raphael-nibishaka/rwanda-vehicle-insights

# Vehicle Analytics System A Django web application integrating machine learning models for comprehensive vehicle sales analysis and prediction. ## Overview This system provides intelligent vehicle analytics through three core ML models: - **Price Prediction**: Forecast vehicle selling prices using regression analysis - **Income Classification**: Categorize customer income levels - **Customer Segmentation**: Group clients using K-Means clustering ## Technology Stack - **Backend**: Django 4.x - **Machine Learning**: scikit-learn, pandas, numpy - **Data Visualization**: matplotlib, seaborn, plotly - **Frontend**: Bootstrap 5, HTML templates - **Model Persistence**: joblib ## Project Structure ``` vehicles-prediction/ ├── manage.py ├── config/ # Django configuration │ ├── settings.py │ ├── urls.py │ └── wsgi.py ├── predictor/ # Main application │ ├── views.py │ ├── urls.py │ ├── models.py │ └── templates/predictor/ │ ├── index.html │ ├── regression_analysis.html │ ├── classification_analysis.html │ └── clustering_analysis.html ├── model_generators/ # ML training scripts │ ├── regression/ │ ├── classification/ │ └── clustering/ ├── dummy-data/ │ └── vehicles_ml_dataset.csv ├── requirements.txt └── *.pkl # Trained models ``` ## Installation ### Prerequisites - Python 3.9+ - pip package manager - Virtual environment (recommended) ### Setup 1. **Clone the repository** ```bash git clone github.com cd vehicles-prediction ``` 2. **Create and activate virtual environment** ```bash python -m venv venv # Windows: venv\Scripts\activate # Mac/Linux: source venv/bin/activate ``` 3. **Install dependencies** ```bash pip install -r requirements.txt ``` 4. **Prepare dataset** - Place `vehicles_ml_dataset.csv` in `dummy-data/` - Required columns: `year`, `kilometers_driven`, `seating_capacity`, `estimated_income`, `selling_pr …