Data Science & Machine Learning
4-6 Weeks
Web Interface & Python Backend
Predicting highly volatile sports outcomes typically suffers from historical bias and data leakage, failing to accurately weigh current team form and real-time performance metrics.
We developed a robust machine learning pipeline trained on over 45,000 international matches to calculate precise win/draw/loss probabilities for the FIFA World Cup.
Engineered 12 custom features utilizing time-aware processing to strictly prevent data leakage.
Applied dynamic tournament weighting and a symmetric prediction approach to neutralize home-field bias.
Implemented a Calibrated Random Forest Classifier with isotonic regression for high-reliability probability outputs.
Processed and filtered a massive dataset of post-2006 modern football matches.
Achieved ~68% prediction accuracy, vastly outperforming random guessing (33%) and home-win baselines (48%).
Deployed an interactive web application for real-time user predictions.
