The world’s most popular game, football piques the interest of billions across the globe and so, the prediction of football matches intrigues everyone from the fans to the managers themselves. The prediction of matches is not a simple task, as the game is inherently chaotic. In this paper, we have tested out various machine learning models, trained on historical match data of both sides, considering their attacking, midfield, and defensive abilities, along with data on the player attributes of both sides to predict the outcomes of matches. We have chosen the most relevant features from our dataset after processing it, and used them to train some base classifier models. Using the trained base models, we have built a modified heterogeneous ensemble classifier, with the aim of maximally utilizing the strengths of its constituent models. We have seen that our model has shown an improvement over some of the base line work in this domain.