The Football Match Prediction project is a data-driven analytics platform that uses historical match data, player statistics, and machine learning algorithms to predict the outcomes of football matches with high accuracy. Designed for sports analysts, betting enthusiasts, and football fans, it provides real-time predictions, insights, and trend analysis to support informed decision-making.
Key Features:
- Data Collection & Preprocessing: Aggregates match results, team form, head-to-head stats, and player performance.
- Machine Learning Models: Implements algorithms such as Logistic Regression, Random Forest, and Gradient Boosting for prediction accuracy.
- Probability Scores: Displays win/draw/loss probabilities for each match.
- Live Match Updates: Integrates APIs for real-time team news, injuries, and lineup changes.
- Analytics Dashboard: Interactive charts showing trends, predictions vs. actual results, and model performance.
- Responsive UI: Mobile-friendly design for easy access on any device.
Tech Stack:
- Backend: Python (Flask/Django) for data processing and API integration
- Frontend: HTML5, CSS3, JavaScript (Bootstrap / React.js)
- Database: PostgreSQL / MySQL for storing historical and live data
- Data Sources: Sports APIs for live match stats and historical datasets
- Machine Learning: Scikit-learn, Pandas, NumPy, Matplotlib/Seaborn for model training and visualization
Outcome & Impact:
This system demonstrates the power of predictive analytics in sports, showcasing skills in data science, API integration, and full-stack development while offering a practical tool for sports fans and analysts.
