Abstract
The main objective of this paper is to implement the classifications algorithms in Neo4j graph database using cypher query language. For implementing the classification algorithm, we have used Indian Premier League (IPL) dataset to predict the winner of the matches using some different features. The IPL is the most popular T20 cricket league in the world. The prediction models are based on the city where the matches were played, winner of the toss and decision of the toss. In this paper we have implemented Naïve Bayes and K-Nearest Neighbors (KNN) classification algorithms using cypher query language. Different classifiers are used to predict the outcome of different games like football, volleyball, cricket etc, using python and R. In this paper we shall use cypher query language. We shall also compare and analysis the results which are given by Naïve Bayes and K-Nearest Neighbors algorithms to predict the winner of the matches.
References
Gabriele Pozzani, “Introduction to NoSQL “profs.sci.univr.it/~pozzani/attachments/nosql%20 %2001%20-%20introduction.pdf, April 24, 2013.
R. Angles and C. Gutierrez,” Survey of Database Models”, ACM Comput. Surv .40(1):1–39, 2008.
www.neo4j.com
Shilpi Agrawala, Suraj Pal Singh, Jayash Sharma, “Predicting Results of Indian Premier League T-20 Matches using Machine Learning”, 2018 8th International Conference on Communication Systems and Network Technologies (CSNT).
Gagana and K Paramesha, “ A Perspective on Analyzing IPL Match Results using Machine Learning”, International Journal for Scientific Research & Development| Vol. 7, Issue 03, 2019.
Prince Kansal, Pankaj Kumar, Himanshu Arya and Aditya Methaila, “Player Valuation in Indian Premier League Auction using Data Mining Technique”, IEEE, 2014.
Sasank Viswanadha, Kaustubh Sivalenka, Madan Gopal Jhawar, and Vikram Pudi, “Dynamic winner prediction in Twenty20 Cricket: Based on Relative Team Strength” DSAC, Kohli Centre on Intelligent Systems, IIIT Hyderabad, India.
Chellapilla Deep Prakash, C. Patvardhan,C. Vasantha,” Data Analytics based Deep Mayo Predictor for IPL-9”. International Journal of Computer Applications (0975 – 8887) Volume 152 – No.6, October 2016.
Amal Kaluarachchi, Aparna S. Varde, CricAI: A classification based tool to predict the outcome in ODI cricket, Conference: IEEE Information and Automation for Sustainability (ICIAFs),2010.
Madan Gopal Jhawar, Vikram Pudi, Predicting the Outcome of ODI Cricket Matches: A Team Composition Based Approach, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2016).
Harshit Barot, Arya Kothari, Pramod Bide, Bhavya Ahir, Romit Kankaria, Analysis and Prediction for the Indian Premier League, 2020 International Conference for Emerging Technology (INCET)
Belgaum, India. Jun 5-7, 2020
Stylianos Kampakis, William Thomas, “Using Machine Learning to Predict the Outcome of English
County twenty over Cricket Matches”, Proceedings of the 5th International Conference on Mathematics in Sport Loughborough University, U.K. 29 June – 1 July 2015
www.kaggle.com
Downloads
Download data is not yet available.
