joel1-stack/jabba-house-

# 🏠 Jabba House Price Predictor A machine learning-powered house price prediction application for Nairobi, Kenya, built with Streamlit. ## Features - **Price Prediction**: ML-powered house price estimation - **Data Explorer**: Interactive data visualization and analysis - **Model Training**: Train and compare different ML models - **Market Insights**: Compare with similar properties - **Nairobi Focus**: Tailored for Kenyan real estate market ## Installation 1. Clone or download this repository 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Run the application: ```bash streamlit run app.py ``` ## Usage ### 1. Price Predictor - Enter house details (size, bedrooms, location) - Get instant price predictions - View market comparisons and insights ### 2. Data Explorer - Visualize house price data - Explore price distributions by area - Download data for analysis ### 3. Model Training - Train Linear Regression or Random Forest models - Compare model performance - Save trained models ## Project Structure ``` HousePriceML/ ├── app.py # Main Streamlit application ├── config.py # Configuration settings ├── requirements.txt # Python dependencies ├── README.md # This file ├── data/ # Data files ├── models/ # Trained ML models ├── pages/ # Additional app pages │ ├── data_explorer.py │ └── model_training.py └── utils/ # Utility modules ├── data_handler.py └── model_handler.py ``` ## Nairobi Areas Covered - Eastlands (Kayole, Umoja) - Score: 3 - South B/C, Langata - Score: 5 - Kasarani, Roysambu - Score: 6 - Kilimani, Kileleshwa - Score: 7 - Westlands, Lavington - Score: 9 - Karen, Runda - Score: 10 ## Technology Stack - **Frontend**: Streamlit - **ML**: Scikit-learn - **Data**: Pandas, NumPy - **Visualization**: Plotly, Seaborn - **Language**: Python 3.8+ ## Future Enhancements - Real estate API integration - Google Maps integration - M …