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README.md

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-license: mit
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+# 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