← All drillsBuilding a House-Price Predictor
What you'll be able to do- ✓Build a house-price predictor that estimates a home's value from its features
- ✓Implement linear regression from scratch with the normal equation
- ✓Split data into train/test and measure real generalization (MAE, RMSE, R²)
- ✓Read a model's coefficients to explain what drives price
- ✓Engineer and scale features to beat a baseline
- ✓Ship the predictor as a reusable scikit-learn pipeline you can save and reload
Build the estimate behind a "what's my home worth?" tool — turn a table of house features into a dollar prediction, the way Zillow's Zestimate and Redfin do.
⌁ The engine behind automated home-valuation (AVM) products — Zillow (Zestimate), Redfin, Opendoor — and behind price/estimate features in marketplaces and insurance.
Start this internshipCreate an account to unlock the 10 sections, the workbench, and AskThili.
BeginSections
1. Building a House-Price Predictor
🔒 locked2. Lesson 1 - The problem & the data
🔒 locked3. Lesson 2 - A baseline & how to score it
🔒 locked4. Lesson 3 - Linear regression from scratch
🔒 locked5. Lesson 4 - Train / test — the honest score
🔒 locked6. Lesson 5 - Reading the model
🔒 locked7. Lesson 6 - Better features
🔒 locked8. Lesson 7 - Production regression
🔒 locked9. Lesson 8 - When a straight line isn't enough
🔒 locked10. Lesson 9 - The price predictor
🔒 lockedDig deeper
📄Hedonic Housing Prices and the Demand for Clean Air (Harrison & Rubinfeld, 1978)
paper🔗scikit-learn — Linear Models (Ordinary Least Squares) user guide
docs🔗Python Data Science Cookbook (Subramanian, Packt) — the source recipes
bookPart of these learning paths
I want to become an ML engineer who ships and operates models in production
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