README.md

metadata

title: ZH Apartment Rent Predictor
emoji: 🏠
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false

ZH Apartment Rent Predictor

Predicts monthly rent for apartments in the Canton of Zurich. Built for AI Applications HS24.

Data

• 804 real listings from the canton of Zurich
• Enriched with BFS municipal data (population density, tax income, foreign resident share)
• 59 municipalities covered

Preprocessing

1. Merged listing data with BFS municipal statistics on bfs_number
2. Extracted binary keyword flags from descriptions (Attika, Loft, Seesicht, Luxuriös, Pool, Exklusiv)
3. Area categorised into 3 buckets (< 50 m², 50–99 m², 100+ m²)
4. Added zurich_city flag for city of Zurich listings
5. Log-transformed pop_dens and tax_income to reduce skew
6. Derived room_per_m2 and area_per_room ratios
7. Added has_premium_keyword (new feature) — 1 if any luxury keyword present
8. All numeric features scaled with RobustScaler

Models & Results (5-fold CV)

Iteration	Model	MAE	RMSE	R²
1	Ridge (α=10)	446	671	0.607
1	Lasso (α=10)	448	678	0.599
2	Random Forest	439	659	0.618
2	Gradient Boosting ✓	422	641	0.638

Final model: GradientBoostingRegressor(n_estimators=400, learning_rate=0.04, max_depth=5, subsample=0.8)

New Feature

has_premium_keyword — binary flag consolidating six sparse keyword columns into one stable signal. Ranks in the top 10 most important features.

Files

• app.py — Flask app + HTML (all data baked in, no extra JSON files needed)
• model.joblib — trained model
• Dockerfile / requirements.txt — deployment config