Design features for house price prediction

## Scenario You are building a model to predict **house sale price** from a tabular dataset (similar to typical real-estate datasets). The interviewer expects a simple baseline model (e.g., linear regression), but wants to understand your reasoning. ## Questions 1. **Which features are likely to be predictive** of house price, and why? (Examples: location, size, age, condition, amenities, nearby schools, etc.) 2. **How do you decide which features are usable** (available at prediction time, not leaking label information, stable definitions)? 3. What **data cleaning** steps would you perform before modeling? 4. If starting with **linear regression**, how would you: - handle missing values, - handle categorical variables, - reduce the impact of outliers/skewed price distributions, - detect multicollinearity and mitigate it? 5. How would you evaluate the model and iterate on improvements? Assume you have a training set with historical sales and a holdout set for evaluation.