IPL Prediction Engine (Hybrid XGBoost + LSTM)

🌐 Live Demo

🚀 Key Features

• Hybrid Architecture: Combines XGBoost (for static match context) and LSTM (for temporal momentum).
• Enriched Context: Real-time integration of historical Open-Meteo Weather Data (dew, temperature, humidity).
• Dynamic Form Tracking: Rolling ELO ratings for teams and players calculated from 2008 to 2024.
• Memory Efficient: Optimized training pipeline using IterableDataset to handle 1000+ match files on low-RAM hardware.
• Real-Time Ready: Built-in support for Redis-based live match streaming.

🛠 Project Structure

• backend/ml_engine/hybrid_model.py: Core model architecture and normalization.
• backend/ml_engine/train_efficient.py: Memory-efficient training pipeline.
• backend/data_pipeline/fetch_weather_data.py: Historical weather data harvester.
• backend/data_pipeline/feature_engineer_elo.py: ELO and form calculation engine.

📦 Installation

1. Clone the repository.
2. Create a virtual environment:
   bash

   1python -m venv .venv 2source .venv/bin/activate
3. Install dependencies:
   pip install -r requirements.txt

📈 Usage

1. Enrich the Data

2. Train the Model

3. Run Inference Test

⚖️ License