Gaming Prediction Analysis Using Hybrid Chi Square - Support Vector Machine Model and Historical Data

Shruti Chopra¹ , Vijay Kumar Joshi²

1. Department of Computer Science, Ludhiana College of Engineering and Technology, Ludhiana, India.
2. Department of Computer Science, Ludhiana College of Engineering and Technology, Ludhiana, India.

Correspondence should be addressed to: shruti.91chopra@gmail.com.

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.5 , pp.1-5, Oct-2017

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.15

Online published on Oct 30, 2017

Copyright © Shruti Chopra, Vijay Kumar Joshi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Data Science is a powerful domain that helps in extracting information from a given dataset for giving useful information. Prediction Analysis is also a part of Data Science which uses scientific techniques for determining the prediction results with a given set of input data. Gaming Prediction is an exciting field and lot of researches are being performed on this to determine factors of improvement in terms of player performance, management of matches and imparting a good understanding to Guides, Sponsors and the Players. They also aim the high accuracy of results and have proved fruitful but still includes lot of more advancements in the techniques utilized. This paper has utilized the benefit of advanced algorithm which does a primary feature selection in Basketball Matches which is followed by developing fuzzy rules to check the impact of these features of final result. Finally, Support Vector Machine Technique is employed to determine the Accuracy of Prediction Logic obtained.

Key-Words / Index Term :
Fuzzy Logic, Prediction Analysis, Support Vector Machine, National Basket Ball Association, Chi-Square

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