vif in python

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model  import LinearRegression
from sklearn.metrics import mean_squared_error
import warnings
warnings.filterwarnings('ignore')

X_train, X_test, y_train, y_test = train_test_split(df[vif_df.attributes.values], y)

#Data Engineering
encoder = StandardScaler()
encoder.fit(X_train)
X_train[X_train.columns] = encoder.transform(X_train)
X_test[X_test.columns] = encoder.transform(X_test)

#Model Implementation
model = LinearRegression()
model.fit(X_train, y_train)

#Model Scoring
predictions=model.predict(X_test)
mean_squared_error(predictions,y_test)