Initial commit. I've already played around with the first two exercises.

01_training_a_linear_regression_model/extra_credit_train_gradient_boosting_model.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.metrics import mean_absolute_error
+from sklearn.externals import joblib
+
+# Load our data set
+df = pd.read_csv("house_data.csv")
+
+# Create the X and y arrays
+X = df[["sq_feet", "num_bedrooms", "num_bathrooms"]]
+y = df["sale_price"]
+
+# Split the data set in a training set (75%) and a test set (25%)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
+
+# Create the Linear Regression model
+model = GradientBoostingRegressor()
+
+# Train the model
+model.fit(X_train, y_train)
+
+# Save the trained model to a file so we can use it to make predictions later
+joblib.dump(model, 'house_value_model.pkl')
+
+# Report how well the model is performing
+print("Model training results:")
+
+# Report an error rate on the training set
+mse_train = mean_absolute_error(y_train, model.predict(X_train))
+print(f" - Training Set Error: {mse_train}")
+
+# Report an error rate on the test set
+mse_test = mean_absolute_error(y_test, model.predict(X_test))
+print(f" - Test Set Error: {mse_test}")
+