---
title: Exercise1
emoji: "🏃"
colorFrom: gray
colorTo: gray
sdk: gradio
app_file: app.py
pinned: false
---

# Model Iterations Documentation  
## Task: Apartment Price Prediction (Regression)

## Application Link

**Public URL (Hugging Face Space):** 

https://huggingface.co/spaces/nbacchi/exercise1

---

## Summary of Iterative Process

| Iteration | Objective | Key Changes | Models Used | CV Mean R² | CV Std Dev | Change in Performance | Fit Diagnosis |
|------------|------------|-------------|-------------|------------|------------|-----------------------|----------------|
| **1** | Build baseline model | - Drop missing values<br>- Remove duplicates<br>- Price filter (750–8000 CHF)<br>- Valid rooms/area filter<br>- 5-fold CV | Linear Regression<br>Random Forest (n_estimators=300) | 0.5446 (LR)<br>0.5178 (RF) | 0.1071 (LR)<br>0.1195 (RF) | Baseline | ☑ Overfitting ☐ Underfitting ☐ Good Fit |
| **2** | Improve generalization | - Feature engineering<br>- municipality_area_proxy = pop/pop_dens<br>- emp_per_resident = emp/pop<br>- foreigner_count_est = pop×(frg_pct/100)<br>- Hyperparameter tuning<br>- 5-fold CV | Ridge (alpha=1.0)<br>Tuned Random Forest (n_estimators=500, max_depth=12, min_samples_split=5, min_samples_leaf=2) | 0.5297 (Ridge)<br>0.5509 (RF) | 0.0947 (Ridge)<br>0.1060 (RF) | +0.0331 (RF) | ☐ Overfitting ☐ Underfitting ☑ Good Fit |

---

## Detailed Metrics Comparison

### Iteration 1 – Baseline
| Model | CV Mean R² | CV Std R² | CV Mean RMSE | CV Mean MAE |
|-------|---:|---:|---:|---:|
| Linear Regression | 0.5446 | 0.1071 | 673.00 | 468.07 |
| Random Forest | 0.5178 | 0.1195 | 698.51 | 500.13 |

### Iteration 2 – Feature Engineering
| Model | CV Mean R² | CV Std R² | CV Mean RMSE | CV Mean MAE |
|-------|---:|---:|---:|---:|
| Ridge | 0.5297 | 0.0947 | 682.01 | 481.08 |
| Tuned Random Forest | 0.5509 | 0.1060 | 674.54 | 473.98 |

---

## Created Features

**Iteration 2 Feature Engineering:**
- `municipality_area_proxy` = population / population density  
- `emp_per_resident` = employees / population  
- `foreigner_count_est` = population × (foreigner_pct / 100)  

All features are reproducible from municipality-level variables and can be computed in real-time in the web application.

**In der App angezeigte Bezeichnungen (Deutsch):**
- `municipality_area_proxy` → **Gemeindegröße**
- `emp_per_resident` → **Arbeitsplatzquote**
- `foreigner_count_est` → **Ausländerpopulation**

---

## Final Selected Features

**Feature Set for Final Model:**
- `rooms` – number of apartment rooms
- `area` – living area in m²
- `pop` – municipality population
- `pop_dens` – population density (per km²)
- `frg_pct` – percentage of foreign residents
- `emp` – number of employees in municipality
- `tax_income` – taxable income per capita
- `municipality_area_proxy` – proxy for geographic size
- `emp_per_resident` – economic activity indicator
- `foreigner_count_est` – estimated foreigner count

---

## Reason for Selection

**Final model:** `RandomForestRegressor` (tuned from iteration 2)  
**Justification:**
- Highest cross-validated $R^2$ across all iterations (0.5509)
- Lowest generalization gap (CV Std = 0.1060 vs baseline 0.1195)
- Feature engineering improves predictive power by +0.0331 in $R^2$
- Tuned hyperparameters reduce overfitting (`max_depth=12`, `min_samples_split=5`)
- RMSE of CHF 674.54 acceptable for price range 750–8000

---

## Preprocessing Steps (Iteration 1 → 2)

### Data Cleaning
1. Load original dataset (apartments in canton Zurich)
2. Remove rows with missing values (`dropna()`)
3. Remove duplicate rows (`drop_duplicates()`)
4. Filter unrealistic prices: keep `750 ≤ price ≤ 8000` CHF
5. Filter invalid structures: keep `rooms > 0` and `area > 0`

### Feature Engineering (Iteration 2)
1. Compute `municipality_area_proxy` from `pop` and `pop_dens`
2. Compute `emp_per_resident` from `emp` and `pop`
3. Compute `foreigner_count_est` from `pop` and `frg_pct`
4. Combine with baseline features for final training

### Evaluation Method
- 5-fold cross-validation
- Metrics: $R^2$, RMSE, MAE
- No separate validation set (full data used with CV)

---

## Metric Definition

**$R^2$ (Coefficient of Determination):**  
Proportion of variance in price explained by features. Range: [0, 1]. Higher is better.

**RMSE (Root Mean Squared Error):**  
Square root of average squared prediction error. Units: CHF. Lower is better.

**MAE (Mean Absolute Error):**  
Average absolute prediction error. Units: CHF. Lower is better.

---

## Application & Deployment

- **App Framework:** Gradio
- **App File:** [app.py](app.py)
- **Saved Model:** [models/apartment_price_model.pkl](models/apartment_price_model.pkl)
- **Deployment Platform:** Hugging Face Spaces (URL to be updated)

### How to Run Locally
```bash
cd Projekt1
uv run python app.py
```

---

## Submission Checklist (Mandatory)

- [x] Trained regression model available ([models/apartment_price_model.pkl](models/apartment_price_model.pkl))
- [x] New feature(s) added (iteration 2 feature engineering)
- [x] Working web application ([app.py](app.py))
- [x] Documented iterative modeling process (2 iterations, tables + metrics)
- [x] Completed README
- [x] README uploaded to Hugging Face repository
- [x] Public application link inserted above

---

## Notes

- Baseline R² (0.5446) is competitive for real estate price prediction
- Feature engineering provides modest +0.0331 improvement in $R^2$
- Standard deviation drop (0.1195 → 0.1060) indicates more stable predictions
- Model saved and ready for deployment on Hugging Face Spaces