Tel Aviv Apartments Rent Prediction Project

This project aims to predict apartment rental prices in Tel Aviv using machine learning models. The notebook covers data wrangling, feature engineering, and building predictive models using Elastic Net and Decision Trees. We evaluate the performance of both models and compare their results.

📓 Notebook

👉 Open on nbviewer

⚙️ Technologies

• Python — main programming language
• Pandas — for data wrangling and preprocessing
• Scikit-learn — for building and training models (Elastic Net, Decision Trees)
• Matplotlib & Seaborn — for visualizations
• Pickle — to save the trained models

📂 Project Structure

File/Folder	Description
apartment_rent_prediction.ipynb	Main Jupyter Notebook (data wrangling, feature engineering, modeling)
train.csv	Training dataset
en_model.pkl	Saved Elastic Net trained model
README.md	Project documentation

▶️ How to run

You can get this project in two ways:

Option 1 – Using Git

git clone https://github.com/adirbella37/RentalPrice-ML-Modeling.git
cd RentalPrice-ML-Modeling

Option 2 – Download as ZIP

1. Click the green Code button at the top of this repository
2. Select Download ZIP
3. Extract the ZIP file on your computer

📈 Key Insights

• ElasticNet: Regularization helped handle multicollinearity, but overall performance was weaker compared to tree-based models. Removing redundant features simplified the model without reducing accuracy.
• XGBoost: Outperformed all tested models (ElasticNet, Random Forest, Decision Tree, Gradient Boosting) with lower RMSE/MAE and higher R². Captured non-linear interactions and leveraged engineered features effectively.
• Feature importance: Both models ranked apartment area as the most critical feature. ElasticNet emphasized linear predictors like property type and room count, while XGBoost highlighted complex interactions (e.g., area × elevator, parking, AC, neighborhood).
• Rare categories: Grouping infrequent neighborhoods improved stability in XGBoost, while ElasticNet handled them well using Target Encoding with smoothing.
• Model choice: XGBoost was selected as the final model due to superior predictive performance and robustness to complex feature interactions.

📜 License

This project is licensed under the MIT License.