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Upload linear_model.py

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+ # Code source: Jaques Grobler
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+ # License: BSD 3 clause
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+
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+ import matplotlib.pyplot as plt
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+ import numpy as np
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+
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+ from sklearn import datasets, linear_model
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+ from sklearn.metrics import mean_squared_error, r2_score
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+
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+ # Load the diabetes dataset
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+ diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
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+
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+ # Use only one feature
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+ diabetes_X = diabetes_X[:, np.newaxis, 2]
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+
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+ # Split the data into training/testing sets
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+ diabetes_X_train = diabetes_X[:-20]
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+ diabetes_X_test = diabetes_X[-20:]
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+
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+ # Split the targets into training/testing sets
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+ diabetes_y_train = diabetes_y[:-20]
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+ diabetes_y_test = diabetes_y[-20:]
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+
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+ # Create linear regression object
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+ regr = linear_model.LinearRegression()
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+
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+ # Train the model using the training sets
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+ regr.fit(diabetes_X_train, diabetes_y_train)
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+
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+ # Make predictions using the testing set
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+ diabetes_y_pred = regr.predict(diabetes_X_test)
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+
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+ # The coefficients
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+ print("Coefficients: \n", regr.coef_)
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+ # The mean squared error
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+ print("Mean squared error: %.2f" % mean_squared_error(diabetes_y_test, diabetes_y_pred))
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+ # The coefficient of determination: 1 is perfect prediction
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+ print("Coefficient of determination: %.2f" % r2_score(diabetes_y_test, diabetes_y_pred))
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+
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+ # Plot outputs
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+ plt.scatter(diabetes_X_test, diabetes_y_test, color="black")
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+ plt.plot(diabetes_X_test, diabetes_y_pred, color="blue", linewidth=3)
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+
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+ plt.xticks(())
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+ plt.yticks(())
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+
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+ plt.show()