Chess Player Profiling : A Game changing perspective on player development
Akanksha Funde, Yash Khandebharad, Prajwal Zade, Harsh Gharate
machine learning, chess game, big data, data analysis, Django, database management.
In the age of big data, significant quantities of different data can be fluently collected or generated at a rapid-fire pace. This data holds precious information that necessitates the use of machine literacy ways for advanced knowledge discovery. Game data, including data from sports games, card games, online videotape games, and chess games, serves as a precious source of big data. Chess, known for its profound commerce and straightforward representation, ranks among the most considerably delved games encyclopedically. multitudinous studies have been conducted in the once exercising chess data. These studies aimed to delve into the vast amount of chess data and employ machine learning models to classify it into various groups. Upon conducting our own exploration, we made an interesting discovery- a significant number of websites warrant comprehensive analysis for player development and are noticeably deficient in terms of coffers. In this paper, to address this issue, we propose the development of a dynamic dashboard with an expansive range of analysis and perceptivity, all grounded on the data they give. Our approach involves the perpetration of unsupervised literacy ways to construct a model able of grading players according to their scores. latterly, we will be suitable to offer acclimatized coffers and support to players grounded on their separate orders, with the ultimate end of fostering and enhancing their growth within the realm of chess.
Article Details
Unique Paper ID: 162192

Publication Volume & Issue: Volume 10, Issue 8

Page(s): 265 - 271
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