XGBoost Traffic Prediction API

A FastAPI-based REST API for predicting traffic congestion using XGBoost model. The API provides real-time traffic predictions for various intersections in the city.

Features

• Real-time traffic congestion prediction
• Support for multiple intersection points
• Congestion level and average speed estimation
• RESTful API with FastAPI
• Swagger documentation included

Project Structure

xgboost-api/
├── data/
│   ├── preprocessing.py      # Data preprocessing utilities
│   └── xgboost_model.pkl    # Trained XGBoost model (Git LFS)
├── main.py                  # FastAPI application
├── requirements.txt         # Project dependencies
└── README.md               # Project documentation

Installation

1. Clone the repository:

git clone https://github.com/PrajwalShetty-114/XG-Boost-Model.git
cd XG-Boost-Model

2. Create and activate a virtual environment:

# Windows
python -m venv .venv
.venv\Scripts\activate  # CMD
# OR
source .venv/Scripts/activate  # Git Bash

3. Install dependencies:

pip install -r requirements.txt

Usage

1. Start the API server:

python -m uvicorn main:app --reload --host 0.0.0.0 --port 8000

2. Access the API:

• API Root: http://127.0.0.1:8000
• Interactive API Documentation: http://127.0.0.1:8000/docs

3. Make predictions using the API:

curl -X POST "http://127.0.0.1:8000/predict/" \
     -H "Content-Type: application/json" \
     -d '{
       "model": "xgboost",
       "junctionName": "Intersection_Trinity Circle",
       "predictionTime": "Next Hour"
     }'

API Endpoints

GET /

• Returns API status
• Response: {"message": "XGBoost Traffic Prediction API is running!"}

POST /predict/

• Makes traffic predictions for a given intersection
• Request Body:

  {
    "model": "xgboost",
    "junctionName": "Intersection_Trinity Circle",
    "predictionTime": "Next Hour"
  }

• Response:

  {
    "predictions": {
      "congestion": {
        "level": 0.5,
        "label": "Medium"
      },
      "avgSpeed": 30
    },
    "alternativeRoute": null
  }

Supported Intersections

The model supports predictions for the following intersections:

• Intersection_100 Feet Road
• Intersection_Anil Kumble Circle
• Intersection_Ballari Road
• Intersection_CMH Road
• Intersection_Hebbal Flyover
• Intersection_Hosur Road
• Intersection_ITPL Main Road
• Intersection_Jayanagar 4th Block
• Intersection_Marathahalli Bridge
• Intersection_Sarjapur Road
• Intersection_Silk Board Junction
• Intersection_Sony World Junction
• Intersection_South End Circle
• Intersection_Trinity Circle
• Intersection_Tumkur Road
• Intersection_Yeshwanthpur Circle

Dependencies

• fastapi>=0.110.0
• uvicorn[standard]>=0.23.0
• scikit-learn>=1.3.0
• pandas>=2.1.0
• xgboost>=2.0.0
• joblib>=1.3.0
• pydantic>=2.4.0
• python-multipart>=0.0.6

Note

The XGBoost model file (data/xgboost_model.pkl) is stored using Git LFS due to its size. Make sure you have Git LFS installed when cloning the repository to get the actual model file.

License

This project is licensed under the MIT License - see the LICENSE file for details.