In the context of EDA, you find that certain features in your dataset are highly correlated. How would you interpret this finding and how might it affect your analysis?

High correlation between features indicates multicollinearity. This can be problematic in certain types of models (like linear regression) as it can destabilize the model and make the effects of predictor variables hard to separate. Depending on the severity of multicollinearity, you may need to consider it during model selection or feature engineering steps, such as removing highly correlated variables, combining them, or using regularization techniques.