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ZeyadKhalil
Sports Analytics · Predictive Modeling · Full Stack

EPL Predict: Real-Time Premier League Match Predictions

A full-stack sports analytics application that combines historical football data, live API inputs, database-backed feature storage and a predictive model for English Premier League fixtures.

Stack
Python · Flask · MySQL · JS
Theme
Sports analytics · ML
Frontend
AJAX-driven UI
Status
Archived

Overview

EPL Predict combines historical match data and live APIs with a statistical and machine learning predictor to produce forecasts for English Premier League fixtures. The frontend is a lightweight AJAX-driven interface, and the backend is a Flask service backed by MySQL.

Architecture

  1. Step 01

    Live + historical APIs

    Match data ingest

  2. Step 02

    Flask service

    Python backend

  3. Step 03

    MySQL

    Persisted match + team stats

  4. Step 04

    Feature engineering

    Form, h2h, recency

  5. Step 05

    Predictor

    Statistical / ML model

  6. Step 06

    API

    JSON endpoints

  7. Step 07

    AJAX frontend

    JS · HTML · CSS

What it shows

  • End-to-end full-stack work from data ingestion to user-visible UI.
  • Practical ML / statistical modelling on live, messy data.
  • Database design, REST endpoints and a dynamic frontend.

Reflections

EPL Predict is an earlier project, but it remains useful evidence of taking a problem from raw data through feature engineering, modelling, API design and a working web interface. It sits alongside the AI and data projects as full-stack software evidence.