🥇 Olympic Gender Analysis & Prediction

This is a Streamlit web application that performs an analysis of the historical Olympic dataset with a focus on gender. The app includes data exploration, interactive visualizations, and a machine learning component to predict an athlete's gender based on their physical and event-related attributes.

The project leverages CatBoost for modeling and Optuna for efficient hyperparameter tuning.

✨ Features

• Interactive UI: A clean, tab-based user interface built with Streamlit.
• Efficient Machine Learning: Uses CatBoost's native handling of categorical features, avoiding inefficient one-hot encoding.
• Hyperparameter Tuning: Integrates Optuna to automatically find the best parameters for the model.
• Stateful App: Uses st.session_state to store model tuning results, preventing re-computation and creating a responsive user experience.
• Rich Visualizations: Interactive plots from Plotly for data exploration and model evaluation (e.g., confusion matrix, feature importance).
• Robust & Modular Code: The codebase is organized into logical modules for clarity and maintainability.

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📂 Project Structure

olympic-gender-analysis/
├── .gitignore          # Files to be ignored by Git
├── README.md           # This file
├── requirements.txt    # Project dependencies
├── app.py              # Main Streamlit application script
├── utils.py            # Utility functions (e.g., CSS styling)
└── data/
    └── (athlete_events.csv goes here)

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🚀 Getting Started

Prerequisites

• Python 3.8+
• Git

Installation & Setup

1. Clone the repository:

   git clone <your-repository-url>
   cd olympic-gender-analysis

2. Create and activate a virtual environment (recommended):

   # For macOS/Linux
   python3 -m venv venv
   source venv/bin/activate
   
   # For Windows
   python -m venv venv
   venv\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

4. Download the dataset: Download the Olympic History dataset from Kaggle. Place the athlete_events.csv file inside the data/ directory.

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⚙️ How to Run

From the root directory of the project (olympic-gender-analysis/), run the following command in your terminal:

streamlit run app.py

Your web browser should automatically open with the running application.

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🖼️ App Preview

Navigate the app using the tabs:

1. Welcome & Data Overview: Get an introduction and view the raw data.
2. Model Hyperparameter Tuning: Start the Optuna optimization process to train the gender prediction model.
3. Results & Analysis: Once tuning is complete, explore the model's performance, feature importance, and interactive charts on gold medal distribution.

(You can add a screenshot of your app here after uploading it to your GitHub repo)