Live Football Match Data & Predictive Analytics Web App

Introduction

This project is a full-stack web application that streams live football (soccer) match data, performs predictive analytics using Python, and provides real-time forecasts. Built with Django REST Framework for the backend and Next.js with Tailwind CSS for the frontend, this app demonstrates how to create and deploy a production-ready predictive analytics system.

The project is designed as a demo for students interested in learning how to build data-driven web applications using modern web technologies. It is part of a 10-part tutorial series that guides users through backend setup, API development, frontend integration, forecasting, and deployment on AWS EC2.

Features

• Live Football Match Data Streaming: Continuously updated match statistics simulated
• Predictive Analytics & Forecasting: Predict live match outcomes during the game, minute by minute
• Django REST API: Provides structured data for frontend consumption.
• Next.js Frontend with Tailwind CSS: Displays live data with a modern UI presenting actionable insights
• Automated Data Generation: datafactory.py script populates data dynamically.
• Deployment on AWS EC2: Configured for production use with Apache and cron jobs.

Tech Stack

• Backend: Django, Django REST Framework, PostgreSQL, Python statsmodels
• Frontend: Next.js, React, Tailwind CSS
• Deployment: AWS EC2 (Ubuntu), AWS RDS, Apache, Cron Jobs

Setup Instructions

Backend Setup

1. Clone the repository:

   git clone `https://github.com/cagdasyetkin/betrade.git`

2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. Run the backend:

   python manage.py runserver

Frontend Setup

1. Navigate to the frontend directory:

   cd ../frontend

2. Install dependencies:

   npm install

3. Run the frontend:

   npm run dev

Step by Step Helper Files

1. django_steps.md: follow along for setting up the backend
2. frontend.md: follow along for setting up the frontend

Deployment

• 'Infrastructure As A Service'on an AWS EC2 Ubuntu server.
• Apache Web Server.
• Cron jobs automate scheduled tasks like data updates.
• Follow the tutorial series for a step-by-step guide to deploying the application.

Tutorial Series

This project is part of a 10-part tutorial series covering:

• 1: Building a Live Data Streaming Web App - Setting Up the Backend with Python
• 2: Forecasting Football Match Stats with Python - Real-Time Predictions Using Exponential Smoothing
• 3: Setting Up Django for Real-Time Data - Building the Backend and API
• 4: Building the Django Backend - Serving Live Data with APIs and Handling CORS
• 5: Styling and Rendering Live Data with Tailwind CSS and React
• 6: Deploying a Django and Next.js App on AWS - Setting Up Ubuntu for Production
• 8: Pushing to GitHub and Deploying Your Project on an EC2 Instance
• 9: Efficient Path Management and Process Handling with Screens in Python Projects
• 10: Deploying with Apache and Automating Tasks with Cron Jobs for a Production-Ready Python App

Contributing

Pull requests are welcome! If you'd like to contribute, please open an issue first to discuss changes.

License

This project is licensed under the MIT License.

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This README provides a high-level overview. Let me know if you'd like to refine specific sections!