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7th Sem Project

The project presents an Intelligent Chess Coaching System that integrates web development and machine learning to enhance chess skills. Utilizing a Logistic Regression model, it analyzes player moves and provides feedback on their performance, classifying moves into skill categories. The system aims to improve players' understanding of chess strategies through personalized guidance and a user-friendly interface.

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12 views2 pages

7th Sem Project

The project presents an Intelligent Chess Coaching System that integrates web development and machine learning to enhance chess skills. Utilizing a Logistic Regression model, it analyzes player moves and provides feedback on their performance, classifying moves into skill categories. The system aims to improve players' understanding of chess strategies through personalized guidance and a user-friendly interface.

Uploaded by

shivaramegowda879
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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Intelligent Chess Coaching System with Personalized

Guidance for Skill Development


Y Praveen Kumar, Anjali S, Vedavathi,
Student’s Name Tejas S
Academic
Dr. NAYANA B R
Supervisor(s)
Industrial
-
Supervisor(s)
Keywords: Web development, Wrong move prediction model evaluation and analysis,
Performance analysis, User interface
Abstract.

"Chess Coaching System for Skill Development," our project, combines web
development and machine learning to improve the chess-playing experience. The
system features a web- based chess game in which players make moves that are
analyzed using machine learning algorithms. The system's core is in delivering
instruction to players for incorrect moves, with a Logistic Regression model used for
move evaluation. The system makes use of a dataset that contains move information,
such as player moves, locations, and game outcomes. This data is used to train the
Logistic Regression model, which allows sit to classify moves into distinct skill
categories ranging from "Very Not Good" to "Very Good." The model analyses each
move based on predetermined criteria such as checkmate, material gain, and king
safety. The system's coaching insights provide players with useful feedback on their
plays, assisting them in understanding the strategic complexities of chess. The web
development portion provides a simple and easy-to-use interface for players to
interact with the game and receive coaching feedback. Our project's revolutionary
method intends to contribute to chess skill development by combining traditional
games with cutting-edge machine learning techniques. The use of Logistic Regression
for move evaluation demonstrates machine learning's versatility in analyzing strategic

decision- making in games


Conclusion: This project leverages data preprocessing, feature engineering, and
machine learning to predict incorrect moves and analyze chess games, enhancing
players' comprehension and skills. Implementing a Flask-based interface, it offers
personalized move guidance. Future improvements could include more complex
models, larger datasets, and additional features to enhance accuracy and analytical
depth. The project lays a solid foundation for ongoing refinement and evolution.

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