NormalizationVsStandarization_6.py

# training a Decision Tree model
from sklearn.tree import DecisionTreeRegressor
# measuring RMSE score
from sklearn.metrics import mean_squared_error

# Decision tree
dt = DecisionTreeRegressor(max_depth=10,random_state=27)

rmse = []

# raw, normalized and standardized training and testing data
trainX = [X_train,X_train_norm,X_train_stand]
testX = [X_test,X_test_norm,X_test_stand]

# model fitting and measuring RMSE
for i in range(len(trainX)):
    
    # fit
    dt.fit(trainX[i],y_train)
    # predict
    pred = dt.predict(testX[i])
    # RMSE
    rmse.append(np.sqrt(mean_squared_error(y_test,pred)))

# visualizing the result    
df_dt = pd.DataFrame({'RMSE':rmse},index=['Original','Normalized','Standardized'])
df_dt