How do I check for multicollinearity in my regression model?

How do I check for multicollinearity in my regression model?

You can check for multicollinearity using Variance Inflation Factor (VIF) or by examining the correlation matrix.

What is a good VIF value to consider?

A VIF value above 10 is often considered indicative of high multicollinearity, but some researchers use a threshold of 5.

Can you show me how to calculate VIF in Python?

Sure! You can use the statsmodels library. Here's a simple example:

import statsmodels.api as sm
from statsmodels.stats.outliers_influence import variance_inflation_factor

X = sm.add_constant(data[['feature1', 'feature2', 'feature3']])
VIF = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]
print(VIF)

What should I do if I find high multicollinearity?

You can consider removing one of the correlated variables, combining them, or using techniques like ridge regression.