NormalizationVsStandarization_5.py

# training an SVR model
from  sklearn.svm import SVR
# measuring RMSE score
from sklearn.metrics import mean_squared_error

# SVR
svr = SVR(kernel='rbf',C=5)

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
    svr.fit(trainX[i],y_train)
    # predict
    pred = svr.predict(testX[i])
    # RMSE
    rmse.append(np.sqrt(mean_squared_error(y_test,pred)))

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