AIFootballPredictions is an ML-based system to predict if a football match will have over 2.5 goals. Using historical data from top European leagues (Serie A, EPL, Bundesliga, La Liga, Ligue 1), it employs advanced feature engineering and model training techniques to provide accurate predictions. Perfect for sports analytics enthusiasts.

Predicting outcome of the football match in English Premier League (EPL) using machine learning algorithms (Sports Analytics/ Betting)

Using machine learning models like Random forest classifier and SVM to predict football matches played in the English Premier League

Predicting EPL (English Premier League) Match Results

Premier League Match Predicitons

Predicting the outcome of EPL games using machine learning

Predicts win/loss according to the information of two teams

EPL matches from 2018 to 2022, then make prediction with it.

Score prediction for EPL matches and team-specific recommendations to improve gaol probabilities per match

A Data Science project exploring EPL match prediction. Features web scraping, time-series feature engineering, and model training with Pandas and Scikit-learn.

This program predicts the outcomes of English Premier League (EPL) matches using historical data and various statistical features. It processes raw match data, performs feature engineering, and calculates necessary metrics for prediction models.

Deep ensemble EPL match prediction engine — 13 base learners, Dixon-Coles statistical model, 376+ engineered features, 8 data sources, NLP sentiment analysis. 60.2% accuracy, outperforms market odds.

English Premier League Predictions Predicting weekly football results in EPL for the 2017 season. Updated every week. Structure Data : folder with all the relevant data used in the analysis EDA_Prediction : contains a detail exploration of the dataset and tries to answer a specific question. Can we use data to explicitly show that teams actually possess a 'home advantage'? We then go on to see how home advantage is gradually decreasing in the EPL - a hunch that I wished to find out. EPL_Prediction : weekly predictions of the English Premier League's matches EPL_Prediction_Tuning : trying out different approaches in predictions. Under progress. Report : a comprehensive report of the whole process

In this project, we'll predict the winner of football matches in the English Premier League (EPL). Project Steps Scrape match data using requests, BeautifulSoup, and pandas. Clean the data and get it ready for machine learning using pandas. Make predictions about who will win a match using scikit-learn. Measure error and improve our predictions.

A statistical soccer match predictor using the Poisson Distribution and dynamic form analysis to calculate win probabilities and expected goals (xG). Built with Python, Flask, and Chart.js. Great for people who want to see the raw predictions based on the EPL data.

AIFootballPredictions is an ML-based system to predict if a football match will have over 2.5 goals. Using historical data from top European leagues (Serie A, EPL, Bundesliga, La Liga, Ligue 1), it employs advanced feature engineering and model training techniques to provide accurate predictions. Perfect for sports analytics enthusiasts.

EPL match prediction

EPL match prediction project

EPL Match Outcome Prediction model

Predictions of football matches for EPL

Predictions of football matches for EPL

No description available

Predictions for this weeks EPL matches

To predict of the results of EPL 2022 using odds set by various Betting Houses.

EPL soccer match prediction and Player analytics

Football match prediction model for EPL games.

English Premier League (EPL) match score prediction

A data science project using scikit-learn to predict English Premier League match outcomes.

No description available

End-to-end, explainable machine-learning pipeline for Premier League match outcome prediction (Win/Draw/Loss) using knowledge-driven features, calibrated tree models, and Kelly-based risk-aware staking. Includes reproducible code, evaluation, and explainability tools.