https://github.com/nagarathnatechie/olympics_data_analysis
This project involves the analysis and visualization of data from the Olympic Games using Python, Streamlit, and data analysis libraries. The goal is to gain insights into various aspects of the Olympics, including medal distribution across countries, trends in athletes' performances, historical changes in sports participation, and more.
https://github.com/nagarathnatechie/olympics_data_analysis
matplotlib plotly python streamlit
Last synced: 11 months ago
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This project involves the analysis and visualization of data from the Olympic Games using Python, Streamlit, and data analysis libraries. The goal is to gain insights into various aspects of the Olympics, including medal distribution across countries, trends in athletes' performances, historical changes in sports participation, and more.
- Host: GitHub
- URL: https://github.com/nagarathnatechie/olympics_data_analysis
- Owner: NagarathnaTechie
- Created: 2025-02-27T18:52:56.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-07T10:03:41.000Z (about 1 year ago)
- Last Synced: 2025-07-16T02:12:33.482Z (12 months ago)
- Topics: matplotlib, plotly, python, streamlit
- Language: Python
- Homepage: https://olympics-data-insights.streamlit.app/
- Size: 5.19 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🏅 Olympics Data Insights Dashboard
**A Streamlit-powered analytics platform** visualizing 120 years of Olympic history with interactive charts, athlete statistics, and country performance metrics. Perfect for sports analysts and Olympics enthusiasts!
🌍 **Live App**: [Olympics Data Insights](https://olympics-data-insights.streamlit.app/)
---
## 📌 Table of Contents
- [🌟 Key Features](#-key-features)
- [📊 Dataset Overview](#-dataset-overview)
- [🛠️ Tech Stack](#️-tech-stack)
- [📸 Screenshots](#-screenshots)
- [🚀 Getting Started](#-getting-started)
- [🤝 Contributing](#-contributing)
- [📞 Contact](#-contact)
---
## 🌟 Key Features
📈 Historical Trends
- 120 years of Olympic data (1896-2016)
- Medal progression by country
- Sport popularity over time
🏅 Athlete Analytics
- Top performers by sport
- Age/height/weight distributions
- Medal-winning athletes
🌍 Geospatial Insights
- Country performance maps
- Host city analysis
- Medal tally by region
---
## 📊 Dataset Overview
| Metric | Value |
|----------------------|---------------------------|
| Time Period | 1896-2016 |
| Athletes Recorded | 271,116 |
| Countries Represented| 207 |
| Sports Covered | 66 |
| Events Tracked | 765 |
| Data Points | 2.3M+ |
---
## 🛠️ Tech Stack
| Component | Technology |
|--------------------|---------------------------|
| Frontend | Streamlit (1.28.0) |
| Visualization | Plotly, Matplotlib |
| Data Processing | Pandas, NumPy |
| Geospatial | PyDeck, GeoPandas |
| Deployment | Streamlit Community Cloud|
---
## 📸 Screenshots
**Interactive DashBoard**
**Country Performance**
**Overall Analysis**
---
## 🚀 Getting Started
### Local Installation
```bash
# Clone repository
git clone https://github.com/yourusername/olympics-data-insights.git
# Install dependencies
pip install -r requirements.txt
# Run the app
streamlit run app.py
```
# Requirements
```plaintext
streamlit==1.28.0
pandas==2.0.3
plotly==5.15.0
pydeck==0.8.0
```
## 🤝 Contributing
1. Fork the repository
2. Create your feature branch:
```bash
git checkout -b feature/your-feature
```
3. Commit changes:
```bash
git commit -m "Add new visualization"
```
4. Push and open a Pull Request
## 📞 Contact
Have questions or suggestions?
📧 [Email](nagarathnashenoy123@gmail.com)
🔗 [LinkedIn](https://www.linkedin.com/in/nagarathna-shenoy-457751218).