Applying Machine Learning- Supervised Learning Techniques for Tennis Players Dataset Analysis

Abstract ATP Tennis stands for the “The Association of Tennis Professionals” which is the primary governing body for male tennis players. ATP was formed in Sep 1972 for professional tennis players. A study has been done on tennis players’ datasets to implement supervised machine learning techniques to illustrate match data and make predictions. An appropriate dataset has been chosen, data cleaning has been implemented to extract anomalies, data is visualized via plotting methods in R language and supervised machine learning models applied. The main models applied are linear regression and decision tree. Results and predictions have been extracted from the applied models. In the linear regression model, the correlation is calculated to find the relation between dependent and independent variables, furthermore the results and prediction are extracted from the linear regression model. Also, three hypotheses are applied for multiple linear regression model. The decision tree modeled the best of 3 or best of 5 sets of matches and predicted which set of matches would be considered best. Keywords: Machine Learning, supervised learning, linear regression, decision tree, R language, Tennis, ATP.