Prediction of IPL Match Performance Based on Batsman Category Using Machine Learning Algorithm

In the world of business, firms have started adopting technologies to develop and grow their businesses. To sustain in this competitive market there is a dire need for better performance and precision in prediction .To achieve this goal various businesses, require to accurately analyze and predict the patterns for data driven decisions. .This research paper themes around sports as a business and takes into consideration cricket which is a very popular and well-known game that is played and watched in 104 countries. Predicting winner teams and performance of each individual team depends on many factors for example batsman’s approach ,bowler’s skill and tactics ,the pitch statistics, the weather conditions. and many more. The previous research studies show player’s probability of winning against the opponent. Study will find out various attributes to predict a model for IPL match game changer. Score prediction has been in existence from many years based on certain parameters taken into consideration ,but this research study will specifically focus on machine learning algorithms to predict a team’s performance and score based on batsman category, The study will make use of Kaggle data to build a predictive model. The results from conducted the research shows that random forest exuded the best accuracy in terms of score prediction taking into consideration the parameters