<![CDATA[This paper presents the design and implementation of a Machine Learning (ML)-based chess game developed in Python. Unlike traditional chess game that rely primarily on alpha-beta pruning and handcrafted evaluation functions, this approach employs supervised learning techniques to create a neural network model capable of evaluating chess positions and making move decisions. The system leverages the python-chess library for game representation and the scikit-learn framework for implementing the machine learning components. We demonstrate that even with relatively simple feature engineering and a modest neural network architecture, the system can learn effective chess strategies. The implementation is designed to run in a Jupyter Notebook environment, providing an interactive interface for human players to compete against the ML agent while facilitating educational insights into both chess strategy and machine learning principles.]]>
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