Gradient boosting regressor on boston dataset

Author: abdoulayegk

machine_learning/Gradient-boosting-regressor.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[17]:
+
+
+"""Implementation of GradientBoostingRegressor in sklearn using the 
+   boston dataset which is very popular for regression problem to 
+   predict house price.
+"""
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_boston
+from sklearn.metrics import mean_squared_error,r2_score
+from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
+from sklearn.model_selection import train_test_split
+
+def main():
+    # loading the dataset from the sklearn package
+    df = load_boston()
+    print(df.keys())
+    # now let constract a data frame with data and target variables
+    df_boston = pd.DataFrame(df.data,columns =df.feature_names)
+    # let add the target to the dataframe
+    df_boston['Price']= df.target
+    # let us print the first five rows using the head function
+    print(df_boston.head())
+    print(df_boston.describe().T) # to see summary statistics of the dataset
+    # Feature selection means for independant and dependent variables
+    X = df_boston.iloc[:,:-1]
+    y = df_boston.iloc[:,-1] # target variable
+    # we are going to 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 = .25)
+    # now let set the parametes of our model
+    params = {'n_estimators': 500, 'max_depth': 5, 'min_samples_split': 4,
+          'learning_rate': 0.01, 'loss': 'ls'}
+    model = GradientBoostingRegressor(**params)
+    # training the model
+    model.fit(X_train,y_train)
+    """ let have a look on the train and test score to see how good the model fit the data"""
+    score = model.score(X_train,y_train).round(3)
+    print("Training score of GradientBoosting is :",score)
+    print("the test score of GradienBoosting is :",model.score(X_test,y_test).round(3))
+    # Let us evaluation the model by finding the errors 
+    y_pred = model.predict(X_test)
+
+    # The mean squared error
+    print("Mean squared error: %.2f"% mean_squared_error(y_test, y_pred))
+    # Explained variance score: 1 is perfect prediction
+    print('Test Variance score: %.2f' % r2_score(y_test, y_pred))
+    
+    # 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 ploting 
+    plt.show()
+    
+
+if __name__ =='__main__':
+    main()
+
+
+# In[ ]:
+
+
+
+