Olympic Swimming Prediction Model

A machine learning system that predicts which Olympic swimming event a swimmer has the best chance of competing in based on their performance data, biometrics, and demographic information.

🏊‍♂️ Project Overview

This project uses a Random Forest classifier to analyze swimmer data and predict their optimal Olympic swimming event. The system considers various factors including:

• Biometric Data: Height, weight, BMI, age, gender
• Performance Data: Best times across different strokes and distances at various ages
• Demographic Information: Ethnicity and race
• Course Types: Short Course Yards (SCY), Short Course Meters (SCM), Long Course Meters (LCM)

📁 Project Structure

├── train_swimming_predictor.py      # Main training script
├── predictionScript.py              # Interactive prediction interface
├── dataEngineering.py               # Data preprocessing and feature engineering
├── youthTimesScraper.py             # Web scraper for swimmer performance data
├── nameListScraper.py               # Scraper for Olympic swimmer names
├── staticdata.py                    # Static data and constants
├── final_swimmer_data.csv           # Processed training dataset
├── best_swimming_predictor_model.pkl # Trained model file
├── best_model_parameters.txt        # Model hyperparameters and performance metrics
├── iterative_imputer.pkl            # Saved imputer for handling missing data
├── X_train_imputed.csv              # Training data after imputation
├── X_test_imputed.csv               # Test data after imputation
└── olympic_swimmers_youth_times.json # Scraped performance data

🚀 Features

Data Collection & Processing

• Web Scraping: Automated collection of Olympic swimmer performance data from SwimCloud
• Data Engineering: Comprehensive preprocessing pipeline for swimmer data
• Missing Data Handling: Advanced imputation techniques for incomplete performance records
• Feature Engineering: Age-specific performance metrics and biometric calculations

Machine Learning

• Random Forest Classifier: Robust prediction model with hyperparameter optimization
• Multi-class Classification: Predicts across 12 Olympic swimming events
• Balanced Performance: Handles imbalanced dataset with class weighting
• Cross-validation: Comprehensive model evaluation

Interactive Prediction

• User-friendly Interface: Interactive command-line tool for predictions
• Flexible Input: Accepts various course types and performance data
• Real-time Analysis: Instant predictions with confidence scores

🛠️ Installation & Setup

Prerequisites

• Python 3.8+
• pip package manager

Dependencies

Install the required packages:

pip install pandas numpy scikit-learn joblib playwright beautifulsoup4 asyncio

Environment Setup

1. Clone the repository:

git clone <repository-url>
cd Olympic-Swimming-Prediction-Model

2. Install Playwright browsers (for web scraping):

playwright install

3. Set up environment variables (optional):

cp secrets.env.example secrets.env
# Edit secrets.env with your configuration

📊 Data Pipeline

1. Data Collection

• Name Scraping: Extract Olympic swimmer names from official sources
• Performance Scraping: Collect youth and current performance times from SwimCloud
• Biometric Data: Gather physical characteristics and demographic information

2. Data Processing

• Cleaning: Remove duplicates, handle missing values, standardize formats
• Feature Engineering: Create age-specific performance metrics
• Imputation: Fill missing performance data using iterative imputation
• Encoding: Convert categorical variables to numerical format

3. Model Training

• Feature Selection: Identify most predictive variables
• Hyperparameter Tuning: Optimize Random Forest parameters using RandomizedSearchCV
• Cross-validation: Ensure robust model performance
• Model Persistence: Save trained model and preprocessing components

🎯 Usage

Training the Model

python train_swimming_predictor.py

This script will:

• Load and preprocess the training data
• Perform hyperparameter optimization
• Train the final model
• Save the model and performance metrics

Making Predictions

python predictionScript.py

The interactive interface will prompt you for:

1. Basic Information: Height, weight, age, gender, ethnicity
2. Performance Data: Best times in various strokes and distances
3. Course Type: SCY, SCM, or LCM

Example Prediction Session

=== SWIMMING OLYMPIC EVENT PREDICTOR ===
Let's find out which Olympic swimming event you have the best chance in!

Enter your height in cm: 180
Enter your weight in kg: 75
Enter your race/ethnicity: White
Enter your gender (M/F): M
Enter your age: 18

=== COURSE TYPE ===
What course type are your swim times from?
1. SCY (Short Course Yards - 25 yards)
2. SCM (Short Course Meters - 25 meters)
3. LCM (Long Course Meters - 50 meters)

Enter your choice (1/2/3): 1

=== PERFORMANCE DATA ===
Enter your best 50m Freestyle time (seconds): 22.5
...

🎯 PREDICTION RESULTS:
Best Olympic Event: 100m_Freestyle
Confidence: 85.2%

Model Parameters

• Algorithm: Random Forest Classifier
• Estimators: 200 trees
• Max Depth: 40
• Min Samples Split: 10
• Min Samples Leaf: 2
• Max Features: sqrt
• Class Weight: balanced
• Bootstrap: False

🔧 Customization

Adding New Features

1. Modify dataEngineering.py to include new feature calculations
2. Update the training script to incorporate new variables
3. Retrain the model with the enhanced feature set

Modifying Prediction Events

1. Edit the target event list in train_swimming_predictor.py
2. Update the prediction interface in predictionScript.py
3. Retrain the model with the new event categories

Data Source Integration

1. Extend youthTimesScraper.py for new data sources
2. Modify data parsing functions in dataEngineering.py
3. Update the data pipeline to handle new formats

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• SwimCloud: Data source for swimmer performance records
• Olympic Committee: Official swimmer information
• Scikit-learn: Machine learning framework
• Playwright: Web scraping automation

📞 Support

For questions, issues, or contributions, please:

1. Check existing issues in the repository
2. Create a new issue with detailed description
3. Contact the development team

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Note: This model is for educational and research purposes. Predictions should not be used as the sole basis for training decisions or Olympic qualification strategies.