Multicollinearity is a common issue in regression analysis where two or more independent variables have a strong linear relationship, making it difficult to determine the unique impact of each variable on the dependent variable. This can lead to difficulty in interpretation and reduced model predictive power. To detect multicollinearity, methods such as Variance Inflation Factor (VIF), correlation matrix, heatmaps, clustermaps, eigenvalues, and conditional index can be used. Once detected, multicollinearity can be mitigated by removing correlated variables, using Principal Component Analysis (PCA) to combine them into a single variable, or employing machine learning models like Ridge Regression that are less sensitive to collinearity.