Olympics Data Analysis

This project is designed to analyze historical Olympics data and generate insightful visualizations using Python. It utilizes libraries like Pandas and Plotly to explore trends, such as country-wise performance, athlete achievements, and medal distribution across different sports and years. The project is built using a Streamlit web app for interactivity and ease of use.

Sidebar option selection

Features

• Country-wise Performance: Analyze how different countries have performed across various editions of the Olympics.
• Medal Distribution: Visualize the distribution of medals across sports and years.
• Athlete Analysis: Explore individual athlete performance and achievements.
• Interactive Visualizations: Use Plotly to create interactive and dynamic charts.
• Easy to Use Web Interface: Powered by Streamlit for a smooth user experience.

Technologies Used

• Python: Core programming language.
• Pandas: Data manipulation and analysis library.
• Plotly: Used for creating interactive visualizations.
• Streamlit: For building and deploying the web application.

Installation

Prerequisites

Before running the project, ensure you have the following installed on your system:

• Python 3.x
• pip (Python package manager)
• A virtual environment tool (optional but recommended)

Steps to Install

1. Clone the Repository

   git clone https://github.com/abhaysingh71/olympics-data-analysis.git
   cd olympics-data-analysis
   

2. Stage your changes and commit:

   # Add changes to index
   git add .
   
   # Commit to the local repository
   git commit -m "<your_commit_message>"
   
    

3. Push your local commits to the remote repository:

   git push -u origin <your_branch_name>
   
   

Create a Pull Request (PR)!

Congratulations! Sit back and relax, you've made a contribution to WhatsApp Chat Analyzer.

NOTE: Feel free to open issues if you encounter any problems.

Run on Local System

To run the application on your local machine:

Install required packages Ensure you are using Python 3.11.0 or higher:

pip install -r requirements.txt

4. Run the Streamlit App:

   streamlit run app.py

🌐 Deployed Version

The app is deployed on Streamlit! You can check out the live version and explore the analysis on your own:Streamlit App.

Usage

Once the app is running, you can explore the following sections:

• Country Analysis: Select a country to see its performance over different Olympic editions.
• Medal Tally: View a chart of medals won by countries in a specific year.
• Athlete Achievements: Filter athletes by sport, year, and country to analyze their achievements.
• Medal Distribution: Select a sport and year to see how medals were distributed.

Data Source

The Olympics data used in this project is sourced from Kaggle. You can download the dataset from Olympics Dataset.

🤝 Contributing

Contributions are welcome! If you'd like to improve this project, feel free to fork the repository and submit a pull request. Please make sure to follow the coding standards and include proper documentation.

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

🙏 Acknowledgments

• The dataset is sourced from Kaggle.
• Special thanks to the contributors of Plotly and Streamlit for their wonderful libraries that power the visualizations and interface.