Durhack2023 - Data Pipeline for Premier League Predictor - Table 7

Marshall Wace Task: Data Pipeline for Predicting Premier League Game Scores

Welcome to our predictive analytics project developed during Durhack2023 by Team 7. Our system employs a data pipeline coupled with a machine learning model to predict the outcomes of Premier League football matches with high accuracy.

Team Members:

Mateusz Radzikowski
Vidadi Nasibov
Harshvardhan Patil
Mohammad Reza Sharifi

Project Overview

Our solution is built with a microservices architecture that includes the following components:

Message Broker (RabbitMQ): Manages the message queue for asynchronous task processing, coordinating the flow between publishers and consumers.
Backend Service (FastAPI): Offers a high-performance API, encapsulating the logic for interfacing with the machine learning model and database.
Data Publishers and Consumers (Python): Scripts designed to collect, preprocess, and manage the flow of data to and from the database, using RabbitMQ for task queuing.
Database (PostgreSQL): A central repository for storing processed data that powers the backend predictions.
Machine Learning Model (Random Forest Regressor with Python): The predictive engine, hosted within the FastAPI backend, predicts the scores based on the incoming data.
Frontend Application (React): A user-friendly interface that displays upcoming fixtures and historical data along with the predicted scores, allowing users to evaluate the model's performance.

Prerequisites

Before starting, make sure you have the following installed:

Docker and Docker Compose.
Git (to clone the repository).
Ensure that local ports 3000 for frontend and 8080 backend are free.
The local port for data injector (publisher) - 8082
The local port for data processor (consumer) - 8081
The local port for database PostgreSQL - 5432

Installation and Setup

To get the project up and running: