• Priyanka Bhatia
• Sakshi Pandey
• Anubha Roy
• PROF. M. A Rane

With the advent of statistical modeling in sports, predicting the outcome of a game has been established as a fundamental problem. Cricket is one of the most popular team games in the world. We embark on predicting the outcome of a One Day International (ODI) cricket match using a supervised learning approach from a team composition perspective. Our work suggests that the relative team strength between the competing teams forms a distinctive feature for predicting the winner. Modeling the team strength boils down to modeling individual player's batting and bowling performances, forming the basis of our approach. We use career statistics as well as the recent performances of a player to model him. Player independent factors have also been considered to predict the outcome of a match. We will show that the k-Nearest Neighbor (KNN) algorithm yields better results as compared to other classifiers like Naïve Bayes, Support Vector Machine (SVM), etc. The performance is affected by the type, size and quality of the data.

Melanoma; Data Mining, Machine Learning, Kth Nearest neighbor, Naïve Bayes.

"ICC T20 Cricket World Cup Prediction Based Data Analytics and Data Mining Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 5, page no.35-38, May-2020, Available :http://www.jetir.org/papers/JETIR2005307.pdf

"ICC T20 Cricket World Cup Prediction Based Data Analytics and Data Mining Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 5, page no. pp35-38, May-2020, Available at : http://www.jetir.org/papers/JETIR2005307.pdf