Analytics and Machine Learning Approaches to Generate Insights for Different Sports
S.S. Subashka Ramesh1, Nadeem Hassan2, Anushka Khandelwal3, Ritwiz Kaustoob4, Sonal Gupta5
1Dr. S. S. Subashka Ramesh, Assistant professor (O.G), Department of Computer Science & Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai -89 (T.N), India
2Mr. Nadeem Hassan Department of Computer Science & Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai -89 (T.N), India
3Ms. Anushka Khandelwal Department of Computer Science & Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai -89 (T.N), India
4Mr. Ritwiz Kaustoob Department of Computer Science & Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai -89 (T.N), India
5Ms. Sonal Gupta Department of Computer Science & Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai -89 (T.N), India
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1612-1617 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2326037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Machine Learning and Data Analytics are used in many sectors so that it can help them to improve their services and find out the future predictions as well by using the previous data. One such sector that has been increasingly using this technology is sports. Many machine learning algorithms are available for sports prediction so that one can determine the team’s strength, weakness and predict the future outcome of the game. But these predictions are not always accurate. So the objective of this project is to implement common machine learning and analytics approach so that it can be used to predict the future outcomes of different games such as football, basketball and also generate insights for the same. Instead of using only one algorithm on the dataset of the scores from the previous matches, a series of an algorithm will be applied so that it can compare the final result from each algorithm and provide us with the most accurate result. Algorithms used will be the SVM model, NNR model, Random Forest Algorithm and ANN model. By generating the insights it is possible to not only determine the winner but also the position of the individual players on the field based on their respective performances. This project will thus predict the outcome of the games to a great extent which will help the teams to improve and turn their weaknesses into strength
Keywords: Machine Learning, Data Analytics, SVM model, NNR model, ANN model, Random Forest Algorithm
Scope of the Article: Machine Design