To detect multicollinearity, I begin by analyzing the correlation matrix, which calculates the pairwise correlation between numerical features. High correlation coefficients—values close to +1 or -1—indicate strong linear relationships. These relationships can be easily visualized using heatmaps, which help highlight highly correlated feature pairs. Scatter plots are also useful for visualizing the relationship between feature pairs, revealing both linear and non-linear correlations. Another important method is the Variance Inflation Factor (VIF), which quantifies how much the variance of an estimated regression coefficient is increased due to multicollinearity. As a rule of thumb, VIF values greater than 5 or 10 are indicative of significant multicollinearity.

To address multicollinearity, I may remove redundant features when two or more are highly correlated. The decision on which feature to remove depends on domain expertise and the specific objectives of the analysis. Alternatively, correlated features can be combined into a single, more informative variable using techniques such as averaging or creating interaction terms. Principal Component Analysis (PCA) is another effective approach; it transforms the original correlated variables into a new set of uncorrelated principal components, thereby reducing dimensionality. Additionally, regularization techniques like Ridge and Lasso regression, typically applied during the modeling phase, can help reduce the effects of multicollinearity by penalizing large coefficient estimates.

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.