Handle multicollinearity in feature selection

You are building an interpretable predictive model for an insurance company, such as a linear or logistic regression model for claim risk. Several input features are highly correlated with one another. Explain what **multicollinearity** is and why it can be a problem in modeling. In your answer, discuss: 1. How multicollinearity affects coefficient estimates, variance, statistical significance, and interpretability. 2. Whether multicollinearity harms prediction performance differently from inference or explanation. 3. How you would detect multicollinearity in practice. 4. How multicollinearity should influence your **feature selection** strategy. 5. What methods you could use to handle it, including tradeoffs among dropping variables, combining variables, regularization, and dimensionality reduction. Assume the business wants both reasonable predictive performance and a model that stakeholders can interpret.