n", + "df[\"CLASS\"].unique()\n", + "#print(df.groupby(\"CLASS\").size()[0].sum())\n", + "#print(df.groupby(\"CLASS\").size()[1].sum())\n", + "df['CLASS'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gausian/Naive Bayes" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 91.69407894736842\n", + "[[586 29]\n", + " [ 72 529]]\n", + "MCC: 0.8358210636901913\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model=GaussianNB()\n", + "from sklearn.metrics import matthews_corrcoef\n", + "test_size = 0.4\n", + "seed = 25\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "random_state=seed)\n", + "\n", + "model.fit(X_train, Y_train)\n", + "predicted = model.predict(X_test)\n", + "matrix = confusion_matrix(Y_test, predicted)\n", + "result = model.score(X_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))\n", + "print(matrix)\n", + "print('MCC: ', matthews_corrcoef(model.predict(X_test),Y_test))\n", + "#print(test_data)\n", + "\n", + "X=test_amp[:,0:5]\n", + "Y=test_amp[:,5]\n", + "model=GaussianNB() \n", + "model.fit(X,Y)\n", + "kj_results=model.predict(test_data2)\n", + "\n", + "#print(test_data2)\n", + "kj_results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear Discriminant Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 90.948% (1.647%)\n" + ] + } + ], + "source": [ + "model=LinearDiscriminantAnalysis()\n", + "folds=10\n", + "kfold = KFold(n_splits=folds, random_state=7, shuffle=True,)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print((\"Accuracy: %.3f%% (%.3f%%)\") % (results.mean()*100.0, results.std()*100.0))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Support Vector Machines" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 91.804% (1.970%)\n" + ] + } + ], + "source": [ + "model_sv=SVC()\n", + "folds=10\n", + "kfold = KFold(n_splits=folds, random_state=7, shuffle=True,)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print((\"Accuracy: %.3f%% (%.3f%%)\") % (results.mean()*100.0, results.std()*100.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", + " tol=0.001, verbose=False)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_sv.fit(X_train, Y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((758, 5), (2430, 5))" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data2.shape, X_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 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"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 +} +>>>>>>> 74881c0303436bfa83a7232ecf1bd82de23bb1b2 diff --git a/Assignment Colab/MARIA MAGDALENE NAMAGANDA FINAL_UPDATE.ipynb b/Assignment Colab/MARIA MAGDALENE NAMAGANDA FINAL_UPDATE.ipynb new file mode 100644 index 0000000..dec17ea --- /dev/null +++ b/Assignment Colab/MARIA MAGDALENE NAMAGANDA FINAL_UPDATE.ipynb @@ -0,0 +1,2154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MARIA MAGDALENE NAMAGANDA\n", + "# 2019/HD07/24853U\n", + "# Ace_class Kaggle assignment_1" + ] + }, + { + "cell_type": "raw", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", + "# For example, here's several helpful packages to load in \n", + "\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "\n", + "# Input data files are available in the \"../input/\" directory.\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "for dirname, _, filenames in os.walk('/kaggle/input'):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "\n", + "# Any results you write to the current directory are saved as output." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing some of the needed packages and libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import pyplot\n", + "import seaborn as sns\n", + "\n", + "from sklearn.model_selection import KFold\n", + "from pandas import read_csv\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.svm import SVC\n", + "from sklearn.ensemble import AdaBoostClassifier\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Description of Libraries used.\n", + "* Pandas is a library in Python for data manipulation and analysis. It offers data structures and operations for manipulating numerical tables and time series.\n", + "* Matplotlib is a plotting library for the Python.\n", + "* Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n", + "* K-Fold cross validation is where a given data set is split into a K number of sections/folds where each fold is used as a testing set at some point.\n", + "* A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known.\n", + "* In Train/Test Split the data we use is usually split into training data and test data. The training set contains a known output and the model learns on this data in order to be generalized to other data later on\n", + "* Logistic Regression is used for classification problems, it is a predictive analysis algorithm and based on the concept of probability.\n", + "* A decision tree is a flowchart-like tree structure where an internal node represents feature(attribute), the branch represents a decision rule, and each leaf node represents the outcome.\n", + "* KNeighbours Classifier is a non-parametric algorithm whose purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point.\n", + "* The Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. It performs well in cases of categorical input variables compared to numerical variables. \n", + "* The linear Discriminant analysis estimates the probability that a new set of inputs belongs to every class.\n", + "* SVC (Support Vector Classifier) is to fit the data you provide, returning a \"best fit\" hyperplane that divides, or categorizes your data.\n", + "* The basic concept behind Adaboost is to set the weights of classifiers and training the data sample in each iteration such that it ensures the accurate predictions of unusual observations.\n", + "* AdaBoost works by weighting the observations, putting more weight on difficult to classify instances and less on those already handled well.\n", + "* ExtraTreesClassifier, like RandomForest, randomizes certain decisions and subsets of data to minimize over-learning from the data and overfitting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> *Now i need to get rid of some unnecessary warnings by ignoring them*" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#To avoid unnecessary warnings, I will ignore them in the code below\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the datasets \n", + "### There are two datasets given; \n", + "1. AMP_TrainSet.csv and \n", + "2. Test.csv " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "_cell_guid": "", + "_uuid": "" + }, + "outputs": [], + "source": [ + "#Loading the datasets, \n", + "\n", + "Train = pd.read_csv(\"../Data/AMP_TrainSet.csv\")\n", + "Test = pd.read_csv(\"../Data/Test.csv\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For training the model, I will first use the training set which I will further split into the train and validation set. \n", + "\n", + "
\n", + " @atwine: Did not explain why they are splitting, I need to see why they are doing this.\n", + "
\n", + "### Then I will test the model on the Test set provided to gauge its performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exploring my data\n", + "## Exploring data helps one understand what kind of data is given. That is to say knowing how much data it is, the shape, type, dimensions, missing values, data summary, correlation of attributes among others as am going to do in the following steps." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#here, am trying to find the type of data\n", + "type(Train)\n", + "type(Test)\n", + "Train.dtypes, Test.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((3038, 12), (758, 11))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking the dimensions of the data\n", + "# this retuns the number of rows and columns in the data\n", + "\n", + "Train.shape, Test.shape\n", + "\n", + "#this helps to know how big the data is in terms of rows and columns.\n", + "#also from here I can tell which data is labeled" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data description\n", + ">For now, I will focus more on the train dataset because its what I will use to train the model " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + "0 5.0 0.000 0.951 74.842 \n", + "1 4.0 5.405 0.931 71.595 \n", + "2 5.5 5.405 0.873 73.595 \n", + "3 5.0 4.167 0.895 66.250 \n", + "4 7.5 8.537 0.932 64.720 \n", + "\n", + " FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n", + "0 0.975 -3.663 0 0.282 \n", + "1 0.957 -4.011 1 0.600 \n", + "2 0.961 -2.512 0 0.593 \n", + "3 0.999 -1.362 0 0.614 \n", + "4 0.979 -2.091 0 0.616 \n", + "\n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 CLASS \n", + "0 73.444 5.661 1.041 1 \n", + "1 68.222 6.537 1.453 1 \n", + "2 69.444 4.934 1.722 1 \n", + "3 67.222 4.316 1.382 1 \n", + "4 72.944 4.540 1.539 1 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#looking at the first 5 entries of my data\n", + "Train.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Class Distribution\n", + "> From the Train dataset, i see there is an extra 'CLASS' column which will be my validation set.\n", + "### I need to know how this class is distributed to guide me on what to do with it." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#to see the class distribution, I will plot a bar graph\n", + "Train.groupby('CLASS').size().plot(kind='bar')\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> I would like to know how many instaces i have for each class" + ] + }, + { + "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": [ + "#getting the number of instances in each class\n", + "Train.groupby('CLASS').size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### There are 1519 intances for each class which is proof for even distribution.\n", + "### From the above barplot and class instances, I can see that the classes are evenly distributed so no need to use smote." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Visualisation\n", + "### Data visualization is the technique to present the data in a pictorial or graphical format. It enables one to analyze data visually. The data in a graphical format allows identification of new trends and patterns easily.\n", + "### Visualisation identifies the relationship between data points and variables." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Density plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> I chose to use density plots because they are better at determing the distribution shape as they are not affected by the number of bins as is the case with Histograms." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#now plotting density subplots\n", + "#setting the figsize to 12 so that my graphs are not congested\n", + "Train.plot(kind='density', subplots = True, layout=(3,4), sharex= False, sharey= False, figsize=(12,12))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> From the above density subpots, I can see that most of the data follows a Gaussian distribution given some of the characteristics such as bell shaped curves and graphs being symmetrical about the mean." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Box and whisker plots\n", + "> A box plot is a type of graph that displays a summary of a large amount of data in terms of the median, upper quartile, lower quartile, minimum and maximum data values.\n", + "\n", + "> It handles large data easily, gives a clear summary and displays outliers." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plotting a box and whisker graph\n", + "#setting the figsize to 10 so that the plots are not congested.\n", + "Train.plot(kind='box', subplots = True, layout=(3,4), sharex= False, sharey= False, figsize=(10,10))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Looking at the correlation of the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Correlation is the relationship between two variables. The commonly used method is Pearson's correlation coefficient. It assumes a normal distribution of the attributes involved.\n", + "\n", + "> -1 shows full negative correlation, \n", + "\n", + "> +1 shows full negative correlation and \n", + "\n", + "> 0 shows no correlation at all." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FULL_DAYM780201-0.4346030.5414810.5482531.0000000.0101180.334778-0.090058-0.4087930.8941910.055915-0.326792-0.554838
FULL_GEOR030101-0.0587250.1152010.3461390.0101181.0000000.319157-0.230417-0.160269-0.0290850.040480-0.151935-0.260470
FULL_OOBM850104-0.2837580.5133440.4627120.3347780.3191571.000000-0.230561-0.3362970.275640-0.4527690.155304-0.453287
NT_EFC1950.088068-0.143168-0.169540-0.090058-0.230417-0.2305611.0000000.178683-0.0368440.1459240.0808980.260702
AS_MeanAmphiMoment0.355477-0.431590-0.426097-0.408793-0.160269-0.3362970.1786831.000000-0.3223780.0255800.1715240.693552
AS_DAYM780201-0.3653740.4496210.4562600.894191-0.0290850.275640-0.036844-0.3223781.0000000.045562-0.256060-0.437168
AS_FUKS010112-0.0905700.0023340.0329580.0559150.040480-0.4527690.1459240.0255800.0455621.000000-0.4452840.033432
CT_RACS8201040.232929-0.213543-0.403599-0.326792-0.1519350.1553040.0808980.171524-0.256060-0.4452841.0000000.267652
CLASS0.534602-0.598816-0.584111-0.554838-0.260470-0.4532870.2607020.693552-0.4371680.0334320.2676521.000000
\n", + "
" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 \\\n", + "FULL_Charge 1.000000 -0.612996 -0.490977 \n", + "FULL_AcidicMolPerc -0.612996 1.000000 0.794796 \n", + "FULL_AURR980107 -0.490977 0.794796 1.000000 \n", + "FULL_DAYM780201 -0.434603 0.541481 0.548253 \n", + "FULL_GEOR030101 -0.058725 0.115201 0.346139 \n", + "FULL_OOBM850104 -0.283758 0.513344 0.462712 \n", + "NT_EFC195 0.088068 -0.143168 -0.169540 \n", + "AS_MeanAmphiMoment 0.355477 -0.431590 -0.426097 \n", + "AS_DAYM780201 -0.365374 0.449621 0.456260 \n", + "AS_FUKS010112 -0.090570 0.002334 0.032958 \n", + "CT_RACS820104 0.232929 -0.213543 -0.403599 \n", + "CLASS 0.534602 -0.598816 -0.584111 \n", + "\n", + " FULL_DAYM780201 FULL_GEOR030101 FULL_OOBM850104 \\\n", + "FULL_Charge -0.434603 -0.058725 -0.283758 \n", + "FULL_AcidicMolPerc 0.541481 0.115201 0.513344 \n", + "FULL_AURR980107 0.548253 0.346139 0.462712 \n", + "FULL_DAYM780201 1.000000 0.010118 0.334778 \n", + "FULL_GEOR030101 0.010118 1.000000 0.319157 \n", + "FULL_OOBM850104 0.334778 0.319157 1.000000 \n", + "NT_EFC195 -0.090058 -0.230417 -0.230561 \n", + "AS_MeanAmphiMoment -0.408793 -0.160269 -0.336297 \n", + "AS_DAYM780201 0.894191 -0.029085 0.275640 \n", + "AS_FUKS010112 0.055915 0.040480 -0.452769 \n", + "CT_RACS820104 -0.326792 -0.151935 0.155304 \n", + "CLASS -0.554838 -0.260470 -0.453287 \n", + "\n", + " NT_EFC195 AS_MeanAmphiMoment AS_DAYM780201 \\\n", + "FULL_Charge 0.088068 0.355477 -0.365374 \n", + "FULL_AcidicMolPerc -0.143168 -0.431590 0.449621 \n", + "FULL_AURR980107 -0.169540 -0.426097 0.456260 \n", + "FULL_DAYM780201 -0.090058 -0.408793 0.894191 \n", + "FULL_GEOR030101 -0.230417 -0.160269 -0.029085 \n", + "FULL_OOBM850104 -0.230561 -0.336297 0.275640 \n", + "NT_EFC195 1.000000 0.178683 -0.036844 \n", + "AS_MeanAmphiMoment 0.178683 1.000000 -0.322378 \n", + "AS_DAYM780201 -0.036844 -0.322378 1.000000 \n", + "AS_FUKS010112 0.145924 0.025580 0.045562 \n", + "CT_RACS820104 0.080898 0.171524 -0.256060 \n", + "CLASS 0.260702 0.693552 -0.437168 \n", + "\n", + " AS_FUKS010112 CT_RACS820104 CLASS \n", + "FULL_Charge -0.090570 0.232929 0.534602 \n", + "FULL_AcidicMolPerc 0.002334 -0.213543 -0.598816 \n", + "FULL_AURR980107 0.032958 -0.403599 -0.584111 \n", + "FULL_DAYM780201 0.055915 -0.326792 -0.554838 \n", + "FULL_GEOR030101 0.040480 -0.151935 -0.260470 \n", + "FULL_OOBM850104 -0.452769 0.155304 -0.453287 \n", + "NT_EFC195 0.145924 0.080898 0.260702 \n", + "AS_MeanAmphiMoment 0.025580 0.171524 0.693552 \n", + "AS_DAYM780201 0.045562 -0.256060 -0.437168 \n", + "AS_FUKS010112 1.000000 -0.445284 0.033432 \n", + "CT_RACS820104 -0.445284 1.000000 0.267652 \n", + "CLASS 0.033432 0.267652 1.000000 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#first I will checkfor the pairwise correlation of the attributes.\n", + "Train.corr(method='pearson')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Then now am reviewing the inter-correlation of attributes using heatmap\n", + "#graphical representation\n", + "plt.figure(figsize=(6,6))\n", + "sns.heatmap(Train.corr(method='pearson'))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FULL_Charge 0.534602\n", + "FULL_AcidicMolPerc -0.598816\n", + "FULL_AURR980107 -0.584111\n", + "FULL_DAYM780201 -0.554838\n", + "FULL_GEOR030101 -0.260470\n", + "FULL_OOBM850104 -0.453287\n", + "NT_EFC195 0.260702\n", + "AS_MeanAmphiMoment 0.693552\n", + "AS_DAYM780201 -0.437168\n", + "AS_FUKS010112 0.033432\n", + "CT_RACS820104 0.267652\n", + "CLASS 1.000000\n", + "Name: CLASS, dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Ill also check the correlation in regards to the 'CLASS' attribute\n", + "Train.corr(method='pearson')['CLASS']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Skewnwess of the data\n", + "> knowing data skewness allows one to perform data preparation and improve a model\n", + "\n", + "> If Skewness value lies above +1 or below -1 then the data is highly skewed. \n", + "\n", + "> If skewness value lies between +0.5 and -0.5 then the data ids moderately skewed.\n", + "\n", + "> If skewness is 0 then data is symmetrical" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Checking out skewness of data\n", + "Train.skew().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparing Machine Learning Algorithms\n", + "> This helps to choose the best model for the problem at hand.\n", + "\n", + "> Using resampling methods like cross validation, gives an estimate for how accurate each model may be on unseen data.\n", + "\n", + "> It is important to ensure that each algorithm is evaluated in the same way on the same data to avoid bias." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('LR', 0.9063005464480876, 0.13577589382538835)\n", + "('LDA', 0.9081202185792349, 0.16200541794052895)\n", + "('KNN', 0.8893770491803281, 0.15039644863318388)\n", + "('CART', 0.8810109289617487, 0.12535175208835309)\n", + "('NB', 0.9150163934426229, 0.06671294103051226)\n", + "('SVM', 0.8977868852459018, 0.16388180621798357)\n", + "('AB', 0.9005573770491805, 0.14889029794369535)\n", + "('GBC', 0.9146939890710383, 0.1271369611553431)\n", + "('EXT', 0.9265300546448089, 0.09354977798111189)\n", + "('RTC', 0.9218852459016393, 0.10656696397955046)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#comparing different models to from which ill choose.\n", + "\n", + "\n", + "# load dataset\n", + "\n", + "array = Train.values\n", + "\n", + "#split the dataset \n", + "X = array[:,0:11] #X = Train.drop(columns=['CLASS'])\n", + "Y = array[:,11] #Y = Train['CLASS']\n", + "\n", + "# preparing models and adding them to a list\n", + "models = []\n", + "models.append(('LR', LogisticRegression()))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('SVM', SVC()))\n", + "models.append(('AB', AdaBoostClassifier()))\n", + "models.append(('GBC', GradientBoostingClassifier()))\n", + "models.append(('EXT', ExtraTreesClassifier()))\n", + "models.append(('RTC', RandomForestClassifier()))\n", + "#models.append(('XGB', XGBClassifier)) \n", + "#am commenting out XGB it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' methods.\n", + "\n", + "# evaluating each model in turn\n", + "results = []\n", + "names = []\n", + "\n", + "for name, model in models:\n", + " kfold = KFold(n_splits=50, random_state=42)\n", + " cv_results = cross_val_score(model, X, Y, cv=kfold, scoring='accuracy')\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " msg = (name, cv_results.mean(), cv_results.std())\n", + " print(msg)\n", + "\n", + "# boxplot algorithm comparison\n", + "fig = pyplot.figure()\n", + "fig.suptitle('Algorithm Comparison')\n", + "ax = fig.add_subplot(111)\n", + "pyplot.boxplot(results)\n", + "ax.set_xticklabels(names)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> ## From the above algorithm comparison, I can see that Naive Bayes(NB), ExtraTreesClassifiers(EXT) and RandomForestClassifiers(RTC) are some of the best performing algorithms. \n", + "> ## Am yet to find out the overall best valgorithm after prediction on the Test set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature selection \n", + "## Using recursive feature elimination\n", + "> Sometimes, you may asses a dataset and find out that you do not need to use all the given features. This can be because some of them are highly correlates or they are not in any way helpful in developing the model.\n", + "* It is therefore important to get rid of some features where applicable.\n", + "\n", + "> For this data, I will use Recursive Feature Elimination(RFE)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num Features: 4\n", + "Selected Features: [False False True False False True True False False False True]\n", + "Feature Ranking: [3 7 1 6 2 1 1 4 8 5 1]\n" + ] + } + ], + "source": [ + "#feature selection using RFE\n", + "#first i will start with choosing 4 features\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = LogisticRegression()\n", + "rfe = RFE(model, 4)\n", + "fit = rfe.fit(X, Y)\n", + "print(\"Num Features: \", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking: \", fit.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n", + " 'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n", + " 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n", + " 'CLASS'],\n", + " dtype='object')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Calling out the column names so I can know which features am going to drop from RFE\n", + "Train.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.08227437 0.14418508 0.08895859 0.08005117 0.05746269 0.07433544\n", + " 0.03416612 0.30382632 0.05147578 0.03226204 0.0510024 ]\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import ExtraTreesClassifier\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = ExtraTreesClassifier()\n", + "model.fit(X, Y)\n", + "print(model.feature_importances_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## If am to use 4 features and drop the rest\n", + "> I will assign the new dataset a variable 'New_Train4' after choosing the 4 features and dropping the rest." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# I will call the new Train data with selected features New_Train4.\n", + "Train\n", + "New_Train4 = Train.drop(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_DAYM780201', 'FULL_GEOR030101', 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112'], axis =1)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#dropping the same features in the test dataset\n", + "New_Test4 = Test.drop(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_DAYM780201', 'FULL_GEOR030101', 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112'], axis =1)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_AURR980107', 'FULL_OOBM850104', 'NT_EFC195', 'CT_RACS820104',\n", + " 'CLASS'],\n", + " dtype='object')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#viewing the selected features\n", + "New_Train4.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "#now splitting data\n", + "#array = New_Train.values\n", + "X = New_Train4.values[:,0:4]\n", + "Y = New_Train4.values[:,4]\n", + "from sklearn.model_selection import train_test_split\n", + "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The result is: 62.28 Mathew's Coef\n" + ] + } + ], + "source": [ + "# also train and test the model on Matthews correlation coefficient.\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "GS = GaussianNB()\n", + "GS.fit(X_Train,Y_Train)\n", + "pred = GS.predict(X_Test)\n", + "\n", + "print(\"The result is: \",np.round(matthews_corrcoef(Y_Test,pred) *100,2),\" Mathew's Coef\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using four features gives me a very low MCC and low overall score.\n", + "## I'll therefore consider using 8 features and see what score I get." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = LogisticRegression()\n", + "rfe = RFE(model, 8)\n", + "fit = rfe.fit(X, Y)\n", + "print(\"Num Features: \", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking: \", fit.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# I will call the new Train data with selected features New_Train8.\n", + "Train\n", + "New_Train8 = Train.drop(['FULL_AcidicMolPerc', 'FULL_DAYM780201', 'AS_DAYM780201'], axis =1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#dropping the same features in the test dataset\n", + "New_Test8 = Test.drop(['FULL_AcidicMolPerc', 'FULL_DAYM780201', 'AS_DAYM780201'], axis =1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#now splitting data\n", + "#array = New_Train.values\n", + "\n", + "\n", + "X = New_Train8.values[:,0:8]\n", + "Y = New_Train8.values[:,8]\n", + "from sklearn.model_selection import train_test_split\n", + "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#now creating a model and training it on 8 features\n", + "model = LogisticRegression(solver='liblinear', C=0.05, multi_class='ovr', random_state=30)\n", + "model.fit(X_Train, Y_Train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_Train, X_Test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2,\n", + "random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "nv = GaussianNB()\n", + "nv.fit(X_Train,Y_Train)\n", + "pred = nv.predict(X_Test)\n", + "\n", + "print(\"The result is: \",np.round(matthews_corrcoef(Y_Test,pred) *100,2),\" Mathew's Coef\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Standardising data\n", + "> This is useful to transform attributes with a Gaussian distribution and it workd better with rescaled data(also known as normalistaion where attributes are scaled into a range between 0 and 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Below are some of the algorithms I will be using;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LINEAR ALGORITHMS\n", + "1. Linear Regression\n", + "2. Logistic Regression\n", + "3. Linear Discriminant Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression using 8 features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# here am using a dataset 'New_Train8' with 8 selected features\n", + "array = New_Train8.values\n", + "X = array[:,0:8]\n", + "Y = array[:,8]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42) #spliiting my data into 10 folds and random_state of 42 for reproducibility\n", + "model = LogisticRegression() #calling out the prediction algorithm\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(New_Test8.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_logistic = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_logistic.columns = ['CLASS'] #renaming the output column to 'CLASS'\n", + "maria_logistic.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_logistic['CLASS']=maria_logistic['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_logistic.to_csv(\"maria_logistic.csv\") #changing my output file as a 'csv' file\n", + "\n", + "print(maria_logistic['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria_logistic.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_logistic.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NOTE\n", + "> I have noticed that for this particular dataset, the more I drop features, the prediction scores also keep decreasing.\n", + "\n", + "> With 4 features selected, the score was low, with 8 features, the score improved and with all features, the prediction score was high.\n", + "\n", + "> I therefore decided to work with all the features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Linear Dicriminant Analysis (LDA) using all features " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "num_folds = 10\n", + "kfold = KFold(n_splits=10, random_state=42) #spliiting my data into 10 folds and random_state of 42 for reproducibility\n", + "model = LinearDiscriminantAnalysis() #calling out the prediction algorithm\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_LDA = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_LDA.columns = ['CLASS'] #renaming the output column to 'CLASS'\n", + "maria_LDA.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_LDA['CLASS']=maria_LDA['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_LDA.to_csv(\"maria_LDA.csv\") #changing my output file as a 'csv' file\n", + "\n", + "print(maria_LDA['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria_LDA.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_LDA.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NON LINEAR ALGORITHMS\n", + "1. Naive Bayes\n", + "2. Support Vector Machines\n", + "3. K-Nearest Neighbours\n", + "4. Classification and Regression Trees\n", + "5. Learning Vector Quantization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Naive Bayes and all the features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "test_size = 0.35 \n", + "\n", + "kfold = KFold(n_splits=10) #spliiting my data into 10 folds \n", + "\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=42) #random_state of 42 for reproducibility\n", + "\n", + "model = GaussianNB() #calling out the prediction algorithm\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria2_bayes = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria2_bayes.columns = ['CLASS'] #renaming the output column to 'CLASS'\n", + "maria2_bayes.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria2_bayes['CLASS']=maria2_bayes['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria2_bayes.to_csv(\"maria2_bayes.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria2_bayes['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria2_bayes.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria2_bayes.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Support Vector Machines algorithm using all features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "kfold = KFold(n_splits=10) #spliiting my data into 10 folds\n", + "\n", + "model = SVC() #calling out the prediction algorithm\n", + "scoring = 'acuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "maria_svc = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_svc.columns = ['CLASS'] #renaming the output column to 'CLASS'\n", + "maria_svc.index.name = 'Index' #naming the index column as 'Index'\n", + "maria_svc['CLASS'] = maria_svc['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "\n", + "maria_svc.to_csv('maria_svc.csv') #changing my output file as a 'csv' file\n", + "\n", + "print(maria_svc['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria_svc.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_svc.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Classification and Regression Trees" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "kfold = KFold(n_splits=10, random_state=42) #spliiting my data into 10 folds and random_state of 42 for reproducibility\n", + "model = DecisionTreeClassifier() #calling out the prediction algorithm\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "\n", + "model.fit(X,Y)\n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_tree = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_tree.columns = ['CLASS'] #renaming the output column to 'CLASS'\n", + "maria_tree.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_tree['CLASS']=maria_tree['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_tree.to_csv(\"maria_tree.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria_tree['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria_tree.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_tree.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# K-Nearest Neighbours" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42) #spliiting my data into 10 folds and random_state of 42 for reproducibility\n", + "model = KNeighborsClassifier() #calling out the prediction algorithm\n", + "results = cross_val_score(model, X, Y, cv=kfold) #estimating the model on new data and assigning it to results variable\n", + "print(results.mean()) #getting the mean of the accuracy scores from cross validation scores\n", + "\n", + "model.fit(X,Y) \n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_knn = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_knn.columns = ['CLASS'] #renaming the output column to 'CLASS' \n", + "maria_knn.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_knn['CLASS']=maria_knn['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_knn.to_csv(\"maria_knn.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria_knn['CLASS'].unique()) #checking out the unique instances in the 'CLASS' column\n", + "print('False: ',maria_knn.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_knn.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Esemble Algorithms\n", + "1. Boosting and Adaboost\n", + "2. Bagging and Random forest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Adaboost " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "\n", + "model = AdaBoostClassifier(n_estimators=200, random_state=42) \n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "\n", + "print(results.mean())\n", + "\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "maria_AB= pd.DataFrame(output)\n", + "maria_AB.columns = ['CLASS']\n", + "maria_AB.index.name = 'Index'\n", + "maria_AB['CLASS'] = maria_AB['CLASS'].map({0.0:False, 1.0:True})\n", + "\n", + "maria_AB.to_csv('maria_AB.csv')\n", + "\n", + "print(maria_AB['CLASS'].unique())\n", + "print('False: ',maria_AB.groupby('CLASS').size()[0].sum())\n", + "print('True: ',maria_AB.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Gradient Boosting Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "#num_trees = 200\n", + "#seed =42\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = GradientBoostingClassifier(n_estimators=200, random_state=42)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X,Y) \n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_GBC = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_GBC.columns = ['CLASS'] #renaming the output column to 'CLASS' \n", + "maria_GBC.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_GBC['CLASS']=maria_GBC['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_GBC.to_csv(\"maria_GBC.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria_GBC['CLASS'].unique()) #checking out the unique instances in the \n", + "print('False: ',maria_GBC.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_GBC.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Stochastic Gradient Boosting Classification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = XGBClassifier(n_estimators=200, random_state=42)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X,Y) \n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_XGB = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_XGB.columns = ['CLASS'] #renaming the output column to 'CLASS' \n", + "maria_XGB.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_XGB['CLASS']=maria_XGB['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_XGB.to_csv(\"maria_XGB.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria_GBC['CLASS'].unique()) #checking out the unique instances in the \n", + "print('False: ',maria_XGB.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_XGB.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Extra Trees Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "array = Train.values\n", + "\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = ExtraTreesClassifier(n_estimators=200) # (max_features=11)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + " \n", + "model.fit(X,Y) \n", + "output = model.predict(Test.values) #testing the model on the test_set (Test.values)\n", + "\n", + "from sklearn.metrics import matthews_corrcoef\n", + "mcc = matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',mcc)\n", + " \n", + "maria_ETX = pd.DataFrame(output) #here we are converting the array into a pandas dataframe which will give a single column\n", + "maria_ETX.columns = ['CLASS'] #renaming the output column to 'CLASS' \n", + "maria_ETX.index.name = \"Index\" #naming the index column as 'Index'\n", + "maria_ETX['CLASS']=maria_ETX['CLASS'].map({0.0:False, 1.0:True}) #converting '0.0' to' False' and '1.0' to 'True'\n", + "maria_ETX.to_csv(\"maria_ETX.csv\") #changing my output file as a 'csv' file\n", + "\n", + "\n", + "print(maria_ETX['CLASS'].unique()) #checking out the unique instances in the \n", + "print('False: ',maria_ETX.groupby('CLASS').size()[0].sum()) #summing up the '0' instances in the 'CLASS' column\n", + "print('True: ',maria_ETX.groupby('CLASS').size()[1].sum()) #summing up the '1' instances in the 'CLASS' column " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CONCLUSION\n", + "## From the algorithm comparisons and my prediction scores, Naive Bayes algorithm performed the best.\n", + "## I think this is because the data was following a Gaussian distribution(normally distributed) as seen earlier from the density subplots." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# REFERENCES\n", + "1. https://realpython.com/logistic-regression-python/\n", + "2. https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_extra_trees.htm\n", + "3. https://stackabuse.com/gradient-boosting-classifiers-in-python-with-scikit-learn/\n", + "4. https://machinelearningmastery.com/stochastic-gradient-boosting-xgboost-scikit-learn-python/\n", + "5. https://machinelearningmastery.com/stochastic-gradient-boosting-xgboost-scikit-learn-python/\n", + "6. https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/\n", + "7. https://machinelearningmastery.com/master-machine-learning-algorithms/\n", + "8. https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/\n", + "9. https://github.com/search?q=data-analysis-and-visualization-with-python&type=Repositories\n", + "10. https://stackabuse.com/applying-filter-methods-in-python-for-feature-selection/\n", + "11. https://towardsdatascience.com/decision-tree-in-machine-learning-e380942a4c96\n", + "12. https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", 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+751,False +752,False +753,False +754,False +755,False +756,False +757,False diff --git a/AMP Data Sets/sample_handin.csv b/Data/sample_handin.csv similarity index 100% rename from AMP Data Sets/sample_handin.csv rename to Data/sample_handin.csv diff --git a/Dimensionality_Reduction_DataCamp.ipynb b/Dimensionality_Reduction_DataCamp.ipynb new file mode 100644 index 0000000..7a2245f --- /dev/null +++ b/Dimensionality_Reduction_DataCamp.ipynb @@ -0,0 +1,1103 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Dimensionality Reduction DataCamp", + "provenance": [], + "authorship_tag": "ABX9TyOpbxCKdT5L31c28gvonWQ/", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q8DGq2UXFmtZ", + "colab_type": "text" + }, + "source": [ + "## When you want to visually explore the patterns in a high dimensional dataset.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lSEn6j5J9_sp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 71 + }, + "outputId": "c293a1bf-47bf-4979-8832-3393bbbab272" + }, + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hHvkBmNd-eDf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#read in the data\n", + "data = pd.read_csv(\"https://assets.datacamp.com/production/repositories/3515/datasets/802fc5cdbe3a29248483e496a966627ea9629e7a/ANSUR_II_FEMALE.csv\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "aKVH0pf7-ivt", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 276 + }, + "outputId": "cc307b73-1383-43c0-bff9-19864910f457" + }, + "source": [ + "#look at the head\n", + "data.head(3)" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Branch Component ... BMI_class Height_class\n", + "0 Combat Support Regular Army ... Overweight Normal\n", + "1 Combat Service Support Regular Army ... Normal Normal\n", + "2 Combat Service Support Regular Army ... Normal Tall\n", + "\n", + "[3 rows x 99 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "b0tgs_d2-kSL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "25e1854a-adf0-42f4-8059-de774ed8d161" + }, + "source": [ + "#shape of the data\n", + "data.shape" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(1986, 99)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cSFn6Dy5-osm", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#lets take only the numeric data\n", + "dt = data.select_dtypes(exclude='object')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "RKy8oymF_FHb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "02874db7-8fab-4caa-9bcd-98c7e3274115" + }, + "source": [ + "#new shape\n", + "dt.shape" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(1986, 94)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Lq8CyCvX_RFJ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#get t-SNE\n", + "from sklearn.manifold import TSNE\n", + "\n", + "m = TSNE(learning_rate=50)\n", + "\n", + "tsne_features = m.fit_transform(dt)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nTOXOr_EA9qq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "0a74ac0c-922c-4f36-e7db-27feb549c1c7" + }, + "source": [ + "#it reduces the features into a two dimensional one.\n", + "tsne_features[1:4,:]" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ -4.6460032, -40.48791 ],\n", + " [-19.894072 , -27.56942 ],\n", + " [-24.738132 , 7.966632 ]], dtype=float32)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6d57vuFdGnHX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 500 + }, + "outputId": "7c7ff86f-6e42-4f93-9920-f2c361b11c2a" + }, + "source": [ + "plt.figure(figsize=(12,8))\n", + "plt.scatter(x=tsne_features[:,0],y=tsne_features[:,1])\n", + "\n", + "#I have to find a way to put these results in the dt dataframe and work with them there\n", + "#I would want to visualize how heigh affects the distribution e.t.c" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "U6pFJduuG1DY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "OndV7f7ILZUY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3-H1pSkrLaHX", + "colab_type": "text" + }, + "source": [ + "# Removing Features\n", + "\n", + "1 -Variance Threshold\n", + "\n", + "2 -High number of missing values." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_EtuAT8LLgcY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "d7448d8f-5086-46e5-c70b-d484589fd870" + }, + "source": [ + "#let's draw the boxplot to show variance\n", + "#variance is affected by different scales of data so we normalize the data by dividing by the mean\n", + "dt_norm = dt/dt.mean()\n", + "plt.figure(figsize = (18,14))\n", + "dt_norm.boxplot()\n", + "plt.xticks(rotation='vertical')\n", + "#we can see the variance normalized at 10" + ], + "execution_count": 46, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n", + " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n", + " 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n", + " 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n", + " 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,\n", + " 86, 87, 88, 89, 90, 91, 92, 93, 94]),\n", + " )" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 46 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qYIMp9BtMNf6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "outputId": "2669168e-5abf-458c-f353-cc74e1d90c51" + }, + "source": [ + "#let's see the variances of all the features\n", + "list(dt_norm.var().sort_values(ascending=False))[:20]" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.026281388190225897,\n", + " 0.021689614994511382,\n", + " 0.01877197853310506,\n", + " 0.01875529617208825,\n", + " 0.016963347661495994,\n", + " 0.013473994470864599,\n", + " 0.012755787812620725,\n", + " 0.01221022494210274,\n", + " 0.012078548319412467,\n", + " 0.010909104241888172,\n", + " 0.010130786183765356,\n", + " 0.008204608384362806,\n", + " 0.007700789927583826,\n", + " 0.007628374548528208,\n", + " 0.007014264997366014,\n", + " 0.0070079680463572885,\n", + " 0.00695285389983951,\n", + " 0.006869515365066108,\n", + " 0.006738086346872471,\n", + " 0.006634223006816111]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 34 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "foeUF8AcRHGj", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.feature_selection import VarianceThreshold\n", + "\n", + "# Create a VarianceThreshold feature selector\n", + "sel = VarianceThreshold(threshold=0.01)\n", + "\n", + "# Fit the selector to normalized head_df\n", + "sel.fit(dt / dt.mean())\n", + "\n", + "# Create a boolean mask\n", + "mask = sel.get_support()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "BcTGX-ukSc0V", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#let's take these values that qualify from the dataset\n", + "#having a high variance means the feature spread touches different subcategories.\n", + "data = dt.loc[:,mask]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1nmdXHB1Spp-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 656 + }, + "outputId": "ccc123cf-5e8d-4fcf-ba8a-e55897e1b7e7" + }, + "source": [ + "#let's visualize this in a heatmap\n", + "plt.figure(figsize = (10,8))\n", + "sns.heatmap(data.corr(), center=0, linewidths=1, annot=True, fmt=\".2f\")" + ], + "execution_count": 39, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 39 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Eryh7wciVtuJ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Create the correlation matrix\n", + "corr = data.corr()\n", + "\n", + "# Generate a mask for the upper triangle \n", + "mask = np.triu(np.ones_like(corr, dtype=bool))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z8SQ2YDgXL3p", + "colab_type": "text" + }, + "source": [ + "# Notes:\n", + "Features that are perfects correlated: positive or negative bring no new information to the model but increase the complexity of the model we have to drop one.\n", + "\n", + "Strong correlation does not imply causation.\n", + "\n", + "Make sure you visually look at the diagrams of the scatter plots to see the relationship in the variables because sometimes its non linear and still the correlation is high, e.g in the image.\n", + "\n", + "---\n", + "\n", + "We can drop highly correlated features like this:\n", + "\n", + "\n", + "\n", + "```\n", + "# Calculate the correlation matrix and take the absolute value\n", + "corr_matrix = ansur_df.corr().abs()\n", + "\n", + "# Create a True/False mask and apply it\n", + "mask = np.triu(np.ones_like(corr_matrix, dtype=bool))\n", + "tri_df = corr_matrix.mask(mask)\n", + "\n", + "# List column names of highly correlated features (r > 0.95)\n", + "to_drop = [c for c in tri_df.columns if any(tri_df[c] > 0.95)]\n", + "\n", + "# Drop the features in the to_drop list\n", + "reduced_df = ansur_df.drop(to_drop, axis=1)\n", + "\n", + "print(\"The reduced_df dataframe has {} columns\".format(reduced_df.shape[1]))\n", + "\n", + "```\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1FobyjoRWAum", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 639 + }, + "outputId": "ac4b12f4-d579-41ff-98a1-c9a47d3e4b6d" + }, + "source": [ + "# Add the mask to the heatmap\n", + "plt.figure(figsize = (12,8))\n", + "sns.heatmap(corr, mask=mask, center=0, linewidths=1, annot=True, fmt=\".2f\")\n", + "plt.show()" + ], + "execution_count": 42, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "SVFa58q3WIR7", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MS8nE2xic_PG", + "colab_type": "text" + }, + "source": [ + "#Select Features based on Importance.\n", + "\n", + "## RFE:-\n", + "\n", + "How does it work?\n", + "\n", + "It is applied on linear algorithms that have feature coefficients as we shall see below.\n", + "\n", + "It then removes each feature that has tending to 0 coefficient one by one.\n", + "\n", + "```\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "scaler = StandardScaler()\n", + "\n", + "# Fit the scaler on the training features and transform these in one go\n", + "X_train_std = scaler.fit_transform(X_train)\n", + "\n", + "# Fit the logistic regression model on the scaled training data\n", + "lr.fit(X_train_std, y_train)\n", + "\n", + "# Scale the test features\n", + "X_test_std = scaler.transform(X_test)\n", + "\n", + "# Predict diabetes presence on the scaled test set\n", + "y_pred = lr.predict(X_test_std)\n", + "\n", + "# Prints accuracy metrics and feature coefficients\n", + "print(\"{0:.1%} accuracy on test set.\".format(accuracy_score(y_test, y_pred))) \n", + "print(dict(zip(X.columns, abs(lr.coef_[0]).round(2))))\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PWXlZoYGe9EO", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-wyn0PnqjdjA", + "colab_type": "text" + }, + "source": [ + "\n", + "\n", + "```\n", + "# Set the feature eliminator to remove 2 features on each step\n", + "rfe = RFE(estimator=RandomForestClassifier(), n_features_to_select=2, step=2, verbose=1)\n", + "\n", + "# Fit the model to the training data\n", + "rfe.fit(X_train, y_train)\n", + "\n", + "# Create a mask\n", + "mask = rfe.support_\n", + "\n", + "# Apply the mask to the feature dataset X and print the result\n", + "reduced_X = X.loc[:, mask]\n", + "print(reduced_X.columns)\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bwThjfxbjhiH", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qnV8zH1Uo2gQ", + "colab_type": "text" + }, + "source": [ + "# Feature Extraction\n", + "\n", + "Calculating New features from the old" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bdHhkPgQo8SK", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Eva Akurut.final.ipynb b/Eva Akurut.final.ipynb new file mode 100644 index 0000000..8d8e19b --- /dev/null +++ b/Eva Akurut.final.ipynb @@ -0,0 +1,3365 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ACE_COMPETITION\n", + "#### Eva Akurut\n", + "#### REG Number:2019/HD07/24850U\n", + "\n", + "#### Exploration Data Analysis assignment\n", + "* Import the datasets into the notebook\n", + "* Find out the Dimensions of the data imported\n", + "* Descibe the data using statistics(Descriptive statistics)\n", + "* Looking at how the variables correlate with each other\n", + "* Data Visualisation\n", + "* Preparation of data for Machine Learning\n", + "* Evaluate the Performance of Machine Learning Algorithms with Resampling\n", + "* Spot-Checking Classiffication Algorithms" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "outputs": [], + "source": [ + "# Importing the tools to use for the assignment\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data structure analysis\n", + "import matplotlib.pyplot as plt # for visualisation\n", + "import seaborn as sns#\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1)Loading the data to be used into my notebook\n", + "The data to be analyzed was read in using the pandas method of pd.read_csv(),\n", + "* Two data sets were loaded in AMP_Trainset.csv \n", + "* Test.csv\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "###Importing the data sets into the notebook using read.csv\n", + "Train=pd.read_csv(\"Data/AMP_TrainSet.csv\")\n", + "Test=pd.read_csv(\"Data/Test.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2)Checking the dimensions of my data\n", + "* Dimensions give a deep insight into the data to be analysed\n", + "* It helps to give sumarry of all the data types present in our data using data.info()function\n", + "* Determines the Number of columns and rows using data.shape()function\n", + "* Finds out if there are missing values in the data using is.null()function\n", + "* The data.head() Funstion gives mre the top 5 rows with all the columns present" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + "0 5.0 0.000 0.951 74.842 \n", + "1 4.0 5.405 0.931 71.595 \n", + "2 5.5 5.405 0.873 73.595 \n", + "3 5.0 4.167 0.895 66.250 \n", + "4 7.5 8.537 0.932 64.720 \n", + "\n", + " FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n", + "0 0.975 -3.663 0 0.282 \n", + "1 0.957 -4.011 1 0.600 \n", + "2 0.961 -2.512 0 0.593 \n", + "3 0.999 -1.362 0 0.614 \n", + "4 0.979 -2.091 0 0.616 \n", + "\n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 CLASS \n", + "0 73.444 5.661 1.041 1 \n", + "1 68.222 6.537 1.453 1 \n", + "2 69.444 4.934 1.722 1 \n", + "3 67.222 4.316 1.382 1 \n", + "4 72.944 4.540 1.539 1 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Train.head(5)\n", + "# To check the first 5 rows on my dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Checking the datatype of of my data to determine the class of my data\n", + "Train.dtypes ,Test.dtypes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((3038, 12), (758, 11))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Finding out the rows and columns present in my data set \n", + "Train.shape , Test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 3038 entries, 0 to 3037\n", + "Data columns (total 12 columns):\n", + "FULL_Charge 3038 non-null float64\n", + "FULL_AcidicMolPerc 3038 non-null float64\n", + "FULL_AURR980107 3038 non-null float64\n", + "FULL_DAYM780201 3038 non-null float64\n", + "FULL_GEOR030101 3038 non-null float64\n", + "FULL_OOBM850104 3038 non-null float64\n", + "NT_EFC195 3038 non-null int64\n", + "AS_MeanAmphiMoment 3038 non-null float64\n", + "AS_DAYM780201 3038 non-null float64\n", + "AS_FUKS010112 3038 non-null float64\n", + "CT_RACS820104 3038 non-null float64\n", + "CLASS 3038 non-null int64\n", + "dtypes: float64(10), int64(2)\n", + "memory usage: 284.9 KB\n" + ] + } + ], + "source": [ + "# Printing the summary of the data frame\n", + "# Summary includes list of all columns with their data types and the number of non-null values in each column. \n", + "#we also have the value of rangeindex provided for the index axis.\n", + "Train.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "( FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + " 0 False False False False \n", + " 1 False False False False \n", + " 2 False False False False \n", + " 3 False False False False \n", + " 4 False False False False \n", + " 5 False False False False \n", + " 6 False False False False \n", + " 7 False False False False \n", + " 8 False False False False \n", + " 9 False False False False \n", + " 10 False False False False \n", + " 11 False False False False \n", + " 12 False False False False \n", + " 13 False False False False \n", + " 14 False False False False \n", + " 15 False False False False \n", + " 16 False False False False \n", + " 17 False False False False \n", + " 18 False False False False \n", + " 19 False False False False \n", + " 20 False False False False \n", + " 21 False False False False \n", + " 22 False False False False \n", + " 23 False False False False \n", + " 24 False False False False \n", + " 25 False False False False \n", + " 26 False False False False \n", + " 27 False False False False \n", + " 28 False False False False \n", + " 29 False False False False \n", + " ... ... ... ... ... \n", + " 3008 False False False False \n", + " 3009 False False False False \n", + " 3010 False False False False \n", + " 3011 False False False False \n", + " 3012 False False False False \n", + " 3013 False False False False \n", + " 3014 False False 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False \n", + " 1 False False False False \n", + " 2 False False False False \n", + " 3 False False False False \n", + " 4 False False False False \n", + " 5 False False False False \n", + " 6 False False False False \n", + " 7 False False False False \n", + " 8 False False False False \n", + " 9 False False False False \n", + " 10 False False False False \n", + " 11 False False False False \n", + " 12 False False False False \n", + " 13 False False False False \n", + " 14 False False False False \n", + " 15 False False False False \n", + " 16 False False False False \n", + " 17 False False False False \n", + " 18 False False False False \n", + " 19 False False False False \n", + " 20 False False False False \n", + " 21 False False False False \n", + " 22 False False False False \n", + " 23 False False False False \n", + " 24 False False False False \n", + " 25 False False False False \n", + " 26 False False False False \n", + " 27 False False False False \n", + " 28 False False False 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False False False False \n", + " 3033 False False False False \n", + " 3034 False False False False \n", + " 3035 False False False False \n", + " 3036 False False False False \n", + " 3037 False False False False \n", + " \n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 CLASS \n", + " 0 False False False False \n", + " 1 False False False False \n", + " 2 False False False False \n", + " 3 False False False False \n", + " 4 False False False False \n", + " 5 False False False False \n", + " 6 False False False False \n", + " 7 False False False False \n", + " 8 False False False False \n", + " 9 False False False False \n", + " 10 False False False False \n", + " 11 False False False False \n", + " 12 False False False False \n", + " 13 False False False False \n", + " 14 False False False False \n", + " 15 False False False False \n", + " 16 False False False False \n", + " 17 False False False False \n", + " 18 False False False False \n", + " 19 False False False False \n", + " 20 False False False False \n", + " 21 False False False False \n", + " 22 False False False False \n", + " 23 False False False False \n", + " 24 False False False False \n", + " 25 False False False False \n", + " 26 False False False False \n", + " 27 False False False False \n", + " 28 False False False False \n", + " 29 False False False False \n", + " ... ... ... ... ... \n", + " 3008 False False False False \n", + " 3009 False False False False \n", + " 3010 False False False False \n", + " 3011 False False False False \n", + " 3012 False False False False \n", + " 3013 False False False False \n", + " 3014 False False False False \n", + " 3015 False False False False \n", + " 3016 False False False False \n", + " 3017 False False False False \n", + " 3018 False False False False \n", + " 3019 False False False False \n", + " 3020 False False False False \n", + " 3021 False False False False \n", + " 3022 False False False False \n", + " 3023 False False False False \n", + " 3024 False False False False \n", + " 3025 False False False False \n", + " 3026 False False False False \n", + " 3027 False False False False \n", + " 3028 False False False False \n", + " 3029 False False False False \n", + " 3030 False False False False \n", + " 3031 False False False False \n", + " 3032 False False False False \n", + " 3033 False False False False \n", + " 3034 False False False False \n", + " 3035 False False False False \n", + " 3036 False False False False \n", + " 3037 False False False False \n", + " \n", + " [3038 rows x 12 columns],\n", + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + " 0 False False False False \n", + " 1 False False False False \n", + " 2 False False False False \n", + " 3 False False False False \n", + " 4 False False False False \n", + " 5 False False False False \n", + " 6 False False False False \n", + " 7 False False False False \n", + " 8 False False False False \n", + " 9 False False False False \n", + " 10 False False False False \n", + " 11 False False False False \n", + " 12 False False False False \n", + " 13 False False False False \n", + " 14 False False False False \n", + " 15 False False False False \n", + " 16 False False False False \n", + " 17 False False False False \n", + " 18 False False False False \n", + " 19 False False False False \n", + " 20 False False False False \n", + " 21 False False False False \n", + " 22 False False False False \n", + " 23 False False False False \n", + " 24 False False False False \n", + " 25 False False False False \n", + " 26 False False False False \n", + " 27 False False False False \n", + " 28 False False False False \n", + " 29 False False False False \n", + " .. ... ... ... ... \n", + " 728 False False False False \n", + " 729 False False False False \n", + " 730 False False False False \n", + " 731 False False False False \n", + " 732 False False False False \n", + " 733 False False False False \n", + " 734 False False False False \n", + " 735 False False False False \n", + " 736 False False False False \n", + " 737 False False False False \n", + " 738 False False False False \n", + " 739 False False False False \n", + " 740 False False False False \n", + " 741 False False False False \n", + " 742 False False False False \n", + " 743 False False False False \n", + " 744 False False False False \n", + " 745 False False False False \n", + " 746 False False False False \n", + " 747 False False False False \n", + " 748 False False False False \n", + " 749 False False False False \n", + " 750 False False False False \n", + " 751 False False False False \n", + " 752 False False False False \n", + " 753 False False False False \n", + " 754 False False False False \n", + " 755 False False False False \n", + " 756 False False False False \n", + " 757 False False False False \n", + " \n", + " FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n", + " 0 False False False False \n", + " 1 False False False False \n", + " 2 False False False False \n", + " 3 False False False False \n", + " 4 False False False False \n", + " 5 False False False False \n", + " 6 False False False False \n", + " 7 False False False False \n", + " 8 False False False False \n", + " 9 False False False False \n", + " 10 False False False False \n", + " 11 False False False False \n", + " 12 False False False False \n", + " 13 False False False False \n", + " 14 False False False False \n", + " 15 False False False False \n", + " 16 False False False False \n", + " 17 False False False False \n", + " 18 False False False False \n", + " 19 False False False False \n", + " 20 False False False False \n", + " 21 False False False False \n", + " 22 False False False False \n", + " 23 False False False False \n", + " 24 False False False False \n", + " 25 False False False False \n", + " 26 False False False False \n", + " 27 False False False False \n", + " 28 False False False False \n", + " 29 False False False False \n", + " .. ... ... ... ... \n", + " 728 False False False False \n", + " 729 False False False False \n", + " 730 False False False False \n", + " 731 False False False False \n", + " 732 False False False False \n", + " 733 False False False False \n", + " 734 False False False False \n", + " 735 False False False False \n", + " 736 False False False False \n", + " 737 False False False False \n", + " 738 False False False False \n", + " 739 False False False False \n", + " 740 False False False False \n", + " 741 False False False False \n", + " 742 False False False False \n", + " 743 False False False False \n", + " 744 False False False False \n", + " 745 False False False False \n", + " 746 False False False False \n", + " 747 False False False False \n", + " 748 False False False False \n", + " 749 False False False False \n", + " 750 False False False False \n", + " 751 False False False False \n", + " 752 False False False False \n", + " 753 False False False False \n", + " 754 False False False False \n", + " 755 False False False False \n", + " 756 False False False False \n", + " 757 False False False False \n", + " \n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 \n", + " 0 False False False \n", + " 1 False False False \n", + " 2 False False False \n", + " 3 False False False \n", + " 4 False False False \n", + " 5 False False False \n", + " 6 False False False \n", + " 7 False False False \n", + " 8 False False False \n", + " 9 False False False \n", + " 10 False False False \n", + " 11 False False False \n", + " 12 False False False \n", + " 13 False False False \n", + " 14 False False False \n", + " 15 False False False \n", + " 16 False False False \n", + " 17 False False False \n", + " 18 False False False \n", + " 19 False False False \n", + " 20 False False False \n", + " 21 False False False \n", + " 22 False False False \n", + " 23 False False False \n", + " 24 False False False \n", + " 25 False False False \n", + " 26 False False False \n", + " 27 False False False \n", + " 28 False False False \n", + " 29 False False False \n", + " .. ... ... ... \n", + " 728 False False False \n", + " 729 False False False \n", + " 730 False False False \n", + " 731 False False False \n", + " 732 False False False \n", + " 733 False False False \n", + " 734 False False False \n", + " 735 False False False \n", + " 736 False False False \n", + " 737 False False False \n", + " 738 False False False \n", + " 739 False False False \n", + " 740 False False False \n", + " 741 False False False \n", + " 742 False False False \n", + " 743 False False False \n", + " 744 False False False \n", + " 745 False False False \n", + " 746 False False False \n", + " 747 False False False \n", + " 748 False False False \n", + " 749 False False False \n", + " 750 False False False \n", + " 751 False False False \n", + " 752 False False False \n", + " 753 False False False \n", + " 754 False False False \n", + " 755 False False False \n", + " 756 False False False \n", + " 757 False False False \n", + " \n", + " [758 rows x 11 columns])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Verifying if there are no missing values in the data\n", + "Train.isnull(), Test.isnull()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# visualization to check for missing data\n", + "import missingno as msno\n", + "msno.bar(Train)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n", + " 'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n", + " 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n", + " 'CLASS'],\n", + " dtype='object')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Printing the column names to get to know the names of the columns\n", + "Train.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* In general, both the train and Test datasets have float and intenger data types\n", + "* The Train Data set has 3038 rows and 12 columns\n", + "* The Test Data set has 758 rows plus 11 columns\n", + "* The data has no missing values since the data.info() as well as the isnull() show none missing\n", + "* Having obtained the above results, It shows that i should classify my data using the column not preset in the Test data set \n", + "* There is no need for further data cleaning since we have no missing values, therefore i proceed to the next stage of descriptive stattistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3)Descriptive statistics\n", + "* Descriptive statistics can give you deeper insight into the spread of the data and shape of each attribute/Variable making the data easily readible and creating summaries of our data.\n", + "* This is done using the describe() function on the Pandas DataFrame lists 8 statistical properties of each attribute:-\n", + " * Count-Indicating the number of times the variable was carried out,\n", + " * Mean(or average) which is the sum of all of the data values divided by the number of data values.\n", + " * Standard Deviation which is a measure of the average distance between the values of the data in the set and the mean. If the data values are all similar, then the standard deviation will be low (closer to zero).\n", + " * Minimum Value which the smallest mathematical value in hich the data \n", + " * Maximum Value which is the largest mathematical value in the data \n", + " * 25th Percentile which are the values which are in the 25% range (First quatile)\n", + " * 50th Percentile (Median) which shows the number of values which are either 50% higher or lower\n", + " * 75th Percentile also known as Q3 are the number of values in the 75% range\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean2.0602378.5215200.97141073.6687600.994007-2.4329270.08854515.68323373.6508285.9113611.2352550.500000
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\n", + "
" + ], + "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": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Getting the spread of my data\n", + "Train.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*The count values in all the columns are 3038 which corellates with the number of rows\n", + "* The columns are 12\n", + "* The highest mean is in the FULL_DAYM780201 which is 73.668760\n", + "* The column with the lowest min value is FULL_Charge with -16.0\n", + "* The highest deviation is in the AS_MeanAmphiMoment and lowest in FULL_AURR980107" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Splitting my data into by classfication to find out how balanced the through classification\n", + "* A groupby() operation will be used\n", + "* It involves a combination of splitting the object,then anapplication of a function, and combining the results.\n", + "* It entails categorizing identical data into groups." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.groupby(\"CLASS\").size().plot(kind=\"bar\", color=(\"green\",\"skyblue\")) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* According to the results above, the data got split into two equal groups with values that are balancing out\n", + "* There was therefor no need to manipulate the data to become balanced out\n", + "* There will therefore be no alogarithm balance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A count plot \n", + "* It can be thought of as a histogram across a categorical, instead of quantitative, variable. \n", + "* The basic API and options are identical to those for barplot(), so you can compare counts across nested variables.\n", + "* I intended to see the spread of the data across a variable FULL_Charge" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x = 'FULL_Charge', data = Train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The data in the FULL_Charge variable is normally distributed attaining a bell shaped so it gives me an insight in the data contained in the variable FULL_Charge " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4)Checking how my data correlates with each other \n", + "* Correlation helps us determine how the different attributes relate with one another.\n", + "* It also helps us when deciding which attributes to select especially in the presence of highly correlated attributes in which case one of the two attributes would be sufficient.\n", + "* The corr function is used with Pearson's correlation method since most of the attributes are continous with the exception of NT_EFC195 and CLASS.In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data.\n", + "* Here the intension was to find out if there is a relationship between the variables" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FULL_AcidicMolPerc-0.6129961.0000000.7947960.5414810.1152010.513344-0.143168-0.4315900.4496210.002334-0.213543-0.598816
FULL_AURR980107-0.4909770.7947961.0000000.5482530.3461390.462712-0.169540-0.4260970.4562600.032958-0.403599-0.584111
FULL_DAYM780201-0.4346030.5414810.5482531.0000000.0101180.334778-0.090058-0.4087930.8941910.055915-0.326792-0.554838
FULL_GEOR030101-0.0587250.1152010.3461390.0101181.0000000.319157-0.230417-0.160269-0.0290850.040480-0.151935-0.260470
FULL_OOBM850104-0.2837580.5133440.4627120.3347780.3191571.000000-0.230561-0.3362970.275640-0.4527690.155304-0.453287
NT_EFC1950.088068-0.143168-0.169540-0.090058-0.230417-0.2305611.0000000.178683-0.0368440.1459240.0808980.260702
AS_MeanAmphiMoment0.355477-0.431590-0.426097-0.408793-0.160269-0.3362970.1786831.000000-0.3223780.0255800.1715240.693552
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AS_FUKS010112-0.0905700.0023340.0329580.0559150.040480-0.4527690.1459240.0255800.0455621.000000-0.4452840.033432
CT_RACS8201040.232929-0.213543-0.403599-0.326792-0.1519350.1553040.0808980.171524-0.256060-0.4452841.0000000.267652
CLASS0.534602-0.598816-0.584111-0.554838-0.260470-0.4532870.2607020.693552-0.4371680.0334320.2676521.000000
\n", + "
" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 \\\n", + "FULL_Charge 1.000000 -0.612996 -0.490977 \n", + "FULL_AcidicMolPerc -0.612996 1.000000 0.794796 \n", + "FULL_AURR980107 -0.490977 0.794796 1.000000 \n", + "FULL_DAYM780201 -0.434603 0.541481 0.548253 \n", + "FULL_GEOR030101 -0.058725 0.115201 0.346139 \n", + "FULL_OOBM850104 -0.283758 0.513344 0.462712 \n", + "NT_EFC195 0.088068 -0.143168 -0.169540 \n", + "AS_MeanAmphiMoment 0.355477 -0.431590 -0.426097 \n", + "AS_DAYM780201 -0.365374 0.449621 0.456260 \n", + "AS_FUKS010112 -0.090570 0.002334 0.032958 \n", + "CT_RACS820104 0.232929 -0.213543 -0.403599 \n", + "CLASS 0.534602 -0.598816 -0.584111 \n", + "\n", + " FULL_DAYM780201 FULL_GEOR030101 FULL_OOBM850104 \\\n", + "FULL_Charge -0.434603 -0.058725 -0.283758 \n", + "FULL_AcidicMolPerc 0.541481 0.115201 0.513344 \n", + "FULL_AURR980107 0.548253 0.346139 0.462712 \n", + "FULL_DAYM780201 1.000000 0.010118 0.334778 \n", + "FULL_GEOR030101 0.010118 1.000000 0.319157 \n", + "FULL_OOBM850104 0.334778 0.319157 1.000000 \n", + "NT_EFC195 -0.090058 -0.230417 -0.230561 \n", + "AS_MeanAmphiMoment -0.408793 -0.160269 -0.336297 \n", + "AS_DAYM780201 0.894191 -0.029085 0.275640 \n", + "AS_FUKS010112 0.055915 0.040480 -0.452769 \n", + "CT_RACS820104 -0.326792 -0.151935 0.155304 \n", + "CLASS -0.554838 -0.260470 -0.453287 \n", + "\n", + " NT_EFC195 AS_MeanAmphiMoment AS_DAYM780201 \\\n", + "FULL_Charge 0.088068 0.355477 -0.365374 \n", + "FULL_AcidicMolPerc -0.143168 -0.431590 0.449621 \n", + "FULL_AURR980107 -0.169540 -0.426097 0.456260 \n", + "FULL_DAYM780201 -0.090058 -0.408793 0.894191 \n", + "FULL_GEOR030101 -0.230417 -0.160269 -0.029085 \n", + "FULL_OOBM850104 -0.230561 -0.336297 0.275640 \n", + "NT_EFC195 1.000000 0.178683 -0.036844 \n", + "AS_MeanAmphiMoment 0.178683 1.000000 -0.322378 \n", + "AS_DAYM780201 -0.036844 -0.322378 1.000000 \n", + "AS_FUKS010112 0.145924 0.025580 0.045562 \n", + "CT_RACS820104 0.080898 0.171524 -0.256060 \n", + "CLASS 0.260702 0.693552 -0.437168 \n", + "\n", + " AS_FUKS010112 CT_RACS820104 CLASS \n", + "FULL_Charge -0.090570 0.232929 0.534602 \n", + "FULL_AcidicMolPerc 0.002334 -0.213543 -0.598816 \n", + "FULL_AURR980107 0.032958 -0.403599 -0.584111 \n", + "FULL_DAYM780201 0.055915 -0.326792 -0.554838 \n", + "FULL_GEOR030101 0.040480 -0.151935 -0.260470 \n", + "FULL_OOBM850104 -0.452769 0.155304 -0.453287 \n", + "NT_EFC195 0.145924 0.080898 0.260702 \n", + "AS_MeanAmphiMoment 0.025580 0.171524 0.693552 \n", + "AS_DAYM780201 0.045562 -0.256060 -0.437168 \n", + "AS_FUKS010112 1.000000 -0.445284 0.033432 \n", + "CT_RACS820104 -0.445284 1.000000 0.267652 \n", + "CLASS 0.033432 0.267652 1.000000 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "corr=Train.corr(method='pearson')\n", + "corr\n", + "# The correllation 1 is a positive relationship, -1 a negative relationship and 0 no relationship" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Heatmap is a way of representing the data in a 2-dimensional form. \n", + "* The data values are represented as colors in the graph. \n", + "* It's goal is to provide a colored visual summary of information so I could easily visualise the summary of the relationships between the features" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12,12))\n", + "sns.heatmap(Train.corr(method='pearson'),annot=True,cmap=\"YlGnBu\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* From the graphs above since correlation is informative regarding which choice of features to retain for analysis, those variables/features which highly correlate with the class Attribute are to be consindered in feature selection and those that do not correlate or correlate weakly should be eliminated\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FULL_Charge 0.534602\n", + "FULL_AcidicMolPerc -0.598816\n", + "FULL_AURR980107 -0.584111\n", + "FULL_DAYM780201 -0.554838\n", + "FULL_GEOR030101 -0.260470\n", + "FULL_OOBM850104 -0.453287\n", + "NT_EFC195 0.260702\n", + "AS_MeanAmphiMoment 0.693552\n", + "AS_DAYM780201 -0.437168\n", + "AS_FUKS010112 0.033432\n", + "CT_RACS820104 0.267652\n", + "CLASS 1.000000\n", + "Name: CLASS, dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Showing how each variable correlates with the CLASS variable\n", + "Train.corr(method='pearson')['CLASS']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* For more depth of understanding the data the clustermap method is used \n", + "* THe clustered heatmap of a pandas DataFrame shows hierarchicay of data\n", + "* The .clustermap() method uses a hierarchical clusters to order data by similarity.\n", + "* This reorganizes the data for the rows and columns and displays similar content next to one another " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.clustermap(Train.corr(method='pearson'),annot=True, cmap=\"Paired_r\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The heatmap went further to show me the similarity matrix between variables so i could compare with the output from the heatmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5)Data Visualisation\n", + "* Data visualizations in python can be done via many packages.\n", + "* Using matplot enables one to have a visual figures for the data\n", + "* Every Axes has an x-axis and y-axis for plotting. \n", + "* Contained within the axes are titles, ticks, labels associated with each axis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1)To check for skewness in my dataset\n", + "* Skew refers to a distribution that is assumed Gaussian (normal or bell curve) that is shifted in one direction or another\n", + "* Positive Skew is when the curve shifts to the right \n", + "* Negative skew is when the curve shifts to the left\n", + "* Skewed data can affect the performance of a machine learning algorithm\n", + "* Skewed data should be normalized using different transformation methods which include the square transformation for negatively skewed data and log transfomations." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.skew().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* From the graph above most of the attributes are positively shwed with the exceptio of 4,6,9, and 10 but since the skew is to to a small extent then the data will not be normalised since it will have little effect on our logarithm model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Use of the box plot\n", + "* Boxplots summarize the distribution of each attribute\n", + "* A line for the median (middle value) is drawn while and a box put around the 25th and 75th percentiles.\n", + "* The whiskers give an idea of the spread of the data and dots outside of the whiskers show candidate outlier values \n", + "* The data.plot(kind='box') function is used" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.plot(kind='box', subplots=True, layout=(4,3), sharex=True,sharey=True)\n", + "plt.subplots_adjust(bottom=1, right=2, top=3)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* AS_MeanAmphiMoment * From the above graphs, the class with no outliers is AS_MeanAmphiMoment and has a normal distribution in its variables\n", + "* " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) Scatter plot: \n", + "* This is plot the relationship between two variables as dots in two dimension.\n", + "* This is done so we could spot if there is a structured relationship within the two variables" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Histograms\n", + "plt.figure(figsize=(15,15))\n", + "Train.hist(color='red')\n", + "\n", + "plt.subplots_adjust(bottom=1, right=2, top=3)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* From the above graphs we can tell that we have 2 categorical attributes NT_EFC195 and CLASS but the numbes in each group do not balance out.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6)Preparation of data for Machine Learning\n", + "### Pre-processing of Data is carried out in this section\n", + "* I will need to preapre my data for prepossessing \n", + "* My preffered method for Data transformation is \"The Fit and Multiple Transform\" method form the scikit-learn library.\n", + "* Split the dataset into the input and output variables for machine learning.\n", + "* Apply a pre-processing transform to the input variables.\n", + "* Summarize the data to show the change.\n", + "### a)Rescaling the Data: \n", + "* Rescaling is done since the data has various scales and these need to be into a scale that fits all\n", + "* From numpy, the set_printoptions module is imported to determine the display of number of floating point values other Nump objects and from scikit learn preprocessing module.\n", + "* The MinMaxScaler option is imported to rescale the data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.457 0. 0.348 0.545 0.33 0.483 0. 0.005 0.508 0.415 0.182]\n", + " [0.435 0.116 0.322 0.489 0.276 0.458 1. 0.011 0.421 0.586 0.475]\n", + " [0.467 0.116 0.246 0.523 0.288 0.565 0. 0.011 0.442 0.273 0.666]\n", + " [0.457 0.089 0.275 0.399 0.403 0.647 0. 0.011 0.405 0.153 0.424]\n", + " [0.511 0.183 0.323 0.373 0.342 0.595 0. 0.011 0.5 0.196 0.536]]\n" + ] + } + ], + "source": [ + "\n", + "from numpy import set_printoptions\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "array = Train.values\n", + "# separate array into input and output components\n", + "X= array[:,0:11]\n", + "Y= array[:,11]\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "rescaledX = scaler.fit_transform(X)\n", + "# summarize transformed data\n", + "set_printoptions(precision=3)\n", + "print(rescaledX[0:5,:])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* No rescaling of data was done since predictions with rescaled data had a poor result in my analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1.]]\n" + ] + } + ], + "source": [ + "# Looking at when my data is binarized instead\n", + "from sklearn.preprocessing import Binarizer\n", + "\n", + "array2 = Train.values\n", + "# separate array into input and output components\n", + "X = array2[:,0:11]\n", + "Y = array2[:,11]\n", + "binarizer = Binarizer(threshold=0.0).fit(X)\n", + "binaryX = binarizer.transform(X)\n", + "# summarize transformed data\n", + "set_printoptions(precision=3)\n", + "print(binaryX[0:5,:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Since my data was not boolean the data binarised was not used" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num Features: 6\n", + "Selected Features: [ True False True False True True True False False False True]\n", + "Feature Ranking: [1 5 1 4 1 1 1 2 6 3 1]\n", + "Num Features: 6\n", + "Selected Features: [ True False True False True True True False False False True]\n", + "Feature Ranking: [1 5 1 4 1 1 1 2 6 3 1]\n" + ] + } + ], + "source": [ + "#b) Feature Selection\n", + "#Select an anttribute will will give the most to the prediction variable \n", + "#a)Selection is by The Recursive Feature Elimination (or RFE) \n", + "# This works by recursively removing attributes and building a model on those attributes that remain.\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# feature extraction\n", + "# feature extraction\n", + "model = LogisticRegression()\n", + "rfe = RFE(model, 6)\n", + "fit = rfe.fit(X, Y)\n", + "print(\"Num Features: \", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking: \", fit.ranking_)\n", + "print(\"Num Features: \", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking: \", fit.ranking_)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### c)PCA\n", + "Principal component analysis is a dimensionality reduction technique. It uses linear algebra to transform the dataset into a compressed form." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explained Variance: [0.605 0.242 0.103]\n", + "[[ 1.409e-01 -3.557e-01 -4.743e-03 -4.935e-01 -1.975e-04 -5.101e-02\n", + " 2.877e-03 6.020e-01 -4.950e-01 -8.765e-04 4.186e-03]\n", + " [-8.867e-03 3.090e-02 4.994e-04 3.921e-01 -5.282e-04 -8.492e-03\n", + " 3.437e-03 7.629e-01 5.129e-01 5.018e-03 -2.000e-03]\n", + " [-2.731e-01 8.713e-01 8.049e-03 -1.349e-01 4.214e-04 8.143e-02\n", + " -2.941e-03 2.312e-01 -2.966e-01 -1.359e-03 -5.700e-04]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "pca = PCA(n_components=3)\n", + "fit = pca.fit(X)\n", + "# summarize components\n", + "print(\"Explained Variance: %s\" % fit.explained_variance_ratio_)\n", + "print(fit.components_)\n", + "\n", + "high_dim = Train.drop(columns='CLASS')\n", + "\n", + "scaler.fit(high_dim)\n", + "scaled_data = scaler.transform(high_dim)\n", + "\n", + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=2)\n", + "pca.fit(scaled_data)\n", + "\n", + "x_pca = pca.transform(scaled_data)\n", + "\n", + "plt.figure(figsize=(8,6))\n", + "plt.scatter(x_pca[:,0], x_pca[:,1], c=Train['CLASS'], cmap='Spectral')\n", + "plt.xlabel('First Principal Component')\n", + "plt.ylabel('First Principal Component')\n", + "labels = np.unique(Y)\n", + "plt.legend(labels)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the PCA plot, Two distinct classes are obseved. The Note is in the class distribution and region of overlap between the two classes.\n", + "The overlap problem is a critical problem in which instances appear as valid for more than one class which may be responsible for the presence of noise in datasets and thus affect the performance of the learning algorithm (Gupta and Gupta, 2018)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.078 0.152 0.082 0.085 0.053 0.064 0.037 0.318 0.052 0.033 0.047]\n" + ] + } + ], + "source": [ + "# Feature importance\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "\n", + "array = Train.values\n", + "A = array[:,0:11]\n", + "B = array[:,11]\n", + "# feature extraction\n", + "model = ExtraTreesClassifier()\n", + "model.fit(X, Y)\n", + "print(model.feature_importances_)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BN2N1+li99a4Fu/ZjN16xYNeWtHhc0UtS41zR67j8DUJa+lzRS1LjDHpJapxbNzqtuFUkzT9X9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TG+YUpvewt1Je0/IKWTheu6CWpcQa9JDXOoJekxhn0ktS4QUGfZEOSvUmmk2ydof7MJJ/u6r+QZHVXvjrJ00ke7H7+6/xOX5I0m1k/dZNkGXAT8HbgALAryVRVPdxrdh1wpKrekGQT8DHgXV3dI1V1wTzPW5I00JCPV64HpqtqH0CSHcBGoB/0G4EPd8e3A59Iknmcp9SMU/1xTj8+qiFbNyuA/b3zA13ZjG2q6ijwN8APd3VrknwlyeeT/NQc5ytJOkEL/YWpvwZWVdVTSf4BcGeSN1XVd/qNkmwGNgOsWrVqgackSS8vQ1b0B4Hzeucru7IZ2yRZDrwGeKqqnqmqpwCq6kvAI8CPjQ9QVbdU1WRVTU5MTJz4o5AkHdOQFf0uYG2SNYwCfRPw82NtpoBrgT8HrgLuq6pKMgEcrqrnk7weWAvsm7fZSzrt+P/+nn5mDfqqOppkC3APsAzYXlV7kmwDdlfVFPBJ4FNJpoHDjN4MAN4KbEvyHPAC8L6qOrwQD0SSNLNBe/RVtRPYOVZ2fe/4B8DVM/S7A7hjjnOUJM2B34yVpMYZ9JLUOINekhrnfzwiaUnzUz6zc0UvSY0z6CWpcW7dSNIJWIpbRa7oJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXGDgj7JhiR7k0wn2TpD/ZlJPt3VfyHJ6l7dr3Xle5NcNn9TlyQNMWvQJ1kG3ARcDqwDrkmybqzZdcCRqnoD8J+Bj3V91wGbgDcBG4Df7q4nSTpFhqzo1wPTVbWvqp4FdgAbx9psBH63O74deFuSdOU7quqZqnoUmO6uJ0k6RVJVx2+QXAVsqKpf7M5/Abi4qrb02vxF1+ZAd/4IcDHwYeCBqrq1K/8kcHdV3T42xmZgc3f648DeuT+0Qc4BnjxFYzleG2M63tIebzHGPFXjnV9VEzNVLD8Fg8+qqm4BbjnV4ybZXVWTjrc0x1uMMR1vaY+3GGMuxmMcN2Tr5iBwXu98ZVc2Y5sky4HXAE8N7CtJWkBDgn4XsDbJmiRnMLq5OjXWZgq4tju+CrivRntCU8Cm7lM5a4C1wBfnZ+qSpCFm3bqpqqNJtgD3AMuA7VW1J8k2YHdVTQGfBD6VZBo4zOjNgK7dbcDDwFHg/VX1/AI9lpNxqreLHG/pj+l4S3u8xRhzMR7jS8x6M1aStLT5zVhJapxBL0mNM+glqXEGvSQ1zqAHkrzX8Zb2mI63tMdbjDFbH+8lY/upG0jyeFWtcrylO6bjLe3xFmPM1sfrOy3+BMKpkOShY1UBP+J4p/+Yjre0x1uMMVsfb6iXTdAzepIvA46MlQe43/GWxJiOt7THW4wxWx9vkJdT0P8hcFZVPThekeRzjrckxnS8pT3eYozZ+niDuEcvSY3zUzeS1DiDXpIaZ9BLUuMMeklqnEEvSY37f3yiVYNZNnqQAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate feature importances\n", + "importances = model.feature_importances_\n", + "# Create decision tree classifer object\n", + "clf = ExtraTreesClassifier(random_state=0, n_jobs=-1)\n", + "\n", + "# Train model\n", + "model = clf.fit(X, Y)\n", + "\n", + "# Sort feature importances in descending order\n", + "indices = np.argsort(importances)[::-1]\n", + "\n", + "# Rearrange feature names so they match the sorted feature importances\n", + "names = [Train.CLASS[i] for i in indices]\n", + "\n", + "# Create plot\n", + "plt.figure()\n", + "\n", + "# Create plot title\n", + "plt.title(\"Feature Importance\")\n", + "\n", + "# Add bars\n", + "plt.bar(range(X.shape[1]), importances[indices])\n", + "\n", + "# Add feature names as x-axis labels\n", + "plt.xticks(range(X.shape[1]), names, rotation=90)\n", + "\n", + "# Show plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "## According to feature importance all the features in the data set were useful hence could not be lost and therefore I kept them all therefor no rescaling was carried out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7)Evaluate the Performance of Machine Learning Algorithms with Resampling\n", + "##There is need to know how well the algorithms will perform on unseen data\n", + "##The best way to evaluate the performance of an algorithm is to make predictions for new data to which answers are known\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 91.63533834586465\n" + ] + } + ], + "source": [ + "## Split the Data into Test and Train\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "test_size = 0.35\n", + "seed = 42\n", + "\n", + "X_train,X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "random_state=seed)\n", + "model = LogisticRegression()\n", + "model.fit(X_train, Y_train)\n", + "result = model.score(X_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False True]\n", + "2\n", + "387\n", + "371\n" + ] + } + ], + "source": [ + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 91.54135338345864\n" + ] + } + ], + "source": [ + "# On rescaled Data\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "test_size = 0.35\n", + "seed = 42\n", + "\n", + "rescaledX_train,rescaledX_test, Y_train, Y_test = train_test_split(rescaledX, Y, test_size=test_size,\n", + "random_state=seed)\n", + "model = LogisticRegression()\n", + "model.fit(rescaledX_train, Y_train)\n", + "result = model.score(rescaledX_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False True]\n", + "2\n", + "752\n", + "6\n" + ] + } + ], + "source": [ + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber1_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "## Despite having a similar accuracy of 91.82,the rescaled data was giving imbalanced false a.nd true values and therefore i would not use it further" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: (88.66787208086441, 18.91491119508906)\n" + ] + } + ], + "source": [ + "#Classification accuracy:is the number of correct predictions made as a ratio of all predictions made.\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array = Train.values\n", + "\n", + "num_folds = 20#number of folds to use\n", + "seed = 42#reproducibility\n", + "\n", + "kfold = KFold(n_splits=num_folds, random_state=None)\n", + "model = LogisticRegression()\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n", + "random_state=seed)\n", + "model.fit(X_train, Y_train)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "\n", + "print(f\"Accuracy:\", (results.mean()*100.0, results.std()*100.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False True]\n", + "2\n", + "387\n", + "371\n" + ] + } + ], + "source": [ + "\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber2_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0.0 0.91 0.92 0.91 521\n", + " 1.0 0.92 0.91 0.92 543\n", + "\n", + " accuracy 0.92 1064\n", + " macro avg 0.92 0.92 0.92 1064\n", + "weighted avg 0.92 0.92 0.92 1064\n", + "\n" + ] + } + ], + "source": [ + "#To check out the classification report \n", + "from sklearn.metrics import classification_report\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "rescaledA = scaler.fit_transform(A)\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "rescaledA = scaler.fit_transform(A)\n", + "test_size = 0.35\n", + "seed = 42\n", + "rescaledX_train,rescaledX_test, Y_train, Y_test = train_test_split(rescaledX, Y, test_size=test_size,\n", + "random_state=seed)\n", + "model = LogisticRegression()\n", + "model.fit(rescaledX_train, Y_train)\n", + "predicted = model.predict(rescaledX_test)\n", + "report = classification_report(Y_test, predicted)\n", + "print(report)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE: (-0.26434068269258093, 0.156909136567752)\n" + ] + } + ], + "source": [ + "# we then look at the regression M\n", + "# The Mean Absolute Error(MAE) is the sum of the absolute differences between predictions and actual values.\n", + "# Its aim is to give an idea of how wrong the predictions were. \n", + "from pandas import read_csv\n", + "from sklearn.linear_model import LinearRegression\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "kfold = KFold(n_splits=10, random_state=None)\n", + "model = LinearRegression()\n", + "scoring = 'neg_mean_absolute_error'\n", + "results = cross_val_score(model, rescaledX, Y, cv=kfold, scoring=scoring)\n", + "print(\"MAE:\",(results.mean(), results.std()))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R^2: (-1.2197243963358124, 3.6591731890074377)\n" + ] + } + ], + "source": [ + "#The R2 metric provides an indication of the goodness of fit of a set of predictions to the actual values\n", + "#Cross Validation Regression R^2\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "kfold = KFold(n_splits=10, random_state=None)\n", + "model = LinearRegression()\n", + "scoring = 'r2'\n", + "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", + "print(\"R^2:\",(results.mean(), results.std()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8)Spot-Checking Classiffication Algorithms\n", + "##Algorithms Overview\n", + "##looking at six classication algorithms that will help spot-check on your dataset. Starting with two ##linear machine learning algorithms:\n", + "\n", + "8.1.1)Logistic Regression.\n", + "8.1.2)Linear Discriminant Analysis.\n", + "Then looking at four nonlinear machine learning algorithms:\n", + "\n", + "8.2.1)k-Nearest Neighbors.\n", + "8.2.2)Naive Bayes.\n", + "8.2.3)Classication and Regression Trees.\n", + "8.2.4)Support Vector Machines.\n", + "8.2.5)Random Forest\n", + "8.2.6)Gradient Boosting for classification\n", + "8.2.7)An extra-trees classifier.\n", + "8.2.8)Neural Network-MLPC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## 8.1)Linear Machine Language Alogarithms\n", + "### 8.1.1)Logistic Regression\n", + "Logistic regression assumes a Gaussian distribution for the numeric input variables and can model binary classiffication problems.\n", + "****This is done using the Logisticregression class" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy 91.54051589369463\n", + "MCC: 0.8342865299822478\n" + ] + } + ], + "source": [ + "# Logistic Regression Classification\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import matthews_corrcoef\n", + "from sklearn.metrics import plot_confusion_matrix\n", + "\n", + "#for model set up\n", + "model = LogisticRegression()\n", + "# For cross validation of data\n", + "num_folds = 10\n", + "seed=42\n", + "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n", + "#For accuracy of model\n", + "scoring='accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n", + "print('accuracy',results.mean()*100)\n", + "#Training the model to make a prediction\n", + "model.fit(X,Y)\n", + "kmodel=model.predict(Test)\n", + "#With Mathews correlation cofficient on the model\n", + "kmcc=matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',kmcc)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False True]\n", + "2\n", + "383\n", + "375\n" + ] + } + ], + "source": [ + "#Converting the LogisticRegression predicted model first to data frame then a CSV file\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber3_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# The difference between Accuracy and MCC model is slight but giving the same output pf 383 True values and 375 false values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.1.2)Linear Discriminant Analysis or LDA \n", + "-This a statistical technique for binary and multiclass classiffication. \n", + "-It too assumes a Gaussian distribution for the numerical input variables. \n", + "-You can construct an LDA model using the LinearDiscriminantAnalysis class" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy 91.70574952232066\n", + "MCC: 0.8377162908048379\n" + ] + } + ], + "source": [ + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "#Model set up\n", + "model = LinearDiscriminantAnalysis()\n", + "# For cross validation of data\n", + "num_folds = 10\n", + "seed=42\n", + "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n", + "#For accuracy of model\n", + "scoring='accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n", + "print('accuracy',results.mean()*100)\n", + "#Training the model to make a prediction\n", + "model.fit(X,Y)\n", + "kmodel=model.predict(Test)\n", + "#With Mathews correlation cofficient on the model\n", + "kmcc=matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',kmcc)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False True]\n", + "2\n", + "401\n", + "357\n" + ] + } + ], + "source": [ + "#Converting the LinearDiscriminantAnalysis predicted model first to data frame then a CSV file\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber4_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.2 Nonlinear machine learning algorithms.\n", + "\n", + "### 8.2.1)k-Nearest Neighbors\n", + "##The k-Nearest Neighbors algorithm (or KNN) uses a distance metric to find the k most similar instances ##In the training data for a new instance and takes the mean outcome of the neighbors as the prediction. #You can construct a KNN model using the KNeighborsClassifier class." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy 90.5863066851643\n", + "MCC: 0.8690586462107053\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "#Model set up\n", + "model = KNeighborsClassifier()\n", + "# For cross validation of data\n", + "num_folds = 5\n", + "seed=42\n", + "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n", + "#For accuracy of model\n", + "scoring='accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n", + "print('accuracy',results.mean()*100)\n", + "#Training the model to make a prediction\n", + "model.fit(X,Y)\n", + "kmodel=model.predict(Test)\n", + "#With Mathews correlation cofficient on the model\n", + "kmcc=matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',kmcc)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False True]\n", + "2\n", + "402\n", + "356\n" + ] + } + ], + "source": [ + "#Converting the LinearDiscriminantAnalysis predicted model first to data frame then a CSV file\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber5_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.2.2) Naive Bayes\n", + "-Naive Bayes calculates the probability of each class and the conditional probability of each class given each input value. \n", + "-These probabilities are estimated for new data and multiplied together\n", + "-The Assumption is that they are all independent (a simple or naive assumption).\n", + "-When working with real-valued data, a Gaussian distribution is assumed to easily estimate the probabilities for input variables using the Gaussian Probability Density Function. \n", + "##You can construct a Naive Bayes model using the GaussianNB class4" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy 91.93622980719125\n", + "MCC: 0.8407203694376205\n", + "0.9193622980719125\n" + ] + } + ], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "# For cross validation of data\n", + "num_folds = 10\n", + "seed=42\n", + "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n", + "model = GaussianNB()\n", + "\n", + "#For accuracy of model\n", + "scoring='accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n", + "print('accuracy',results.mean()*100)\n", + "#Training the model to make a prediction\n", + "model.fit(X,Y)\n", + "kmodel=model.predict(Test)\n", + "#With Mathews correlation cofficient on the model\n", + "kmcc=matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',kmcc)\n", + "\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False]\n", + "2\n", + "370\n", + "388\n" + ] + } + ], + "source": [ + "#Converting the Naive Bayes predicted model first to data frame then a CSV file\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber5_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.2.3)Classiffication and Regression Trees\n", + "Classiffication and Regression Trees (CART or just decision trees) construct a binary tree from the training data. Split points are chosen greedily by evaluating each attribute and each value of each attribute in the training data in order to minimize a cost function (like the Gini index). You can construct a CART model using the DecisionTreeClassifier class" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy 89.20379537953795\n", + "MCC: 1.0\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "# For cross validation of data\n", + "num_folds = 10\n", + "seed=42\n", + "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n", + "model = DecisionTreeClassifier()\n", + "#For accuracy of model\n", + "scoring='accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n", + "print('accuracy',results.mean()*100)\n", + "#Training the model to make a prediction\n", + "model.fit(X,Y)\n", + "kmodel=model.predict(Test)\n", + "#With Mathews correlation cofficient on the model\n", + "kmcc=matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',kmcc)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False]\n", + "2\n", + "388\n", + "370\n" + ] + } + ], + "source": [ + "#Converting the DecisionTreeClassifier predicted model first to data frame then a CSV file\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber6_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.2.4)Support Vector Machines\n", + "Support Vector Machines (or SVM) seek a line that best separates two classes. \n", + "The data instances closest to the line that best separates the classes are called support vectors and \n", + "influence where the line is placed. \n", + "SVM supports multiple classes and that of particular importance is the use of dierent kernel functions via the kernel parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy 90.81618030224075\n", + "MCC: 0.8187103751493263\n", + "0.9081618030224075\n" + ] + } + ], + "source": [ + "from sklearn.svm import SVC\n", + "# For cross validation of data\n", + "num_folds = 10\n", + "seed=42\n", + "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n", + "model = SVC()\n", + "\n", + "#For accuracy of model\n", + "scoring='accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n", + "print('accuracy',results.mean()*100)\n", + "#Training the model to make a prediction\n", + "model.fit(X,Y)\n", + "kmodel=model.predict(Test)\n", + "#With Mathews correlation cofficient on the model\n", + "kmcc=matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',kmcc)\n", + "\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False True]\n", + "2\n", + "397\n", + "361\n" + ] + } + ], + "source": [ + "#Converting the SVC predicted model first to data frame then a CSV file\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber7_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.2.5)Random Forest\n", + "Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.\n", + "Random decision forests correct for decision trees’ habit of over fitting to their training set." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy 93.68030224075041\n", + "MCC: 1.0\n", + "0.9417394042035783\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "# For cross validation of data\n", + "num_folds = 10\n", + "seed=42\n", + "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n", + "model = RandomForestClassifier(bootstrap = True,\n", + " max_features = None)\n", + "\n", + "#For accuracy of model\n", + "scoring='accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n", + "print('accuracy',results.mean()*100)\n", + "#Training the model to make a prediction\n", + "model.fit(X,Y)\n", + "kmodel=model.predict(Test)\n", + "#With Mathews correlation cofficient on the model\n", + "kmcc=matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',kmcc)\n", + "\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False]\n", + "2\n", + "383\n", + "375\n" + ] + } + ], + "source": [ + "#Converting the Randomforest predicted model first to data frame then a CSV file\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber8_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.2.6)Gradient Boosting for classification.\n", + "GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. \n", + "In each stage n_classes_ regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function.\n", + "Binary classification is a special case where only a single regression tree is induced." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy 93.15442070522842\n", + "MCC: 0.9092011960769395\n", + "0.9315442070522841\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "# For cross validation of data\n", + "num_folds = 10\n", + "seed=42\n", + "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n", + "model = GradientBoostingClassifier(learning_rate = 1.0,\n", + " max_depth = 1)\n", + "\n", + "#For accuracy of model\n", + "scoring='accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n", + "print('accuracy',results.mean()*100)\n", + "#Training the model to make a prediction\n", + "model.fit(X,Y)\n", + "kmodel=model.predict(Test)\n", + "#With Mathews correlation cofficient on the model\n", + "kmcc=matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',kmcc)\n", + "\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False]\n", + "2\n", + "382\n", + "376\n" + ] + } + ], + "source": [ + "#Converting the GradientBoostingClassifier predicted model first to data frame then a CSV file\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber9_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.2.7)An extra-trees classifier.\n", + "This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset.\n", + "It uses averaging to improve the predictive accuracy and control over-fitting." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy 93.77898645127671\n", + "MCC: 1.0\n", + "0.9364751606739621\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import ExtraTreesClassifier\n", + "# For cross validation of data\n", + "num_folds = 10\n", + "seed=42\n", + "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n", + "model = ExtraTreesClassifier() \n", + "\n", + "#For accuracy of model\n", + "scoring='accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n", + "print('accuracy',results.mean()*100)\n", + "#Training the model to make a prediction\n", + "model.fit(X,Y)\n", + "kmodel=model.predict(Test)\n", + "#With Mathews correlation cofficient on the model\n", + "kmcc=matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',kmcc)\n", + "\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False]\n", + "2\n", + "384\n", + "374\n" + ] + } + ], + "source": [ + "#Converting the ExtratreeClassifier predicted model first to data frame then a CSV file\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber10_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8.2.8)Multi-layer Perceptron classifier.\n", + "This model optimizes the log-loss function using LBFGS or stochastic gradient descent." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy 92.39708181344449\n", + "MCC: 0.8470217206261198\n", + "0.9213359822824387\n" + ] + } + ], + "source": [ + "from sklearn.neural_network import MLPClassifier\n", + "# For cross validation of data\n", + "num_folds = 10\n", + "seed=42\n", + "kfold = KFold(n_splits=num_folds, random_state=seed,shuffle=True)\n", + "model = MLPClassifier() \n", + "\n", + "#For accuracy of model\n", + "scoring='accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold,scoring=scoring)\n", + "print('accuracy',results.mean()*100)\n", + "#Training the model to make a prediction\n", + "model.fit(X,Y)\n", + "kmodel=model.predict(Test)\n", + "#With Mathews correlation cofficient on the model\n", + "kmcc=matthews_corrcoef(model.predict(X),Y)\n", + "print('MCC:',kmcc)\n", + "\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False]\n", + "2\n", + "356\n", + "402\n" + ] + } + ], + "source": [ + "#Converting the MLPClassifier predicted model first to data frame then a CSV file\n", + "out= model.predict(Test.values)\n", + "\n", + "Eva = pd.DataFrame(out) #Converting to data frame\n", + "Eva.columns=[\"CLASS\"] #Naming the column\n", + "Eva.index.name=\"Index\" #Creating a column index\n", + "Eva[\"CLASS\"]=Eva[\"CLASS\"].map({0.0:False,1.0:True}) # Chaninging 0 to \"False\" 1 to \"True\"\n", + "\n", + "Eva.to_csv(\"Amber11_csv\") ## Writing a csv file\n", + "print(Eva['CLASS'].unique())\n", + "print(Eva['CLASS'].nunique())\n", + "\n", + "#printing the numbers of False and True\n", + "print(Eva.groupby('CLASS').size()[0].sum()) #\n", + "print(Eva.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9)Compare Machine Learning Algorithms\n", + "It is important to compare the performance of multiple different machine learning algorithms consistently. The test harness as a template on your own machine learning problems and add more and different algorithms to compare." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('LR', 0.8353580423831856, 0.27188952844394665)\n", + "('LDA', 0.8535044293903076, 0.2571395669719574)\n", + "('KNN', 0.8027933385443807, 0.2521136100771112)\n", + "('CART', 0.7345470731283654, 0.2898967460848411)\n", + "('NB', 0.880815746048289, 0.11642272449162755)\n", + "('SVM', 0.8350280093798853, 0.25836507020625044)\n", + "('MLPC', 0.8291112992878235, 0.2688132800232323)\n", + "('EXTC', 0.8370375195414278, 0.25047049509079944)\n", + "('GBC', 0.7899437641132534, 0.2887124941351131)\n", + "('RDF', 0.8093625151988884, 0.2921369545256496)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Compare Algorithms\n", + "from matplotlib import pyplot\n", + "# prepare models and add them to a list\n", + "models = []\n", + "models.append(('LR', LogisticRegression()))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('SVM', SVC()))\n", + "models.append(('MLPC', MLPClassifier()))\n", + "models.append(('EXTC', ExtraTreesClassifier()))\n", + "models.append(('GBC', GradientBoostingClassifier()))\n", + "models.append(('RDF', RandomForestClassifier()))\n", + "# evaluate each model in turn\n", + "results = []\n", + "names = []\n", + "scoring = 'accuracy'\n", + "for name, model in models:\n", + " kfold = KFold(n_splits=10, random_state=42)\n", + " cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " msg = (name, cv_results.mean(), cv_results.std())\n", + " print(msg)\n", + "\n", + "# boxplot algorithm comparison\n", + "fig = pyplot.figure()\n", + "fig.suptitle('Algorithm Comparison')\n", + "ax = fig.add_subplot(111)\n", + "pyplot.boxplot(results)\n", + "ax.set_xticklabels(names)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "# From the alogarithms above the best was the Gausian Naive Bayes with the highest mean value pf 0.88 followed by LDA with 0.85\n", + "# The least fit alogarith was DecisionTreeClassifier(0.73)" + ] + } + ], + "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/Jupiter Marina.ipynb b/Jupiter Marina.ipynb new file mode 100644 index 0000000..98ca8ae --- /dev/null +++ b/Jupiter Marina.ipynb @@ -0,0 +1 @@ +{"cells":[{"metadata":{},"cell_type":"markdown","source":"KABAHITA JUPITER MARINA\n2019/HD07/24828U\n"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport sklearn\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Importing data"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"# Importing data\ntest_data = pd.read_csv(\"/kaggle/input/ace-class-assignment/Test.csv\", header=0, sep=\",\")\namp_train = pd.read_csv(\"/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\", header=0, sep=\",\")\namp_train.head()\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_data.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## General data attributes and descriptive statistics"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Describing general data attributes\ntrain_dim=amp_train.shape #gets the dimensions of the tranining dataset\ntest_dim= test_data.shape #gets the dimensions of the tesing dataset\nprint(train_dim)\nprint(test_dim)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"We can see that the test data has one attribute less than the train data set"},{"metadata":{"trusted":true},"cell_type":"code","source":"atr_types=amp_train.dtypes #gets the data type for each column in training dataset\n\natr_types\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"amp_train.isnull().sum() # gets the sum of the missing values/observations in each column","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":" All attributes have complete observations"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Descriptive statistics \nstats=amp_train.describe()\nstats","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"From the summary statistics, its visible that some of the attributes like FULL_CHARGE, FULL_AcidicMolPerc and\tFULL_OOBM850104 have outlier values basing on the values of the 75% quartile and maximum values "},{"metadata":{},"cell_type":"markdown","source":"# Class distributions\n\nSometimes, observations from the different classes may not be equal, these may need special handling, so its important to note the class distributions, as if they are uneven, they may bias our model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# CLass distibutions \ncdists= amp_train.groupby('CLASS').size() # groups the observation by the column of class, and counts all the observations including null values\nprint(cdists)\n\n#plots to visualize the class distribution\namp_train.groupby('CLASS').size().plot(kind=\"pie\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The observations from each class seem8 to be equal"},{"metadata":{},"cell_type":"markdown","source":"# Correlation between the different attributes\nBasically, we need to examine our attributes for correlation as highly correlated attributes present the same data to the algorithm. Removing one may speed learning of the algorithm (less dimensions more speed) and also prevent overfitting of the algorithm"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Correlation between attributes \ncorrelations = amp_train.corr(method=\"pearson\") # calculates pairwaise correlation for the attributes \ncorrelations\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"FULL_AURR980107 and FULL_AcidicMolPerc have a correlation coefficent of 0.79, this might mean these two attributes may be some what correlated. Same goes for AS_DAYM780201 and FULL_DAYM780201 (correlation coefficient is 0.894191)"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Plots to visualize the correlations\nfrom matplotlib import rcParams\nsns.heatmap(correlations, annot=True)\nrcParams['figure.figsize'] = (11.7,8.27)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Data skewness\n\nMost machine learning algorithms assume that data from the attributes is normally distributed, so identifying attributes with data that has a left or right skew, can be important as this can be easily corrected"},{"metadata":{"trusted":true},"cell_type":"code","source":"sk = amp_train.skew() # calculates the skew for each attribute\nsk","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Negative values like those of FULL_DAYM780201, FULL_OOBM850104,AS_DAYM780201, AS_FUKS010112, indicate that these attributes have values skewed more to the left\nValues closer to zero are indicative of a less or no skew, while positive values would mean that that particular attribute is skewed more to the right"},{"metadata":{},"cell_type":"markdown","source":" # Visualising data distributions with plots"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Plot one, histogram to show the distributions\namp_train.hist(layout=(4,3), figsize=(16,16))\nplt.subplots_adjust(wspace=0.4, hspace=0.5)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Plot two density plots can show the distributions better\namp_train.plot(kind=\"density\",subplots=True,layout=(4,3),sharex=False,sharey=False, figsize=(16,16))\n#amp_train.plot(kind=\"density\",subplots=False,layout=(4,3),sharex=True,sharey=True)\nplt.show()\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Distributions for AS_MeanAmphiMoment,FULL_AcidicMolPerc and NT_EFC195 dont look so good"},{"metadata":{},"cell_type":"markdown","source":"# Data transformations\nUsing squaring to transform the attributes with negative values into postives"},{"metadata":{"trusted":true},"cell_type":"code","source":"\nneg1=amp_train.iloc[:,0]\ntneg1=neg1**2\ntneg1.plot(kind=\"density\")\n\namp_train.iloc[:,0]=tneg1\n\nneg2=amp_train.iloc[:,5]\ntneg2=neg2**2\ntneg2.plot(kind=\"density\")\namp_train.iloc[:,5]=tneg2\n\n## test\nneg3=test_data.iloc[:,0]\ntneg3=neg3**2\ntneg3.plot(kind=\"density\")\ntest_data.iloc[:,0]=tneg3\n\n\n\nneg4=test_data.iloc[:,5]\ntneg4=neg4**2\ntneg4.plot(kind=\"density\")\ntest_data.iloc[:,5]=tneg4","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Feature selection\n\n\n"},{"metadata":{"trusted":true},"cell_type":"code","source":" #test one selecting out features with low variance\nfrom sklearn.feature_selection import VarianceThreshold\nsel = VarianceThreshold(threshold=(.8 * (1 - .8)))\nk=sel.fit_transform(amp_train)\nnames=amp_train.columns[sel.get_support(indices=True)]\nprint(k.shape)\namp_train2=pd.DataFrame(k,columns=names)\namp_train2.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"After selecting out features with low variance, only 8 attribute remained "},{"metadata":{"trusted":true},"cell_type":"code","source":"#test two using sklearn with a chi square test\n\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import chi2\n\ntest_array = amp_train2.values\n\ntest_atr = test_array[:,0:7]\nclass_atr = test_array[:,7]\n\n\ntest = SelectKBest(score_func=chi2, k=5)\nfit = test.fit(test_atr, class_atr)\n\n\nprint(fit.scores_)\nfeatures = fit.transform(test_atr)\nnames2=amp_train2.columns[fit.get_support(indices=True)]\namp_train3=pd.DataFrame(features,columns=names2)\namp_train3.iloc[:5,]\nprint(names2)\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Test three Recursive feature elimination\n\nfrom sklearn.feature_selection import RFE\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\n\nmodel1 = LogisticRegression()\nmodel2=RandomForestClassifier()\n\nrfe = RFE(model1, 5)\nfit2 = rfe.fit(test_atr, class_atr)\nfeatures2 = fit2.transform(test_atr)\n\nrfe2= RFE(model2, 5)\nfit3 = rfe2.fit(test_atr, class_atr)\n\n\n\n\nnames3= set(amp_train2.columns[fit2.get_support(indices=True)])\n\nnames4= set(amp_train2.columns[fit3.get_support(indices=True)])\n\nnames2=set(names2)\n\ncomn=names2&names3&names4 ### identifies features common to all the feature selection methods used\ncomn\n\nimport collections\n\ncomn=collections.deque(comn)\ncomn.appendleft(\"FULL_Charge\")\ncomn\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Getting the indices of the chosen attributes"},{"metadata":{"trusted":true},"cell_type":"code","source":"\nindice=[]\n\nfor index,name in enumerate(amp_train.columns):\n for sub_name in comn:\n if name==sub_name:\n indice.append(index)\nindice\n ","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Subsetting the data for the chosen attributes "},{"metadata":{"trusted":true},"cell_type":"code","source":"amp_train = pd.read_csv(\"/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\", header=0, sep=\",\")\n\n\ntest_amp =amp_train.iloc[:,[0,1,3,5,7,11]]\n\ntest_amp.head()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import confusion_matrix\n\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom sklearn.neighbors import KNeighborsClassifier\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Logistic regression using cross validation"},{"metadata":{"trusted":true},"cell_type":"code","source":"\nX=test_amp.iloc[:,0:5]\nY=test_amp.iloc[:,5]\nfolds=10\nkfold = KFold(n_splits=folds, random_state=7, shuffle=True,)\nmodel = LogisticRegression()\nresults = cross_val_score(model, X, Y, cv=kfold)\n\nprint((\"Accuracy: %.3f%% (%.3f%%)\") % (results.mean()*100.0, results.std()*100.0))\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model one\n ## Logistic regression with a test train split using 5 features\n "},{"metadata":{"trusted":true},"cell_type":"code","source":"test_amp =amp_train.values[:,[0,1,3,5,7,11]]\nfrom sklearn.metrics import matthews_corrcoef\nX=test_amp[:,0:5]\nY=test_amp[:,5]\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel1 = LogisticRegression()\nmodel1.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model1.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult1 = model1.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result1*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model1.predict(X_test),Y_test))\nmcc1=matthews_corrcoef(model1.predict(X_test),Y_test)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":" # Model two\n## Gausian/Naive Bayes using five features "},{"metadata":{"trusted":true},"cell_type":"code","source":"test_amp =amp_train.values[:,[0,1,3,5,7,11]]\nfrom sklearn.metrics import matthews_corrcoef\nX=test_amp[:,0:5]\nY=test_amp[:,5]\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel2 = GaussianNB()\nmodel2.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model2.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult2 = model2.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result2*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model2.predict(X_test),Y_test))\nmcc2=matthews_corrcoef(model2.predict(X_test),Y_test)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model three\n## Linear Discriminant Analysis using five features "},{"metadata":{"trusted":true},"cell_type":"code","source":"test_amp =amp_train.values[:,[0,1,3,5,7,11]]\nfrom sklearn.metrics import matthews_corrcoef\nX=test_amp[:,0:5]\nY=test_amp[:,5]\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel3=LinearDiscriminantAnalysis()\nmodel3.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model3.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult3 = model3.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result3*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model3.predict(X_test),Y_test))\n\nmcc3=matthews_corrcoef(model3.predict(X_test),Y_test)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model four \n ## Support Vector Machines with five features"},{"metadata":{"trusted":true},"cell_type":"code","source":"test_amp =amp_train.values[:,[0,1,3,5,7,11]]\nfrom sklearn.metrics import matthews_corrcoef\nX=test_amp[:,0:5]\nY=test_amp[:,5]\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel4=SVC()\nmodel4.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model4.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult4 = model4.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result4*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model4.predict(X_test),Y_test))\n\n\nmcc4=matthews_corrcoef(model4.predict(X_test),Y_test)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# model 5\n ## K-Nearest neighbours with five features\n "},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier\nmodel5 = KNeighborsClassifier(n_neighbors=5)\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel5.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model5.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult5 = model5.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result5*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model5.predict(X_test),Y_test))\n\nmcc5=matthews_corrcoef(model5.predict(X_test),Y_test)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model six\n ## Centroid clasifier "},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.neighbors import NearestCentroid\nmodel6 = NearestCentroid()\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel6.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model6.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult6 = model6.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result6*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model6.predict(X_test),Y_test))\n\nmcc6=matthews_corrcoef(model6.predict(X_test),Y_test)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model seven\n\n## Decision trees with five features \n"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn import tree\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel7=tree.DecisionTreeClassifier()\nmodel7.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model7.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult7 = model7.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result7*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model7.predict(X_test),Y_test))\nmcc7=matthews_corrcoef(model7.predict(X_test),Y_test)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model eight \n## Randomised tree"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nmodel8 =RandomForestClassifier()\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel8.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model8.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult8 = model8.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result8*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model8.predict(X_test),Y_test))\n\n\nmcc8=matthews_corrcoef(model8.predict(X_test),Y_test)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model nine \n ## Gradient tree boosting\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.ensemble import GradientBoostingClassifier\nmodel9 =GradientBoostingClassifier()\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel9.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model9.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult9 = model9.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result9*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model9.predict(X_test),Y_test))\n\nmcc9=matthews_corrcoef(model9.predict(X_test),Y_test)\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model ten\n## Stochastic Gradient Descent "},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.linear_model import SGDClassifier\nmodel10 = SGDClassifier()\n\n#Training data spliting \n\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed, shuffle=True)\n\n#model training \n\nmodel10.fit(X_train, Y_train)\n\n# predicting\n\npredicted = model10.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult10 = model10.score(X_test, Y_test)\n\n# Results accuracy \n\nprint(\"Accuracy: \", (result10*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model10.predict(X_test),Y_test))\n\n\nmcc10=matthews_corrcoef(model10.predict(X_test),Y_test)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"compare=[result1,result2,result3,result4,result5,result6,result7,result8,result9,result10]\n\ncompare=pd.DataFrame(compare).T\n\ncompare=compare.rename(columns={0:\"LGR\", 1:\"GNB\", 2:\"LDN\", 3:\"SVM\", 4:\"KNN\", 5:\"CCL\", 6:\"DCS\", 7:\"RDT\", 8:\"GBT\", 9:\"SGD\"})\n\ncompareMCC=[mcc1,mcc2,mcc3,mcc4,mcc5,mcc6,mcc7,mcc8,mcc9,mcc10]\ncompareMCC=pd.DataFrame(compareMCC).T\ncompareMCC=compareMCC.rename(columns={0:\"LGR\", 1:\"GNB\", 2:\"LDN\", 3:\"SVM\", 4:\"KNN\", 5:\"CCL\", 6:\"DCS\", 7:\"RDT\", 8:\"GBT\", 9:\"SGD\"})\n\ncompare=compare.append(compareMCC)\n#compare.plot(kind=\"box\")\n#plt.plot(\"Mean of MCC and Accuracy\",\"Different models\", data=compare)\n\n\ncompare=compare.values\n\nfig = plt.figure()\nax = fig.add_subplot(111)\nax.boxplot(compare)\nplt.xticks([1, 2, 3,4,5,6,7,8,9,10], [\"LGR\",\"GNB\",\"LDN\",\"SVM\",\"KNN\",\"CCL\",\"DCS\",\"RDT\",\"GBT\",\"SGD\"])\n\n\n\nax.set_title('A comparison of the different Accuracies and MCC from the different Models')\nax.set_xlabel('The different Models')\nax.set_ylabel('Mean of Accuracy and MCC')\nplt.show()\n\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Predicting from the test data"},{"metadata":{"trusted":true},"cell_type":"code","source":"\ntest_data = pd.read_csv(\"/kaggle/input/ace-class-assignment/Test.csv\", header=0, sep=\",\")\ntest_data2=test_data.values[:,[0,1,3,5,7]]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Predictions from logistic Regression"},{"metadata":{"trusted":true},"cell_type":"code","source":"model1.fit(X,Y) ## retrains the algorithm again on the entire train data set\nkj_results=model1.predict(test_data2)\n\ndf = pd.DataFrame(kj_results)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\ndf.to_csv(\"kjLGR.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score from the leader board using this prediction is 0.82503"},{"metadata":{},"cell_type":"markdown","source":"## Predicting using Gausssian"},{"metadata":{"trusted":true},"cell_type":"code","source":"model2.fit(X,Y) ## retrains the algorithm again on the entire train data set\nkj_results=model2.predict(test_data2)\n\ndf = pd.DataFrame(kj_results)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\ndf.to_csv(\"kjGNB.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score on leader board using this prediction was 0.84591"},{"metadata":{},"cell_type":"markdown","source":"# Predicting using Support Vector Machines "},{"metadata":{"trusted":true},"cell_type":"code","source":"model4.fit(X,Y) ## retrains the algorithm again on the entire train data set\nkj_results=model4.predict(test_data2)\n\ndf = pd.DataFrame(kj_results)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\ndf.to_csv(\"kjSVM.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score on leader board using this prediction was 0.82136"},{"metadata":{},"cell_type":"markdown","source":"## Predicting using the randomised tree classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"model8.fit(X,Y) ## retrains the algorithm again on the entire train data set\nkj_results=model8.predict(test_data2)\n\ndf = pd.DataFrame(kj_results)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\ndf.to_csv(\"kjRDT.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score on leader board using this prediction was 0.80561"},{"metadata":{},"cell_type":"markdown","source":"## Predicting using the Gradient tree boosting classifier "},{"metadata":{"trusted":true},"cell_type":"code","source":"model9.fit(X,Y) ## retrains the algorithm again on the entire train data set\nkj_results=model9.predict(test_data2)\n\ndf = pd.DataFrame(kj_results)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\ndf.to_csv(\"kjGBT.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score on leader board using this prediction was 0.79678"},{"metadata":{},"cell_type":"markdown","source":"Random forest and Gradient boosting classifiers had the highest accuracies during training but gave the least scores on the leader board\n\nGausian gave the highest score"},{"metadata":{},"cell_type":"markdown","source":"# More with the gausian "},{"metadata":{},"cell_type":"markdown","source":"## Using gausian with more features"},{"metadata":{},"cell_type":"markdown","source":"## Using gausian with eight features "},{"metadata":{"trusted":true},"cell_type":"code","source":"amp_train = pd.read_csv(\"/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\", header=0, sep=\",\")\ntest_data = pd.read_csv(\"/kaggle/input/ace-class-assignment/Test.csv\", header=0, sep=\",\")\n\n#selecting out features with low variance\nfrom sklearn.feature_selection import VarianceThreshold\nsel = VarianceThreshold(threshold=(.8 * (1 - .8)))\nk=sel.fit_transform(amp_train)\nnames=amp_train.columns[sel.get_support(indices=True)]\nprint(k.shape)\namp_train2=pd.DataFrame(k,columns=names)\n#print(amp_train2)\n\nn=sel.fit_transform(test_data)\nnames2=test_data.columns[sel.get_support(indices=True)]\ntest_data=pd.DataFrame(n,columns=names2)\ntest_data2=test_data.values\n\n#print(test_data)\n\n### data \ntest_amp =amp_train2.values\nX=test_amp[:,0:7]\nY=test_amp[:,7]\n\n\n\n\n#model\nmodel=GaussianNB()\nfrom sklearn.metrics import matthews_corrcoef\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\nrandom_state=seed, shuffle=\"True\")\nmodel.fit(X_train, Y_train)\npredicted = model.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult = model.score(X_test, Y_test)\nprint(\"Accuracy: \", (result*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model.predict(X_test),Y_test)) \nmodel.fit(X,Y)\nkj_resultsg8=model.predict(test_data)\n\n\n#### results \n\ndf = pd.DataFrame(kj_resultsg8)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\n\ndf.to_csv(\"kjGNB8.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"MCC value for the Gaussian with 8 features and that with 5 features are almost the same, but the leaderboard score using the gausian with 8 features was 0.86379, which is an improvement over the 0.84 score from the five features"},{"metadata":{},"cell_type":"markdown","source":"## Using gaussian with all attributes "},{"metadata":{"trusted":true},"cell_type":"code","source":"amp_train = pd.read_csv(\"/kaggle/input/ace-class-assignment/AMP_TrainSet.csv\", header=0, sep=\",\")\ntest_data = pd.read_csv(\"/kaggle/input/ace-class-assignment/Test.csv\", header=0, sep=\",\")\n\n\n### data \ntest_amp =amp_train.values\nX=test_amp[:,0:11]\nY=test_amp[:,11]\n\n\n#model\nmodel=GaussianNB()\nfrom sklearn.metrics import matthews_corrcoef\ntest_size = 0.4\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\nrandom_state=seed)\nmodel.fit(X_train, Y_train)\npredicted = model.predict(X_test)\nmatrix = confusion_matrix(Y_test, predicted)\nresult = model.score(X_test, Y_test)\nprint(\"Accuracy: \", (result*100.0))\nprint(matrix)\nprint('MCC: ', matthews_corrcoef(model.predict(X_test),Y_test)) \nmodel.fit(X,Y)\n\nkj_resultsg12=model.predict(test_data)\n\n\n#### results \n\ndf = pd.DataFrame(kj_resultsg12)\ndf.columns = ['CLASS']\ndf.index.name = 'Index'\ndf['CLASS']=df['CLASS'].map({0.0:False,1.0:True})\n\ndf.to_csv(\"kjGNB11.csv\")\ndf[\"CLASS\"].unique()\nprint(df.groupby(\"CLASS\").size()[0].sum())\nprint(df.groupby(\"CLASS\").size()[1].sum())\ndf['CLASS'].value_counts()\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Score from the leaderboard using all features was 0.99559"},{"metadata":{},"cell_type":"markdown","source":"References\n\n\n1. https://realpython.com/logistic-regression-python/\n2. https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_extra_trees.htm\n3. https://stackabuse.com/gradient-boosting-classifiers-in-python-with-scikit-learn/\n4. https://machinelearningmastery.com/stochastic-gradient-boosting-xgboost-scikit-learn-python/\n5. https://machinelearningmastery.com/stochastic-gradient-boosting-xgboost-scikit-learn-python/\n6. https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/\n7. https://machinelearningmastery.com/master-machine-learning-algorithms/\n8. https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/\n9. https://github.com/search?q=data-analysis-and-visualization-with-python&type=Repositories\n10. https://stackabuse.com/applying-filter-methods-in-python-for-feature-selection/\n11. https://towardsdatascience.com/decision-tree-in-machine-learning-e380942a4c96\n12. https://machinelearningmastery.com/master-machine-learning-algorithms/\n13. https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/\n\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/Reading Material/Basics for Linear Algebra for Machine Learning - Discover the Mathematical Language of Data in Python ( PDFDrive.com ).pdf b/Reading Material/Basics for Linear Algebra for Machine Learning - Discover the Mathematical Language of Data in Python ( PDFDrive.com ).pdf new file mode 100644 index 0000000..19f6bfe Binary files /dev/null and b/Reading Material/Basics for Linear Algebra for Machine Learning - Discover the Mathematical Language of Data in Python ( PDFDrive.com ).pdf differ diff --git a/Reading Material/First Contact with Deep Learning_ Practical Introduction with Keras ( PDFDrive.com ).pdf b/Reading Material/First Contact with Deep Learning_ Practical Introduction with Keras ( PDFDrive.com ).pdf new file mode 100644 index 0000000..5483992 Binary files /dev/null and b/Reading Material/First Contact with Deep Learning_ Practical Introduction with Keras ( PDFDrive.com ).pdf differ diff --git "a/Reading Material/Hands\342\200\223On Machine Learning with Scikit\342\200\223Learn and TensorFlow 2e ( PDFDrive.com ).pdf" "b/Reading Material/Hands\342\200\223On Machine Learning with Scikit\342\200\223Learn and TensorFlow 2e ( PDFDrive.com ).pdf" new file mode 100644 index 0000000..dc19a58 Binary files /dev/null and "b/Reading Material/Hands\342\200\223On Machine Learning with Scikit\342\200\223Learn and TensorFlow 2e ( PDFDrive.com ).pdf" differ diff --git a/Reading Material/Machine Learning Mastery With Python ( PDFDrive.com ).pdf b/Reading Material/Machine Learning Mastery With Python ( PDFDrive.com ).pdf new file mode 100644 index 0000000..a1492de Binary files /dev/null and b/Reading Material/Machine Learning Mastery With Python ( PDFDrive.com ).pdf differ diff --git a/Reading Material/Pandas Cookbook/Pandas Cookbook ( PDFDrive.com ).pdf b/Reading Material/Pandas Cookbook/Pandas Cookbook ( PDFDrive.com ).pdf new file mode 100755 index 0000000..df41be8 Binary files /dev/null and b/Reading Material/Pandas Cookbook/Pandas Cookbook ( PDFDrive.com ).pdf differ diff --git a/Reading Material/Pandas Cookbook/Pandas-Cookbook b/Reading Material/Pandas Cookbook/Pandas-Cookbook new file mode 160000 index 0000000..b45d95c --- /dev/null +++ b/Reading Material/Pandas Cookbook/Pandas-Cookbook @@ -0,0 +1 @@ +Subproject commit b45d95ccef24b020fdf28ecb5a07e3348bee62f1 diff --git a/Thur 20 Feb/.ipynb_checkpoints/.Rhistory b/Thur 20 Feb/.ipynb_checkpoints/.Rhistory new file mode 100644 index 0000000..e69de29 diff --git a/Thur 20 Feb/ACE_ClassML Pipeline.ipynb b/Thur 20 Feb/ACE_ClassML Pipeline_edit.ipynb similarity index 98% rename from Thur 20 Feb/ACE_ClassML Pipeline.ipynb rename to Thur 20 Feb/ACE_ClassML Pipeline_edit.ipynb index e55fe6d..1752842 100644 --- a/Thur 20 Feb/ACE_ClassML Pipeline.ipynb +++ b/Thur 20 Feb/ACE_ClassML Pipeline_edit.ipynb @@ -24,14 +24,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#import the necessary libraries\n", "import pandas as pd\n", "import numpy as np\n", - "import matplotlib.pyplot as plt\n", + "import matplotlib.pyplot as plt # like this\n", + "# from matplotlib import pyplot, plt.plot.scatter(x,y)\n", "import seaborn as sns \n", "\n", "#let's remove the annoying warnings from our cells.\n", @@ -41,7 +42,13 @@ }, { "cell_type": "code", +<<<<<<< HEAD:Thur 20 Feb/ACE_ClassML Pipeline_edit.ipynb "execution_count": 3, +<<<<<<< HEAD +======= +======= + "execution_count": 2, +>>>>>>> daa8e17d37c3ff3550a78fcd977a06dc0f3cca7d:Thur 20 Feb/ACE_ClassML Pipeline.ipynb "metadata": {}, "outputs": [ { @@ -58,25 +65,52 @@ }, { "cell_type": "code", +<<<<<<< HEAD:Thur 20 Feb/ACE_ClassML Pipeline_edit.ipynb "execution_count": 4, +>>>>>>> 44f80edc61a6b27d34519215b7d10c3ac1a6fee6 +======= + "execution_count": 3, +>>>>>>> daa8e17d37c3ff3550a78fcd977a06dc0f3cca7d:Thur 20 Feb/ACE_ClassML Pipeline.ipynb "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ +<<<<<<< HEAD + "'ACE_ClassML Pipeline.ipynb' diabetes.csv housing.csv untitled\n", + "total 1036\n", + "-rw-r--r-- 1 namaganda namaganda 984181 Feb 28 09:01 'ACE_ClassML Pipeline.ipynb'\n", + "-rwxr-xr-x 1 namaganda namaganda 23873 Feb 20 14:32 diabetes.csv\n", + "-rwxr-xr-x 1 namaganda namaganda 49082 Feb 20 14:32 housing.csv\n", + "-rw-r--r-- 1 namaganda namaganda 0 Feb 20 14:33 untitled\n", + "total 1040\n", + "-rw-r--r-- 1 namaganda namaganda 984181 Feb 28 09:01 'ACE_ClassML Pipeline.ipynb'\n", + "-rwxr-xr-x 1 namaganda namaganda 23873 Feb 20 14:32 diabetes.csv\n", + "-rwxr-xr-x 1 namaganda namaganda 49082 Feb 20 14:32 housing.csv\n", + "drwxr-xr-x 2 namaganda namaganda 4096 Mar 6 09:19 meeee\n", + "-rw-r--r-- 1 namaganda namaganda 0 Feb 20 14:33 untitled\n" +======= "ACE_ClassML Pipeline.ipynb \u001b[31mhousing.csv\u001b[m\u001b[m\r\n", "\u001b[31mdiabetes.csv\u001b[m\u001b[m\r\n" +>>>>>>> 44f80edc61a6b27d34519215b7d10c3ac1a6fee6 ] } ], "source": [ "#lets' check which files we have in our folder\n", - "!ls" + "!ls\n", + "!ls -l\n", + "!mkdir meeee\n", + "!ls -l" ] }, { "cell_type": "code", +<<<<<<< HEAD:Thur 20 Feb/ACE_ClassML Pipeline_edit.ipynb +<<<<<<< HEAD + "execution_count": 4, +======= "execution_count": null, "metadata": {}, "outputs": [], @@ -87,6 +121,10 @@ { "cell_type": "code", "execution_count": 6, +>>>>>>> 44f80edc61a6b27d34519215b7d10c3ac1a6fee6 +======= + "execution_count": 7, +>>>>>>> daa8e17d37c3ff3550a78fcd977a06dc0f3cca7d:Thur 20 Feb/ACE_ClassML Pipeline.ipynb "metadata": {}, "outputs": [ { @@ -202,7 +240,15 @@ "4 2.288 33 1 " ] }, +<<<<<<< HEAD:Thur 20 Feb/ACE_ClassML Pipeline_edit.ipynb +<<<<<<< HEAD + "execution_count": 4, +======= "execution_count": 6, +>>>>>>> 44f80edc61a6b27d34519215b7d10c3ac1a6fee6 +======= + "execution_count": 7, +>>>>>>> daa8e17d37c3ff3550a78fcd977a06dc0f3cca7d:Thur 20 Feb/ACE_ClassML Pipeline.ipynb "metadata": {}, "output_type": "execute_result" } @@ -210,7 +256,126 @@ "source": [ "#let's read in the data\n", "data = pd.read_csv('diabetes.csv')\n", - "data.head(5)" + "data.head(5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n", + "[[0, 1, 4, 9, 16, 25, 36, 49, 64, 81], [0, 1, 8, 27, 64, 125, 216, 343, 512, 729]]\n", + "[[ 0 1 2]\n", + " [ 3 4 5]\n", + " [ 6 7 8]\n", + " [ 9 10 11]]\n", + " 0 1 2\n", + "0 0 1 2\n", + "1 3 4 5\n", + "2 6 7 8\n", + "3 9 10 11\n", + "[[ 0 1 2]\n", + " [ 3 4 5]\n", + " [ 6 7 8]\n", + " [ 9 10 11]]\n", + " 0 1 2\n", + "0 0 1 2\n", + "1 3 4 5\n", + "2 6 7 8\n", + "3 9 10 11\n", + "[[ 0 1 2]\n", + " [ 3 4 5]\n", + " [ 6 7 8]\n", + " [ 9 10 11]]\n", + " aria youu meh\n", + "0 0 1 2\n", + "1 3 4 5\n", + "2 6 7 8\n", + "3 9 10 11\n", + " 0 1 2\n", + "xx 0 1 2\n", + "yy 3 4 5\n", + "zz 6 7 8\n", + "3 9 10 11\n" + ] + } + ], + "source": [ + "arr = [x**2 for x in range(10)]\n", + "print(arr)\n", + "arr1 = [x**3 for x in range(10)]\n", + "\n", + "#print(arr, arr1)\n", + "\n", + "list = [arr, arr1]\n", + "\n", + "#print(arr1)\n", + "\n", + "print(list)\n", + "\n", + "arr3 = [x for x in range(12)]\n", + "\n", + "arr3 = np.array(arr3)\n", + "\n", + "arr3 = arr3.reshape(4,3)\n", + "print(arr3)\n", + "df = pd.DataFrame(arr3)\n", + "print(df)\n", + "\n", + "df.head()\n", + "\n", + "dff = arr3.reshape((4,3), order = 'C')\n", + "dfff = arr3.reshape((4,3), order = 'F')\n", + "# there are two types of reshaping, C is default and fills rows, F fills columns\n", + "print(dff)\n", + "print(df)\n", + "print(dff)\n", + "df\n", + "\n", + "dffff = df.rename(columns={0: \"aria\", 1: \"youu\", 2: \"meh\"})\n", + "print(dffff)\n", + "\n", + "\n", + "dffff.index\n", + "\n", + "type(dffff)\n", + "dffff.index\n", + "\n", + "dffff = df.rename(index={0: \"xx\", 1: \"yy\", 2: \"zz\"})\n", + "\n", + "print(dffff)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" + ] + } + ], + "source": [ + "for x in range(10):\n", + " print(x)" ] }, { @@ -228,7 +393,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD:Thur 20 Feb/ACE_ClassML Pipeline_edit.ipynb +<<<<<<< HEAD + "execution_count": 5, +======= "execution_count": 7, +>>>>>>> 44f80edc61a6b27d34519215b7d10c3ac1a6fee6 +======= + "execution_count": 8, +>>>>>>> daa8e17d37c3ff3550a78fcd977a06dc0f3cca7d:Thur 20 Feb/ACE_ClassML Pipeline.ipynb "metadata": {}, "outputs": [ { @@ -237,7 +410,15 @@ "(768, 9)" ] }, +<<<<<<< HEAD:Thur 20 Feb/ACE_ClassML Pipeline_edit.ipynb +<<<<<<< HEAD + "execution_count": 5, +======= "execution_count": 7, +>>>>>>> 44f80edc61a6b27d34519215b7d10c3ac1a6fee6 +======= + "execution_count": 8, +>>>>>>> daa8e17d37c3ff3550a78fcd977a06dc0f3cca7d:Thur 20 Feb/ACE_ClassML Pipeline.ipynb "metadata": {}, "output_type": "execute_result" } @@ -252,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -263,7 +444,7 @@ " dtype='object')" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -286,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,7 +485,7 @@ "dtype: object" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -314,6 +495,35 @@ "data.dtypes" ] }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n", + " 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n", + " dtype='object')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -755,6 +965,16 @@ "data.corr(method='pearson')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#if we have missing data, we can replace them with the median but not the average.\n", + "#because the median is one of those things that dont change." + ] + }, { "cell_type": "code", "execution_count": 9, @@ -1042,6 +1262,88 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 4.997212\n", + "1 4.442651\n", + "2 5.209486\n", + "3 4.488636\n", + "4 4.919981\n", + "5 4.753590\n", + "6 4.356709\n", + "7 4.744932\n", + "8 5.283204\n", + "9 4.828314\n", + "10 4.700480\n", + "11 5.123964\n", + "12 4.934474\n", + "13 5.241747\n", + "14 5.111988\n", + "15 4.605170\n", + "16 4.770685\n", + "17 4.672829\n", + "18 4.634729\n", + "19 4.744932\n", + "20 4.836282\n", + "21 4.595120\n", + "22 5.278115\n", + "23 4.779123\n", + "24 4.962845\n", + "25 4.828314\n", + "26 4.990433\n", + "27 4.574711\n", + "28 4.976734\n", + "29 4.762174\n", + " ... \n", + "738 4.595120\n", + "739 4.624973\n", + "740 4.787492\n", + "741 4.624973\n", + "742 4.691348\n", + "743 4.941642\n", + "744 5.030438\n", + "745 4.605170\n", + "746 4.990433\n", + "747 4.394449\n", + "748 5.231109\n", + "749 5.087596\n", + "750 4.912655\n", + "751 4.795791\n", + "752 4.682131\n", + "753 5.198497\n", + "754 5.036953\n", + "755 4.852030\n", + "756 4.919981\n", + "757 4.812184\n", + "758 4.663439\n", + "759 5.247024\n", + "760 4.477337\n", + "761 5.135798\n", + "762 4.488636\n", + "763 4.615121\n", + "764 4.804021\n", + "765 4.795791\n", + "766 4.836282\n", + "767 4.532599\n", + "Name: Glucose, Length: 768, dtype: float64" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.log(data)\n", + "np.log((data['Glucose'])) #data['Glucose'] = np.log((data['Glucose'])) to correct the skewness of your data." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1062,6 +1364,13 @@ "in your data." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 16, @@ -1163,6 +1472,31 @@ "sns.pairplot(data)" ] }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"['Pregancies'] not in index\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Glucose'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'BMI'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Pregancies'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2932\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2933\u001b[0m indexer = self.loc._convert_to_indexer(key, axis=1,\n\u001b[0;32m-> 2934\u001b[0;31m raise_missing=True)\n\u001b[0m\u001b[1;32m 2935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2936\u001b[0m \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[1;32m 1352\u001b[0m kwargs = {'raise_missing': True if is_setter else\n\u001b[1;32m 1353\u001b[0m raise_missing}\n\u001b[0;32m-> 1354\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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 1355\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1356\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m 1159\u001b[0m self._validate_read_indexer(keyarr, indexer,\n\u001b[1;32m 1160\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\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;32m-> 1161\u001b[0;31m raise_missing=raise_missing)\n\u001b[0m\u001b[1;32m 1162\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m 1250\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'loc'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mraise_missing\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[1;32m 1251\u001b[0m \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\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;32m-> 1252\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{} not in index\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnot_found\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 1253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1254\u001b[0m \u001b[0;31m# we skip the warning on Categorical/Interval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: \"['Pregancies'] not in index\"" + ] + } + ], + "source": [ + "data[['Glucose', 'BMI', 'Pregancies']].head(1)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2076,18 +2410,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[141 21]\n", - " [ 41 51]]\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", @@ -3865,7 +4190,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.3" }, "varInspector": { "cols": { diff --git a/Thur 20 Feb/untitled b/Thur 20 Feb/untitled new file mode 100644 index 0000000..e69de29 diff --git a/handin_5.csv b/handin_5.csv new file mode 100644 index 0000000..41b7236 --- /dev/null +++ b/handin_5.csv @@ -0,0 +1,759 @@ +Index,CLASS +0,0 +1,0 +2,0 +3,0 +4,0 +5,0 +6,0 +7,0 +8,0 +9,0 +10,0 +11,0 +12,0 +13,0 +14,0 +15,0 +16,0 +17,0 +18,0 +19,0 +20,0 +21,0 +22,0 +23,0 +24,0 +25,0 +26,0 +27,0 +28,0 +29,0 +30,0 +31,0 +32,0 +33,0 +34,0 +35,0 +36,0 +37,0 +38,0 +39,0 +40,0 +41,0 +42,0 +43,0 +44,0 +45,0 +46,0 +47,0 +48,0 +49,0 +50,0 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