🧠 Chess Game Length Predictor
▶️ Project Presentation Video: https://www.loom.com/share/7e7a5a47fec8469f9b457a910fc52c0d
Predicting whether a chess game will be short, medium, or long using machine learning
This repository contains the final trained model from an end-to-end machine learning project analyzing ~20K chess games sourced from Lichess. The goal was to predict the length of a chess game (in number of half-moves) by classifying each game into one of three categories:
- 0 – Short game
- 1 – Medium-length game
- 2 – Long game
The project includes exploratory data analysis, feature engineering, categorical encoding, model training, and exporting the final model as a pickle file.
📘 Project Overview
Chess games vary greatly in structure and duration. Some games end quickly due to opening traps or mistakes, while others develop into long positional struggles.
This model predicts a game’s length category using information available before or shortly after the opening phase, including:
- Player ratings
- Time control increment
- Opening characteristics
- Engineered features describing opening length
- Rating differences and averages
The final exported artifact is a trained Random Forest Classifier.
🔍 Dataset Summary
The dataset contains approximately 19,800 chess games with:
- Game metadata (ID, number of turns, time increment)
- Player ratings (white, black)
- Opening information (short names, ECO-like categories)
- Game outcome and victory status
The target variable (turns_class) was created by binning number of turns into three quantile-based classes.
📊 Exploratory Data Analysis
Key insights included:
• Game length shows patterns across different opening families.
Some openings consistently lead to longer or shorter games.
• Player ratings mildly influence length.
Higher-rated matchups tend to produce longer, higher-quality games.
• Victory status interacts with game duration.
Games ending by resignation or timeout skew shorter.
• Opening names were highly diverse.
Hundreds of unique openings required careful preprocessing and encoding.
🛠️ Feature Engineering
Engineered features include:
✔ Opening Length Group
opening_moves was grouped into four categories: very_short, short, medium, long.
✔ Rating Difference & Average
rating_diff = abs(white_rating - black_rating)rating_avg = (white_rating + black_rating) / 2
✔ Draw Indicator
Boolean feature marking whether a game ended in a draw.
✔ Optional Opening Clusters
KMeans clustering was used to group openings with similar behavior.
✔ One-Hot Encoding
Categorical variables were transformed using one-hot encoding, with filtering to avoid high dimensionality.
🤖 Models Trained
Three models were evaluated:
1️⃣ Logistic Regression
Baseline model. Accuracy: ~46–47%.
2️⃣ Random Forest Classifier
Best-performing model overall. Accuracy: ~47–48%.
3️⃣ Gradient Boosting Classifier
Slightly below Random Forest.
🏆 Winner: Random Forest
Random Forest achieved the strongest performance due to:
- Good generalization
- Stable accuracy across all classes
- Robust handling of high-dimensional categorical features
This repository hosts the exported model:
random_forest_model.pkl
🗂️ Repository Contents
| File | Description |
|---|---|
random_forest_model.pkl |
Final trained model |
README.md |
Project documentation |
.gitattributes |
Managed by HuggingFace |
Copy_of_Assignment_2_Classification,_Regression,_Clustering,_Evaluation.ipynb |
Google Colab |
🧩 Limitations
- Medium-length games show substantial overlap with other classes.
- High variance in opening names increases sparsity.
- Model is limited by information available early in the game.
🙌 Acknowledgments
Thanks to Lichess for providing open game data and to HuggingFace for model hosting.