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Cricket is one of the famous outdoor sports that contain a large set of statistical data in real world. As IPL games rise in popularity, it is necessary to examine the possible predictors that affect the outcome of the matches. In this paper, the several years' data of IPL containing the players details, match venue details, teams, ball to ball details, is taken and analyzed to draw various conclusions which help in the improvement of a player's performance. It focuses on measuring the outcome of Indian Premier League (IPL) matches by applying the existing data mining algorithms to the balanced as well as imbalanced dataset. This model is very much popular in predictive modelling. Currently, in Twenty-Twenty (T20) cricket matches first innings score is predicted on the basis of current run-rate which can be calculated as the amount of runs scored per the number of over's bowled. It includes factors like number of wickets fallen, venue of the match, toss and predicts the score in each of the innings and finally the winner of the match using Random Forest algorithm. In this paper, Prediction of IPL2020 are done on the basis of survey, and analysis are done based on data mining algorithms.

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