Olympic Data Analysis 🏅📊

Welcome to the "Olympic Data Analysis" project! This data analysis project offers a comprehensive analysis of Olympic data, providing insights into medal tallies, participating nations, athletes, and more. The project is deployed on the Render cloud platform for easy access. 🎉

About This Project 📚

The "Olympic Data Analysis" project is a detailed exploration of Olympic data. It covers a wide range of aspects related to Olympic history, including the overall tally of medals with the option to select specific years or countries. The project also provides an overall analysis section with the following key features:

Top Statistics: Explore the most significant statistics related to Olympic history. 🥇🥈🥉
Participating Nations Over The Years: Visualize the growth in the number of participating nations over time. 🌍
Number of Events Over The Years: Understand the evolution of Olympic events. 🏋️‍♂️
Athletes Over The Years: Analyze the number of athletes who have participated in the Olympics over time. 🏃‍♀️🏃‍♂️
Heat Map of Number of Events Overtime for Sports: Discover how the number of events has changed over time for different sports. 🔥
Most Successful Players with Sport Filtering: Identify the most successful Olympic athletes and filter them by sport. 🏆

In addition to the overall analysis, the project offers Country-Wise Analysis, including:

Medal Analysis of Countries: Examine medal counts for individual countries. 🌐
Country-Sport Heatmap: Visualize the relationship between countries and the sports they excel in. 🌟
Most Successful Players by Country: Discover the most successful Olympic athletes from each country. 🌠

Lastly, the Athlete-Wise Analysis section includes:

Distribution of Age: Explore the age distribution of Olympic athletes. 🎂
Distribution of Age with Respect to Sport for Gold, Silver, and Bronze Medalists: Analyze how age varies for medalists in different sports. 🏅
Scatterplot of Sports: View scatterplots of different sports. 📈
Men vs. Women Participation: Compare the participation of men and women in the Olympics. 👫

Explore the Project 🚀

Technologies Used 🛠️

This project leverages the following technologies:

Python: The primary programming language for data analysis and visualization. 🐍
Pandas: A powerful data manipulation and analysis library. 🐼
Seaborn: A data visualization library built on Matplotlib. 📊
Matplotlib: A popular data plotting library for creating interactive visualizations. 📈

Installation ⚙️

To run this project locally, follow these steps:

Clone the repository to your local machine:

git clone https://github.com/rajatrawal/olympic-data-analysis.git

Navigate to the project directory:
```
cd olympic-data-analysis
```
Install the required Python libraries:
```
pip install -r requirements.txt
```
Run the project using Streamlit:
```
streamlit run olympic_data_analysis.py
```
Open your web browser and explore the project locally. 🌐

Usage 🤝

This project is designed for data enthusiasts, sports enthusiasts, historians, and anyone interested in exploring the rich history of the Olympics. It provides a user-friendly interface to interact with the data, create visualizations, and uncover valuable insights. 📈

Contribute 🤗

If you'd like to contribute to this project, have suggestions for improvement, or wish to add more features to the analysis, please feel free to submit issues or pull requests on GitHub. We welcome your input! 🚀

Thank you for exploring the "Olympic Data Analysis" project! We hope it enhances your understanding of Olympic history and trends. 🏅🌍🎉