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Predict the winning probability of white player in a chess game on the basis of first move of white player and first move of black player. In the dataset all the set of moves are given but I choose to predict the white winner the first move

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white-player auc-score scikit-learn knn-classifier catboost-model decision-tree-classifier random-forest-classifier svm-classifier logistic-regression xgboost-classifier

chess-winner-prediction's Introduction

Chess-winner-prediction

Objective

Predict the white winner in a chess game on the basis of first move of white player and response of black player. In the dataset all the set of moves are given but I choose to predict the white winner the first move.

Data

The dataset is collected from Kaggle https://www.kaggle.com/datasnaek/chess

EDA

In the github the analysis file contains the exploratory analysis and some visualization to understand the data visually.

Models

I have used 10 supervised machine learning models to evaluate the prediction and metric AUC score. Among all the models CatBoost model perform better with 78% of AUC score

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