Assignment1 #1
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| import pandas as pd | ||
| import plotly.express as px | ||
| from sklearn.ensemble import RandomForestClassifier | ||
| from sklearn.linear_model import LogisticRegression | ||
| from sklearn.metrics import classification_report, confusion_matrix | ||
| from sklearn.model_selection import train_test_split | ||
| from sklearn.preprocessing import StandardScaler | ||
| from sklearn.svm import SVC | ||
| from sklearn.pipeline import Pipeline | ||
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| #Loading data into Pandas | ||
| df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', | ||
| names=["SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm", "Species"]) | ||
| print(df) | ||
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| #Summarizing stats | ||
| print(df.describe()) | ||
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| #Some Random Figures | ||
| fig = px.scatter(df, x="SepalWidthCm", y="SepalLengthCm", color="Species", size='PetalLengthCm', | ||
| hover_data=['PetalWidthCm']) | ||
| #fig.show() | ||
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| fig2 = px.bar(df, x="SepalWidthCm", y="SepalLengthCm", color="Species") | ||
| #fig2.show() | ||
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| fig3 = px.box(df, y="PetalWidthCm", color="Species", points='all') | ||
| #fig3.show() | ||
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| fig4 = px.violin(df, y="PetalLengthCm", color="Species", violinmode='overlay', hover_data=df.columns) | ||
| #fig4.show() | ||
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| fig5 = px.ecdf(df, x="SepalLengthCm", y="SepalWidthCm", color="Species", ecdfnorm=None) | ||
| #fig5.show() | ||
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There was a problem hiding this comment. do fig1.show(),fig2.show(), etc so we can see the plots. the code runs fine but they don't show up unless you have them
Owner
Author
There was a problem hiding this comment. Thanks for the note! I commented fig.show() to the code. |
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| #we can tell Setosa tend to have larger sepal width, while versicolor and virginica have larger sepal length | ||
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| #Train and Test Datasets | ||
| X = df.drop('Species', axis=1) | ||
| y = df['Species'] | ||
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| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20) | ||
| print(X_train) | ||
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| scale = StandardScaler() | ||
| scale.fit(X_train) | ||
| X_train_sc = scale.transform(X_train) | ||
| X_test_sc = scale.transform(X_test) | ||
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| #Random Forest | ||
| clf = RandomForestClassifier(n_estimators=100) | ||
| clf.fit(X_train_sc, y_train) | ||
| predictor = clf.predict(X_test_sc) | ||
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| pipeline = Pipeline([('scaler', StandardScaler()), ('classifier', RandomForestClassifier())]) | ||
| pipeline.fit(X_train_sc, y_train) | ||
| r2 = pipeline.score(X_test_sc, y_test) | ||
| print(f"RFR: {r2}") | ||
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| #Other models - SVC and Logistic Regression | ||
| svclassifier = SVC(kernel='poly', degree=8) | ||
| svclassifier.fit(X_train_sc, y_train) | ||
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There was a problem hiding this comment. Again the code runs fine here and everything ran right, but print out the values you get from the classifier.fit so we can see if the models are accurate or not
Owner
Author
There was a problem hiding this comment. Well the code includes confusion matrix for both models which shows accuracy, precision, recall and F1 score. |
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| predictor_svc = svclassifier.predict(X_test_sc) | ||
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| print(confusion_matrix(y_test, predictor_svc)) | ||
| print(classification_report(y_test, predictor_svc)) | ||
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| logreg = LogisticRegression() | ||
| logreg.fit(X_train_sc, y_train) | ||
| predictor_reg = logreg.predict(X_test_sc) | ||
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| print(confusion_matrix(y_test, predictor_reg)) | ||
| print(classification_report(y_test, predictor_reg)) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1,26 @@ | ||
| fastapi | ||
| flake8==5.0.4 | ||
| flask | ||
| graphviz | ||
| gunicorn | ||
| isort[requirements] | ||
| netcal | ||
| nose | ||
| numpy | ||
| pandas | ||
| pip-tools | ||
| pre-commit | ||
| plotly | ||
| pydot | ||
| pygam | ||
| pyspark | ||
| pyspark-stubs | ||
| requests | ||
| scikit-learn | ||
| seaborn | ||
| statsmodels | ||
| sqlalchemy | ||
| sympy | ||
| uvicorn | ||
| wheel | ||
|
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