Detection:

Correlation Matrix and Heatmap: I calculate the pairwise correlation between numerical features. A heatmap visually highlights highly correlated pairs, where values close to +1 or -1 indicate strong linear relationships.

Scatter Plots: For individual pairs of features, scatter plots reveal the nature and strength of their relationship (linear, non-linear).   

Variance Inflation Factor (VIF): For each independent variable, I calculate the VIF, which quantifies how much the variance of its estimated coefficient is inflated due to multicollinearity. A common rule of thumb is that VIF values above 5 or 10 suggest significant multicollinearity.   

Addressing:

Feature Removal: If two or more features are highly correlated, I might remove one of them. The choice depends on domain knowledge and which feature is potentially less important or redundant for the model.

Combining Features: Creating new features that are linear combinations (e.g., sum, average) of the correlated ones can reduce multicollinearity while retaining the information.   

Dimensionality Reduction Techniques: Methods like Principal Component Analysis (PCA) can transform the original features into a smaller set of uncorrelated principal components.   

By employing these techniques during EDA, I aim to identify and mitigate issues related to feature correlation and multicollinearity early in the modeling process. This helps ensure that the subsequent models are more stable, interpretable, and perform better on unseen data.