{"id":26396276,"url":"https://github.com/vipulbunny/olympics-analysis","last_synced_at":"2026-05-07T10:38:11.037Z","repository":{"id":280660303,"uuid":"942727365","full_name":"VIPULbunny/olympics-analysis","owner":"VIPULbunny","description":"A data analysis project exploring the Olympic Games from 1896 to 2016. It includes data cleaning, visualization, and insights on athletes, medals, and countries.","archived":false,"fork":false,"pushed_at":"2025-03-04T17:05:57.000Z","size":2576,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-17T11:31:31.247Z","etag":null,"topics":["data-science","eda","machinelearning","matplotlib","ml","olympics","pandas","python","seaborn","sports-analytics"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/VIPULbunny.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,"zenodo":null}},"created_at":"2025-03-04T15:19:50.000Z","updated_at":"2025-03-04T17:06:01.000Z","dependencies_parsed_at":null,"dependency_job_id":"1cb4db87-cda5-422a-83ac-bad6a5311ab4","html_url":"https://github.com/VIPULbunny/olympics-analysis","commit_stats":null,"previous_names":["vipulbunny/olympics-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/VIPULbunny/olympics-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VIPULbunny%2Folympics-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VIPULbunny%2Folympics-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VIPULbunny%2Folympics-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VIPULbunny%2Folympics-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/VIPULbunny","download_url":"https://codeload.github.com/VIPULbunny/olympics-analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VIPULbunny%2Folympics-analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260520557,"owners_count":23021643,"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","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":["data-science","eda","machinelearning","matplotlib","ml","olympics","pandas","python","seaborn","sports-analytics"],"created_at":"2025-03-17T11:28:06.872Z","updated_at":"2026-05-07T10:38:10.994Z","avatar_url":"https://github.com/VIPULbunny.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🏅 Olympics Data Analysis (1896-2016)\n![A professional banner image for 'Olympics Data Analysis'  The design should feature the Olympic rings, a dynamic stadium background, and a futuristic](https://github.com/user-attachments/assets/f7c35027-5875-4d90-becf-0aa31dda85de)\n\n\n\n## 📌 Project Overview\nThe **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.\n\n## 📂 Dataset Description\nThe size of **athlete_events.csv** is more than 20mb so i had provided the link and description in the file name **'Dataset_link.py'**\n\nThis analysis uses two primary datasets:\n\n1. **athlete_events.csv** - Contains detailed records of Olympic athletes, including:\n   - Name, Age, Gender\n   - Sport, Event, Medal (if won)\n   - Country (NOC), Year, Season (Summer/Winter)\n\n2. **noc_regions.csv** - Maps National Olympic Committees (NOCs) to country names, helping in regional analysis.\n\n## 🚀 Project Objectives\n- Perform **Exploratory Data Analysis (EDA)** to understand athlete trends.\n- Visualize country-wise **medal counts** and athlete participation.\n- Analyze **gender representation** and its evolution in the Olympics.\n- Identify the **most successful athletes and countries** over the years.\n\n## 🔧 Installation\nTo run this project on your local m  achine:\n\n1. **Clone the Repository:**\n   ```sh\n   git clone https://github.com/VIPULbunny/olympics-analysis.git\n   ```\n2. **Navigate to the Project Directory:**\n   ```sh\n   cd olympics-analysis\n   ```\n3. **Install Dependencies:**\n   ```sh\n   pip install numpy pandas matplotlib seaborn\n   ```\n4. **Run the Jupyter Notebook:**\n   ```sh\n   jupyter notebook\n   ```\n\n## 📊 Exploratory Data Analysis (EDA)\n### 🏆 Key Insights from Data\n- **Total Athletes Participated:** `{total_athletes}`\n- **Total Olympic Games Editions:** `{total_games}`\n- **Top 10 Countries by Athlete Count:**\n  - USA, Germany, UK, France, China, etc.\n- **Most Successful Athletes:**\n  - Michael Phelps, Usain Bolt, etc.\n- **Gender Representation Over Time:**\n  - Increasing female participation in modern Olympics.\n\n### 🔎 Data Cleaning \u0026 Preprocessing\n- Merged `athlete_events.csv` with `noc_regions.csv` for accurate country mapping.\n- Handled **missing values** in age, medal, and region data.\n- Converted categorical features (`Sex`, `Medal`) into structured formats for better analysis.\n\n## 📈 Data Visualizations\n### 🎖️ Medal Distribution\n![image](https://github.com/user-attachments/assets/979e489f-84ef-4ec1-aff4-3b54ec167274)\n\n\n### 🏅 Top 10 Countries by Medal Count\n![image](https://github.com/user-attachments/assets/73ea54c6-360e-48e5-9738-9a93f48a8cee)\n\n\n### 📊 Gender Representation Over the Years\n![image](https://github.com/user-attachments/assets/bd1dc1b6-aa44-419c-8fca-36c781230c2e)\n\n### 🌍  Count occurrences of 'Sex' for each 'Season'\n![image](https://github.com/user-attachments/assets/61806be5-16fa-4cca-9e22-ade7da9a11bc)\n\n\n## 📜 License\nThis project is licensed under the **MIT License**.\n\n## 🤝 Contributing\nWe welcome contributions! If you’d like to improve the analysis or add new insights:\n\n1. **Fork the repository**.\n2. **Create a feature branch**: `git checkout -b feature-branch`\n3. **Commit your changes**: `git commit -m \"Added new analysis\"`\n4. **Push to GitHub**: `git push origin feature-branch`\n5. **Open a Pull Request** 🚀\n\n## 📬 Contact\nFor queries or collaborations, reach out via email or open an issue on GitHub.\n\n---\n**⭐ If you find this project useful, please give it a star!** 🌟\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvipulbunny%2Folympics-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvipulbunny%2Folympics-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvipulbunny%2Folympics-analysis/lists"}