python-machine-learning.rar

"""Implementation of GradientBoostingRegressor in sklearn using the boston dataset which is very popular for regression problem to predict house price. """ import matplotlib.pyplot as plt import pandas as pd from sklearn.datasets import load_boston from sklearn.ensemble import GradientBoostingRegressor from sklearn.metrics import mean_squared_error, r2_score from sklearn.model_selection import train_test_split def main(): # loading the dataset from the sklearn df = load_boston() print(df.keys()) # now let construct a data frame df_boston = pd.DataFrame(df.data, columns=df.feature_names) # let add the target to the dataframe df_boston["Price"] = df.target # print the first five rows using the head function print(df_boston.head()) # Summary statistics print(df_boston.describe().T) # Feature selection x = df_boston.iloc[:, :-1] y = df_boston.iloc[:, -1] # target variable # split the data with 75% train and 25% test sets. x_train, x_test, y_train, y_test = train_test_split( x, y, random_state=0, test_size=0.25 ) model = GradientBoostingRegressor( n_estimators=500, max_depth=5, min_samples_split=4, learning_rate=0.01 ) # training the model model.fit(x_train, y_train) # to see how good the model fit the data training_score = model.score(x_train, y_train).round(3) test_score = model.score(x_test, y_test).round(3) print("Training score of GradientBoosting is :", training_score) print("The test score of GradientBoosting is :", test_score) # Let us evaluation the model by finding the errors y_pred = model.predict(x_test) # The mean squared error print(f"Mean squared error: {mean_squared_error(y_test, y_pred):.2f}") # Explained variance score: 1 is perfect prediction print(f"Test Variance score: {r2_score(y_test, y_pred):.2f}") # So let's run the model against the test data fig, ax = plt.subplots() ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0)) ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=4) ax.set_xlabel("Actual") ax.set_ylabel("Predicted") ax.set_title("Truth vs Predicted") # this show function will display the plotting plt.show() if __name__ == "__main__": main()