Upload linear_model.py

linear_model.py

@@ -0,0 +1,47 @@
+# Code source: Jaques Grobler
+# License: BSD 3 clause
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from sklearn import datasets, linear_model
+from sklearn.metrics import mean_squared_error, r2_score
+
+# Load the diabetes dataset
+diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
+
+# Use only one feature
+diabetes_X = diabetes_X[:, np.newaxis, 2]
+
+# Split the data into training/testing sets
+diabetes_X_train = diabetes_X[:-20]
+diabetes_X_test = diabetes_X[-20:]
+
+# Split the targets into training/testing sets
+diabetes_y_train = diabetes_y[:-20]
+diabetes_y_test = diabetes_y[-20:]
+
+# Create linear regression object
+regr = linear_model.LinearRegression()
+
+# Train the model using the training sets
+regr.fit(diabetes_X_train, diabetes_y_train)
+
+# Make predictions using the testing set
+diabetes_y_pred = regr.predict(diabetes_X_test)
+
+# The coefficients
+print("Coefficients: \n", regr.coef_)
+# The mean squared error
+print("Mean squared error: %.2f" % mean_squared_error(diabetes_y_test, diabetes_y_pred))
+# The coefficient of determination: 1 is perfect prediction
+print("Coefficient of determination: %.2f" % r2_score(diabetes_y_test, diabetes_y_pred))
+
+# Plot outputs
+plt.scatter(diabetes_X_test, diabetes_y_test, color="black")
+plt.plot(diabetes_X_test, diabetes_y_pred, color="blue", linewidth=3)
+
+plt.xticks(())
+plt.yticks(())
+
+plt.show()