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  Published Paper Details:

  Paper Title

REVIEW ON CRICKET ANALYSIS AND PREDICTION USING MACHINE LEARNING APPROACH

  Authors

  Patel Divya Shantilal,  Shete Rutuja Arun,  Diwani Jay Kantilal,  Pawar Supriya Haridas

  Keywords

Machine Learning, Supervised learning, Analytic, Cricket.

  Abstract


Many sports may need a tactical assessment, movement analysis, video, and statistical database and modeling, and coach and player data presentations. Large amounts of data were collected in the world of cricket. The data requires to be analyzed for useful insights. Cricket analytic is an ideal market for acquiring innovative players. The Cricket Analysis and Prediction using Machine learning will make use of different machine learning techniques. Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Predictions like who would be the winner, what would be a score, or which player has the same skills as other players will be made using the Machine learning model. The proposed system will be developed using the Python programming language. Django framework will be used. The Ml learning model will be built and trained using Sklearn and Tensorflow

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2204669

  Paper ID - 218815

  Page Number(s) - f894-f896

  Pubished in - Volume 10 | Issue 4 | April 2022

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Patel Divya Shantilal,  Shete Rutuja Arun,  Diwani Jay Kantilal,  Pawar Supriya Haridas,   "REVIEW ON CRICKET ANALYSIS AND PREDICTION USING MACHINE LEARNING APPROACH", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 4, pp.f894-f896, April 2022, Available at :http://www.ijcrt.org/papers/IJCRT2204669.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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