Checking for multicollinearity is a routine task while solving a data problem.

Most people rely on pairwise correlations for detecting multicollinearity which is a faulty approach in many scenarios

So what to do? Check this ↓

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Multicollinearity creates a problem in the interpretation of the model when some predictors explain predictors.

Because then we are unable to understand the effect of each predictor in isolation towards the target variable and our coefficients become less useful.

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Variance Inflation Factor or VIF is an efficient method to check for multicollinearity.

Variance Inflation refers to the inflation in the variance of the estimated coefficient of the independent variable because of the presence of multicollinearity.

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We could have low pairwise correlations, but still have high VIF and vice-versa.

A strong relationship is possible between a predictor and other variables together combined, even though there's no high correlation individually.

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Hence we prefer VIF

VIF performs a set of multivariate regression analyses to check the dependence among the independent variables by fitting multiple regression models on the dependent variables.

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