from statsmodels.stats.outliers_influence import variance_inflation_factor # creating dummies for gender data['Gender'] = data['Gender'].map({'Male':0, 'Female':1}) # the independent variables set X = data[['Gender', 'Height', 'Weight']] # VIF dataframe vif_data = pd.DataFrame() vif_data["feature"] = X.columns # calculating VIF for each feature vif_data["VIF"] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))] print(vif_data)