Ohenedarkoh/Ghana-housing-insights

# Ghana Housing Insights: Predicting Rental Prices with Machine Learning This project delivers an end-to-end data science solution for analyzing and predicting rental housing prices in Ghana. It covers automated web scraping, data processing, machine learning model development, and API deployment to provide data-driven insights for the real estate market. ## Key Features * **Data Acquisition**: Python web scraper for Meqasa property listings. * **ETL Pipeline**: Data cleaning, feature engineering (including advanced amenity grouping), and loading into a **PostgreSQL** database. * **Price Prediction Model**: Optimized **XGBoost Regressor** for housing price prediction. * **Real-time API**: **FastAPI** for serving model predictions with interactive Swagger UI. * **Business Intelligence**: **Power BI dashboards** for market insights. ## Technologies Used Python (Pandas, NumPy, BeautifulSoup4, Requests, Scikit-Learn, XGBoost, FastAPI, Pydantic, Uvicorn, SQLAlchemy), PostgreSQL, Microsoft Power BI, Jupyter Notebook, Render.com. ## Setup & Local Installation 1. **Clone the repository:** ```bash git clone github.com cd Ghana-housing-insights ``` 2. **Create & activate virtual environment.** 3. **Install dependencies:** `pip install -r requirements.txt` 4. **PostgreSQL & Environment Variables**: Set up a PostgreSQL database and configure credentials in a `.env` file (added to `.gitignore`). ## Usage * **Run ETL**: Execute `notebooks/cleaning.ipynb` to clean data and load into PostgreSQL. * **Train Model**: Run `notebooks/xgboost.ipynb` to train, tune, and save the XGBoost model. * **Run API Locally**: From project root, use `uvicorn model.api.app:app --reload`. Access docs at `ghana-housing-insights-1.on…`. ## Deployment The FastAPI application is deployed as a Web Service on **Render.com**. * **Deployed API Base URL**: ghana-housing-insights-1.on… * **Deploye …