A Deep Learning Framework for Football Match Prediction

Football is one of the most far reaching and famous game, along these lines anticipating the consequences of a football match represents a fascinating test. Forecast can likewise help administrators and clubs in settling on the best choices to win associations and competitions. In view of the huge financial stakes engaged with wagering, there is an interest for high prescient exactness in this industry. Rather than utilizing the customary strategies dependent on measurable examination and AI methods, this exploration paper centers around profound learning procedures that assistance in foreseeing the outcome with a high precision. Especially, LSTM model of Recurrent Neural Network is utilized in our exploration. LSTMs are a slightly modified version of RNNs that use multiplications and adds to make tiny changes to the data. Information travels through a mechanism known as cell states in LSTMs. LSTMs can selectively recall or forget things in this way. Accordingly, in this exploration paper, the result of a football coordinate is anticipated with the assistance of LSTMs and attempted to change it for better outcomes.