raphael-nibishaka/rwanda-vehicle-insights
Domaine:
socioeconomicType de record:
software
Créateur:
rap
Hôte:
# 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 …