Designing a City Rent Prediction System

You are designing an end-to-end machine learning system to predict rental prices across a city, leveraging structured listing data, text descriptions, images, and geospatial features. Address the following: (a) **Data collection and cleaning** -- What data sources would you use? What cleaning steps matter most? (b) **Feature engineering** -- How would you extract useful signals from text descriptions, listing photos, and geospatial data? (c) **Model selection** -- What model architectures would you consider, and how would you combine structured and unstructured features? (d) **Validation** -- How should you split data to avoid leakage, given that listings have both temporal and geographic structure? (e) **Fairness and robustness** -- What biases are you worried about, and how do you detect and mitigate them?