🏅 Olympics Data Analysis (1896-2016)

📌 Project Overview

The Olympic Games have been the pinnacle of international sports since 1896. This project explores the historical dataset of the Olympics, uncovering trends, athlete performance, and country-wise participation. Through data cleaning, visualization, and analysis, we gain insights into how the Games have evolved over time.

📂 Dataset Description

The size of athlete_events.csv is more than 20mb so i had provided the link and description in the file name 'Dataset_link.py'

This analysis uses two primary datasets:

1. athlete_events.csv - Contains detailed records of Olympic athletes, including:

   • Name, Age, Gender
   • Sport, Event, Medal (if won)
   • Country (NOC), Year, Season (Summer/Winter)

2. noc_regions.csv - Maps National Olympic Committees (NOCs) to country names, helping in regional analysis.

🚀 Project Objectives

• Perform Exploratory Data Analysis (EDA) to understand athlete trends.
• Visualize country-wise medal counts and athlete participation.
• Analyze gender representation and its evolution in the Olympics.
• Identify the most successful athletes and countries over the years.

🔧 Installation

To run this project on your local m achine:

1. Clone the Repository:

   git clone https://github.com/VIPULbunny/olympics-analysis.git

2. Navigate to the Project Directory:

   cd olympics-analysis

3. Install Dependencies:

   pip install numpy pandas matplotlib seaborn

4. Run the Jupyter Notebook:

   jupyter notebook

📊 Exploratory Data Analysis (EDA)

🏆 Key Insights from Data

• Total Athletes Participated: {total_athletes}
• Total Olympic Games Editions: {total_games}
• Top 10 Countries by Athlete Count:
  • USA, Germany, UK, France, China, etc.
• Most Successful Athletes:
  • Michael Phelps, Usain Bolt, etc.
• Gender Representation Over Time:
  • Increasing female participation in modern Olympics.

🔎 Data Cleaning & Preprocessing

• Merged athlete_events.csv with noc_regions.csv for accurate country mapping.
• Handled missing values in age, medal, and region data.
• Converted categorical features (Sex, Medal) into structured formats for better analysis.

📈 Data Visualizations

🎖️ Medal Distribution

🏅 Top 10 Countries by Medal Count

📊 Gender Representation Over the Years

🌍 Count occurrences of 'Sex' for each 'Season'

📜 License

This project is licensed under the MIT License.

🤝 Contributing

We welcome contributions! If you’d like to improve the analysis or add new insights:

1. Fork the repository.
2. Create a feature branch: git checkout -b feature-branch
3. Commit your changes: git commit -m "Added new analysis"
4. Push to GitHub: git push origin feature-branch
5. Open a Pull Request 🚀

📬 Contact

For queries or collaborations, reach out via email or open an issue on GitHub.

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