{"repo":"mhaythornthwaite/Football_Prediction_Project","free":true,"listed":false,"github":"https://github.com/mhaythornthwaite/Football_Prediction_Project","clone":"git clone https://github.com/mhaythornthwaite/Football_Prediction_Project.git","description":"This project pulls past game data from api-football, and uses this to predict the outcome of future premier league matches with the use of classical machine learning techniques.","language":"Python","stars":305,"topics":["football-prediction","machine-learning","scikit-learn","python","flask","football","premier-league","data-science","data-visualization","prediction"],"license":"MIT","category":"machine-learning","readme_excerpt":"Live predictions were displayed in a webapp from 2020 and 2024 but this feature is no longer supported. Table of Contents Aims and Objectives Dataset Data Cleaning and Preparation Feature Engineering and Data Visualisation Model Selection and Training Evaluation Further Work and Improvements Aims and Objectives The aim of this study was to build a model that could accurately predict the outcome of future premier league football matches. Success was judged using the following two objectives, one quantitative and one qualitative: - Achieve a test accuracy of greater than 50%, with a stretch target of 60%. - Output probabilities that appear sensible/realistic, that are comparable to odds offered on popular betting websites. Dataset The data was collected directly from an API: api-football . This was preferred over a static database that can be readily found online, due to the following: - API calls can be made daily, refreshing the database with the most recent statistics and results, allowing the model to consistently be retrained on up-to-date information. - The API not only provides past game data but also information on upcoming games, essential to make predictions which feed into the web application. Data Cleaning and Preparation Data was initially collected from the 2019-2020 premier league season, in the form of a single json file per fixture containing a range of stats (e.g. number of shots, possession etc.) These json files were loaded into a Pandas DataFrame, and organ","default_branch":null,"files":null,"tree":[],"storefront":"/r/mhaythornthwaite","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/mhaythornthwaite/Football_Prediction_Project/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}