| # Tennis Match Win Streak Analysis | |
| This project aims to analyze the most challenging path to a tennis historical winning-streak using machine learning and deep learning techniques. The model incorporates advanced feature engineering, data preprocessing, and a combination of neural networks and ensemble models to achieve accurate analysis of the level of competition. | |
| ## Table of Contents | |
| - [Features](#features) | |
| - [Installation](#installation) | |
| - [Usage](#usage) | |
| - [Data](#data) | |
| - [Model Architecture](#model-architecture) | |
| - [Hyperparameter Tuning](#hyperparameter-tuning) | |
| - [Results](#results) | |
| - [Contributing](#contributing) | |
| - [License](#license) | |
| ## Features | |
| - Advanced feature engineering, including rank and Elo rating differences. | |
| - Data preprocessing with error handling and missing value management. | |
| - PyTorch Lightning framework for building and training neural networks. | |
| - Hyperparameter optimization using Optuna. | |
| - Ensemble methods for improved accuracy. | |
| - Winning streak analysis using clustering techniques. | |
| ## Installation | |
| To get started with this project, you need to have Python 3.x installed. You can then install the required packages using pip: | |
| pip install -r requirements.txt | |
| ## Usage | |
| Place your match data CSV files (named PlayerMatches2.csv to PlayerMatches15.csv) in the project directory. | |
| Run the script to load the data, preprocess it, and train the models: | |
| python main.py | |
| The script will save the best model and configuration for later analysis. | |
| ## Data | |
| The project uses historical player match data in CSV format. Each file should contain the following columns: | |
| date | |
| tournament | |
| winner_name | |
| winner_rank | |
| winner_eloRating | |
| loser_name | |
| loser_rank | |
| loser_eloRating | |
| Optional columns for enhanced feature engineering can also be included. | |
| ## Model Architecture | |
| The project utilizes a custom neural network with: | |
| Categorical embeddings for player names and other categorical features. | |
| Fully connected layers to process both embedded and numerical input features. | |
| Dropout layers for regularization. | |
| Hyperparameter Tuning | |
| Hyperparameter optimization is performed using Optuna, allowing for fine-tuning of: | |
| Embedding dimensions | |
| Hidden layer sizes | |
| Learning rates | |
| Dropout rates | |
| Batch sizes | |
| ## Results | |
| The model's performance is evaluated using mean squared error (MSE) on the validation set. Ensemble models are also trained and compared for additional insights. | |
| ## Contributing | |
| Contributions are welcome! If you have suggestions for improvements or new features, feel free to submit a pull request or open an issue. | |
| ## License | |
| This project is licensed under the MIT License. See the LICENSE file for more information | |