Ridge Regression

import pandas as pd 

from sklearn import datasets

from sklearn.model_selection import train_test_split 

from sklearn.linear_model import LinearRegression  

import numpy as np  

from sklearn.metrics import mean_squared_error, r2_score

from sklearn.linear_model import Ridge

dataset = pd.read_csv('Total.csv',names = ['alpha','PIR', 'ptt', 'bpmax' ,'bpmin', 'hrfinal', 'ih', 'il', 'meu'])

X = dataset[['alpha','PIR', 'ptt']]

y = dataset[['bpmin']]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) 

from sklearn.preprocessing import StandardScaler

sc_X=StandardScaler()

x_train=sc_X.fit_transform(X_train)

x_test=sc_X.transform(X_test)

regressor = Ridge(normalize=True)

#regressor = LinearRegression() 

#print dataset.isnull().any()

regressor.fit(X_train, y_train)

y_pred = regressor.predict(X_test)

#print('Coefficients: \n', regressor.coef_)

# The mean squared error

from sklearn import metrics  

print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  

print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  

print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

# # Explained variance score: 1 is perfect prediction

print('Variance score: %.2f' % r2_score(y_test, y_pred))