ElasticNetRegression.py

import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet


df = pd.read_csv('data.csv')

x = 
y = 

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 66)

enet = ElasticNet()

enet.fit(x_train, y_train)

print("ElasticNet Train RMSE:", np.round(np.sqrt(metrics.mean_squared_error(y_train, enet.predict(x_train))),5))
print("ElasticNet Test RMSE:", np.round(np.sqrt(metrics.mean_squared_error(y_test, enet.predict(x_test))),5))