{"id":22763828,"url":"https://github.com/krishnaura45/olympics_data_dive","last_synced_at":"2025-03-30T09:42:44.440Z","repository":{"id":226047862,"uuid":"751948195","full_name":"krishnaura45/Olympics_Data_Dive","owner":"krishnaura45","description":"Currently working on","archived":false,"fork":false,"pushed_at":"2024-06-04T15:02:18.000Z","size":973,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-05T11:45:08.348Z","etag":null,"topics":["data-analytics","data-science","insights","powerbi","python3","sports-analytics","streamlit-webapp","webapp"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/krishnaura45.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-02-02T17:17:40.000Z","updated_at":"2024-06-04T15:02:21.000Z","dependencies_parsed_at":"2024-03-13T06:25:05.470Z","dependency_job_id":"86163f47-f137-4656-bfd6-0975fc02a9b1","html_url":"https://github.com/krishnaura45/Olympics_Data_Dive","commit_stats":null,"previous_names":["krishnaura45/olympics_data_dive"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/krishnaura45%2FOlympics_Data_Dive","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/krishnaura45%2FOlympics_Data_Dive/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/krishnaura45%2FOlympics_Data_Dive/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/krishnaura45%2FOlympics_Data_Dive/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/krishnaura45","download_url":"https://codeload.github.com/krishnaura45/Olympics_Data_Dive/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246301955,"owners_count":20755512,"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-analytics","data-science","insights","powerbi","python3","sports-analytics","streamlit-webapp","webapp"],"created_at":"2024-12-11T11:11:16.811Z","updated_at":"2025-03-30T09:42:44.372Z","avatar_url":"https://github.com/krishnaura45.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003eOlympics Data Dive: Unveiling Performance Trends\u003c/h1\u003e\n\n### INTRODUCTION\u003chr\u003e\n- The Olympics are a premier international sports event uniting athletes globally, with a rich history dating back to ancient Greece. \n- Data analytics plays a crucial role in understanding and enhancing athletes' performance, training methods, and overall outcomes.\n- This project employs Power BI for analyzing Olympic data, providing interactive visualization and advanced statistical modeling.\n- The project aims to analyze athlete and country performance across Olympic events, identifying trends and correlations to inform sports management and training strategies.\n\n\n### LITERATURE REVIEW\u003chr\u003e\n![image](https://github.com/krishnaura45/Olympics_Data_Dive/assets/118080140/127d42c4-429f-4a3c-9472-2b18752033b8)\n\n\n### OBJECTIVES\u003chr\u003e\n- Explore historical performance trends.\n- Study data analytics using tools such as Power BI  \n- Develop interactive dashboards for intuitive exploration.\n- Utilize Python for statistical analysis and modeling.\n- Build interactive app\n\n\n### WORKING\u003chr\u003e\n\u003cb\u003eStep 1: Collection of Required Data\u003c/b\u003e\n- Utilized our newly constructed dataset ‘Olympics Legacy: 1896-2020’.\n- It includes comprehensive data spanning 124 years of Olympics.\n- It’s primary file has 12 features and 2,86,238 records.\n\nDataset Link - \u003ca href=\"https://www.kaggle.com/datasets/krishd123/olympics-legacy-1896-2020\" target=\"_blank\"\u003eOlympics Legacy\u003c/a\u003e\n\n\u003cimg src='https://github.com/krishnaura45/Olympics_Data_Dive/blob/main/dataset.png' alt='Main csv file'\u003e all_athete_games.csv\u003c/img\u003e\n\n\u003cb\u003eStep 2: Data Analysis and Dashboard Creation using Power BI\u003c/b\u003e\n- Transform Data: Into a final dataframe by\n  - Removing columns\n  - Defining relationships / Merging\n  - Other measures\n   \n- Analyzing Olympics data using various charts such as-\n  - Table chart: Medal Tally\n  - Ribbon chart: Age-wise Performance\n  - Pie chart: Gender-wise participation\n  - Cards for specific stats\n\n\u003cb\u003eStep 3: Python Analysis\u003c/b\u003e\n- Performed operations such as:\n  - Merging files on the basis of specific features\n  - Extracting summer olympics data\n  - Calculating number and names of countries participated\n  - Handling missing and duplicate values\n  - One Hot Encoding of Medals\n  - Grouping encoded data along with original on the basis of specific features\n  - Calculating two different medal tallies with respect to accuracy\n \n- Performed four types of analysis:\n  - *Medal Tally Analysis*\n    1) ***Overall Tally***: Displays the total medal count for all countries across all years.\n    2) ***Year-wise Tally***: Shows the medal count for all countries for a particular year.\n    3) ***Year-over-Year Tally***: Presents the medal count for all countries over multiple years.\n    4) ***Country-specific Tally***: Provides the medal count for a particular year and country.\n\n  - *Athlete-wise Analysis*\n    1) ***Distribution of Age vs. Medals***: Examines the distribution of athlete ages concerning the number of medals won.\n    2) ***Distribution of Age vs. Sports (Gold Medalist)***: Analyzes the age distribution of gold medalists across different sports.\n    3) ***Men vs. Women Participation Over Years***: Visualizes the participation trends of men and women athletes over various editions of the Olympics.\n\n  - *Country-wise Analysis*\n    1) ***Medal Tally Over Years***: Visualizes the medal tally for a specific country across different editions of the Olympics.\n    2) ***Sports Excellence***: Identifies the sports in which a particular country excels based on medal counts.\n    3) ***Top 10 Athletes***: Highlights the top 10 athletes from a specific country based on their performance in the Olympics.\n\n  - *Overall Analysis*\n    1) ***Top Statistics***: Evaluates key metrics such as the number of editions, hosting countries, sports, events, nations participated, and athletes.\n    2) ***Participating Nations Over Years***: Visualizes the trend of participating nations over different editions of the Olympics.\n    3) ***Events Over Years***: Illustrates the evolution of Olympic events over time using line plots.\n    4) ***Athletes Over Years***: Depicts the growth in the number of athletes participating in the Olympics across editions.\n    5) ***Number of Events Over Time*** and ***Most Successful Athletes***\n\n\u003cb\u003eStep 4: Web App Development\u003c/b\u003e\n- Developed web app using ***Streamlit***, simplifying interactive data exploration with minimal code.\n- Scripted Python functions for preprocessing, analysis, and visualization, ***enhancing modularity***.\n- Created helper modules (helper.py and preprocessor.py) for ***streamlined data manipulation*** and ***maintenance***.\n- Utilized Streamlit's intuitive interface for ***user-friendly data visualization*** and dashboard creation.\n\n\u003cb\u003eStep 5: Deployment\u003c/b\u003e\n- Prepare the locally developed web app for deployment on a cloud platform, prioritizing ***Heroku*** for its ***user-friendly interface*** and ***Python support***.\n- Create necessary files including ***requirements.txt*** and ***Procfile*** to ensure Heroku can install dependencies and execute the application seamlessly.\n- Push the application code and required files to a Git repository for version control and collaboration.\n- Deploy the application on Heroku using either the ***CLI*** or web dashboard, initiating automatic build and deployment processes to generate a unique ***URL for access***.\n\n\n\u003ch3 align=\"left\"\u003eRESULTS AND VISUALIZATIONS\u003c/h3\u003e\u003chr\u003e\n\u003ch4 align=\"left\"\u003eComplete Power BI Dashboard - Overview\u003c/h4\u003e\n\u003cimg src='https://github.com/krishnaura45/Olympics_Data_Dive/blob/main/power_bi_dashboard.jpeg' align='center'\u003e\u003cbr\u003e\n\n\u003ch4 align=\"left\"\u003eWeb App Interface - Overall Medal Tally\u003c/h4\u003e\n\u003cimg src='https://github.com/krishnaura45/Olympics_Data_Dive/assets/118080140/549caaec-e5f1-4ba0-8272-9b45cc37f4b9' align='center'\u003e\u003cbr\u003e\n\n\u003ch4 align=\"left\"\u003eOverall Analysis Page\u003c/h4\u003e\n\u003cimg src='https://github.com/krishnaura45/Olympics_Data_Dive/assets/118080140/24a21301-0442-4580-a51b-236e3bba9a6c' align='center'\u003e\u003cbr\u003e\n\n\u003ch4 align=\"left\"\u003eCountry specific Analysis (India)\u003c/h4\u003e\n\u003cimg src='https://github.com/krishnaura45/Olympics_Data_Dive/assets/118080140/69dcbcd5-2c1a-4e65-ab63-2f537c25f767' align='center'\u003e\u003cbr\u003e\n\n\u003ch4 align=\"left\"\u003eIndia's Overall Performance\u003c/h4\u003e\n\u003cimg src='https://github.com/krishnaura45/Olympics_Data_Dive/assets/118080140/91b8293e-049d-4f54-96b2-5b725666cb03' align='center'\u003e\u003cbr\u003e\n\n\n### CONCLUSIONS/OUTCOMES\u003chr\u003e\n- **Comprehensive Dataset Formation**: Through meticulous exploration of 3-4 datasets, curated a comprehensive repository of Olympic data spanning various aspects, including athlete performances and other logistical details.\n\n- **Insightful Dashboard Creation with Power BI**: Utilizing Power BI, transformed our analytical findings into interactive and visually appealing dashboard, offering stakeholders a user-friendly platform to explore and understand the intricacies of Olympic performance trends.\n  \n- **Enriched understanding via Python analysis**, delving into medal tallies, overall trends, country-specific performances, and athlete characteristics.\n- Extension of analysis reach through **development and deployment** of a **user-friendly web app using Streamlit and Heroku**, facilitating real-time exploration of Olympic datasets.\n- **Strategic implications** can be identified for countries, enabling optimization of training programs, resource allocation, and strategic partnerships to enhance competitiveness on the global Olympic stage.\n\n\n### FUTURE SCOPE\u003chr\u003e\n- Analyze data through Tableau.\n- Enabling dynamic and up-to-date analysis.\n- Enhance predictive modeling capabilities to forecast athlete performances.\n\n\n### REFERENCES\u003chr\u003e\n1) Geurin, Andrea N., and Michael L. Naraine. \"20 years of Olympic media research: trends and future directions.\" Frontiers in Sports and Active Living 2 (2020): 572495.\n2) P. Johnson and S. Lee, \"The Evolution of Gender Parity in the Olympic Games,\" Gender \u0026 Sport, vol. 8, no. 1, pp. 17-28, 2018.\n3) M. Garcia and F. Rodriguez, \"Impact of Hosting the Olympics on National Performance,\" J. Sport Econ., vol. 20, no. 4, pp. 301-315, 2019.\n4) G. Becker and D. Stevens, \"Olympic Medals and Economic Development: A 120-Year Perspective,\" J. Econ. Dev., vol. 15, no. 2, pp. 87-101, 2014.\n5) Y. Kim and J. Park, \"Climate and Its Effect on Olympic Performance,\" Clim. Change Sports, vol. 5, no. 3, pp. 210-225, 2021.\n\n\n### TECH STACKS INVOLVED\u003chr\u003e\n- Python\n- Power BI\n- Streamlit\n\n# TEAM THE BOYS\u003chr\u003e\nKrishna Dubey (Data Collection, Dashboard and Analysis), Pankaj Kumar Giri (Data Collection), Nayandeep (Android)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkrishnaura45%2Folympics_data_dive","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkrishnaura45%2Folympics_data_dive","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkrishnaura45%2Folympics_data_dive/lists"}