UTOMI AMAKA MICHELLE

Football appears to be the most popular sports the world over, making it a game of betting for money making among other thing. This business of betting, over the years has gown making it a difficult and complex task in predicting correctly the outcome of football matches. This is as a result of the numerous number of factors that are considered but cannot be quantitatively valued or modeled. The aim of the project is to develop a machine learning algorithms for the prediction of football matches. The classification algorithms adopted in this project includes: K-Nearest Neighbor (KNN), support vector machines (SVM), Gaussian naïve Bayes (GNB), decision tree (DT) and Logistic Regression (LR) techniques. The dataset used was gathered from football- data-co.uk. The models was built using python programming language environment. The comparative analysis carried out in this project support that machine learning algorithms perform well and shows room for future improvement.