Sports events bring in a lot of people to watch the games. In India Especially Cricket. Cricket and now others sports are contributing to the Indian Economy in a high scale. This paper aims in constructing an application that can extract, process and analyse the data and then give the output to other people who plan on setting up any sports events at a given time. The data is collected from sources that supply with climatic data. We shall further analyse the data using R programming to do a comparison of central tendencies by the fitting efficiency in the analysis on the mean and variance of the data (Temperature data). Choosing the right place to conduct a sports event is the most critical assignments for any game especially for cricket in India. We can't have a situation where cricket fans from around the country travel for a special event only to end up watching a rain soaked ground with their favourite players sitting in the stands. Another point to consider is that the climate though it's not really raining will have an impact on the game. For example, taking a bowler who swings a lot, extra wind can help him in swinging ball to greater lengths, as for a batsman a bright day can help him in connecting the ball perfectly but on a shady day that's reduced. So this paper endeavours to anticipate the most appropriate place to choose for a cricket match or any other outdoor sports events which can be manipulated by the weather conditions. A number of algorithms such as Random forest, Naive Bayes, SVM and decision tree classifier and clustering mechanisms are used to predict the expected results. The final decision was supported by the Random Forest. This is the first attempt on predicting the rain interruption which could affect the cricket match and may impact financial losses both for the organizers and the sponsers. Hence predicting rainfall allows you to design the fixtures positvely without any interruption of rain. © 2019 IEEE.