{"id":22429884,"url":"https://github.com/nehul1149/olympic-data-analysis","last_synced_at":"2026-05-18T02:31:33.054Z","repository":{"id":263661540,"uuid":"891098455","full_name":"Nehul1149/Olympic-Data-Analysis","owner":"Nehul1149","description":"This project is an interactive data visualization and analytics platform for exploring historical Olympic Games data. Built with Python and Streamlit, it offers an in-depth analysis of medal tallies, athlete statistics, and country-wise performance trends, providing users with powerful insights into the world's biggest sporting event.","archived":false,"fork":false,"pushed_at":"2024-11-28T13:31:37.000Z","size":13935,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T07:13:50.204Z","etag":null,"topics":["analysis","data-analysis","data-science","data-visualization","matplotlib","python","streamlit"],"latest_commit_sha":null,"homepage":"https://olympic-data-analysis-bynehul.streamlit.app/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Nehul1149.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-19T18:12:24.000Z","updated_at":"2024-12-07T19:56:55.000Z","dependencies_parsed_at":null,"dependency_job_id":"26b5d639-1aa5-437d-a2e6-540036addfcf","html_url":"https://github.com/Nehul1149/Olympic-Data-Analysis","commit_stats":null,"previous_names":["nehul1149/olympic-data-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Nehul1149/Olympic-Data-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nehul1149%2FOlympic-Data-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nehul1149%2FOlympic-Data-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nehul1149%2FOlympic-Data-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nehul1149%2FOlympic-Data-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Nehul1149","download_url":"https://codeload.github.com/Nehul1149/Olympic-Data-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nehul1149%2FOlympic-Data-Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":272922276,"owners_count":25015766,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-30T02:00:09.474Z","response_time":77,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","data-analysis","data-science","data-visualization","matplotlib","python","streamlit"],"created_at":"2024-12-05T21:06:04.781Z","updated_at":"2026-05-18T02:31:33.027Z","avatar_url":"https://github.com/Nehul1149.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🏅 Olympic Data Analysis\n\nThis project is an interactive data visualization and analytics platform for exploring historical Olympic Games data. Built with Python and Streamlit, it offers an in-depth analysis of medal tallies, athlete statistics, and country-wise performance trends, providing users with powerful insights into the world's biggest sporting event.\n\n---\n\n## 📌 Features\n\n- **Medal Tally Analysis:**\n  - Explore medal tallies by year, country, or both.\n  - Compare the performance of nations and athletes across Olympic editions.\n\n- **Overall Analysis:**\n  - Visualize participation growth in terms of nations, events, and athletes.\n  - Examine trends in sports, events, and athlete demographics.\n\n- **Country-Wise Analysis:**\n  - Delve into the medal-winning history of specific countries.\n  - Identify sports where countries excel using heatmaps.\n\n- **Athlete-Wise Analysis:**\n  - Analyze age distributions of medalists across gold, silver, and bronze categories.\n  - Study the physical attributes (height, weight) of athletes by sport and gender.\n  - Explore the historical participation trends of male and female athletes.\n\n---\n\n## 🚀 Technologies Used\n\n- **Python** for data processing and analysis.\n- **Streamlit** for creating the interactive web app.\n- **Pandas** for data manipulation and cleaning.\n- **Matplotlib**, **Seaborn**, and **Plotly** for visualizing trends and distributions.\n- **Scipy** for generating statistical plots.\n\n---\n\n## 📂 Data Sources\n\n1. **Athlete Events Dataset**: Contains details of athletes, their events, and medal outcomes.\n2. **NOC Regions Dataset**: Maps National Olympic Committees (NOCs) to their respective regions.\n\n---\n\n## 🛠️ How It Works\n\n1. **Data Preprocessing**:\n   - Filtered for Summer Olympics data to ensure relevance.\n   - Merged datasets to include regional information.\n   - Applied one-hot encoding for medal types for detailed analysis.\n\n2. **Interactive Dashboard**:\n   - Users can explore trends via dropdowns and dynamic visualizations.\n   - Options to analyze data by year, sport, athlete, or nation.\n\n---\n\n## 📊 Visual Highlights\n\n- **Line Charts**:\n  - Growth of participating nations, athletes, and events over time.\n- **Heatmaps**:\n  - Sports performance trends of countries.\n- **Scatter Plots**:\n  - Height vs. weight distribution of athletes, categorized by gender and medal type.\n- **Distribution Plots**:\n  - Age trends among gold, silver, and bronze medalists.\n\n---\n\n## 🖥️ How to Run the Project\n\n1. Visit the live application hosted on Streamlit:  \n   [Olympic Data Analysis](https://olympic-data-analysis-bynehul.streamlit.app/)\n\n2. Alternatively, you can run it locally:\n   - Clone the repository:\n     ```bash\n     git clone https://github.com/yourusername/Olympic-Data-Analysis.git\n     cd Olympic-Data-Analysis\n     ```\n   - Install required packages:\n     ```bash\n     pip install -r requirements.txt\n     ```\n   - Run the Streamlit app:\n     ```bash\n     streamlit run app.py\n     ```\n   - Open the app in your browser at `http://localhost:8501`.\n\n\n## 📢 Contributions\n\n- Contributions, issues, and feature requests are welcome! Feel free to open an issue or submit a pull request for improvements.\n\n---\n\n## 📧 Contact\n\n- For any queries or suggestions, reach out at nehulkr30582@gmail.com.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnehul1149%2Folympic-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnehul1149%2Folympic-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnehul1149%2Folympic-data-analysis/lists"}