Question

During exploratory data analysis on a housing price dataset, a data scientist computes a Pearson correlation matrix and finds that 'living_area_sqft' and 'total_rooms' have a correlation coefficient of 0.94. They plan to train a linear regression model. What is the most appropriate action to take based on this finding?

Accepted Answer

Consider dropping one of the two highly correlated features to reduce multicollinearity before training the linear regression model.

Answer

Drop both features because a high correlation means neither is useful for predicting price.

Answer

Apply min-max scaling to both features, which will eliminate the correlation between them.

Answer

Replace the two features with their sum, since combining them removes the multicollinearity concern.