Estimating Chess Player Strength from Game Data using polynomial regression

This project applies Polynomial Regression to predict a chess player's rating strength based on various game-related features.

The model is trained to estimate the black player's rating using information about the game, the opponent (white), and performance indicators such as ACPL (Average Centipawn Loss).

Although predicting exact chess ratings is challenging due to the noisy nature of chess performance data, the model provides practical insights into estimating the opponent’s skill level.