This project is a data science exercise focused on analyzing the best defensive moves for Black based on White's openings in chess. Using the High-Elo Games Chess Opening Dataset from Kaggle, I applied machine learning techniques to predict Black's optimal responses. My initial approach leverages a Random Forest model, chosen for its interpretability and efficiency.

Note: This is a learning project, and I welcome any feedback, comments, or suggestions for improvement!

• Name: High-Elo Games Chess Opening Dataset
• Description: A dataset containing chess games played by high-rated players, focusing on the first moves of both White and Black.
• Key Features:
  • move1w, move2w, move3w, move4w: White’s first four moves
  • move1b, move2b, move3b, move4b: Black’s responses
  • opening_name: Name of the chess opening

I chose Random Forest because:
✅ It is easy to interpret and efficient to train.
✅ It handles categorical data well (chess moves).
✅ It reduces overfitting by aggregating multiple decision trees.

• The dataset was preprocessed and split into training (80%) and test (20%) sets.
• A Random Forest Classifier was trained using sklearn.ensemble.RandomForestClassifier.
• I evaluated the model using precision scores for each predicted move.
• Results showed that predictability decreases as moves progress, highlighting the complexity of later moves.

As expected, predictions for the first Black move are more accurate, while later moves become increasingly difficult to predict due to the growing complexity of the game.

🔹 Explore sequence-based models (LSTM/GRU) to capture dependencies between moves.
🔹 Optimize the Random Forest model (hyperparameter tuning, class balancing).
🔹 Test alternative ML models (Gradient Boosting, XGBoost).
🔹 Feature engineering: Incorporate game metadata for better context.

MCKC93

This project is open for contributions! Feel free to fork the repository, submit pull requests, or share your insights via issues.

This project is licensed under the MIT License – feel free to use, modify, and share.

Any feedback or suggestions are highly appreciated! Thanks for checking out this project!