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https://github.com/krishnaura45/olympics_data_dive

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https://github.com/krishnaura45/olympics_data_dive

data-analytics data-science insights powerbi python3 sports-analytics streamlit-webapp webapp

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README 

        
Olympics Data Dive: Unveiling Performance Trends



### INTRODUCTION


- The Olympics are a premier international sports event uniting athletes globally, with a rich history dating back to ancient Greece. 

- Data analytics plays a crucial role in understanding and enhancing athletes' performance, training methods, and overall outcomes.

- This project employs Power BI for analyzing Olympic data, providing interactive visualization and advanced statistical modeling.

- The project aims to analyze athlete and country performance across Olympic events, identifying trends and correlations to inform sports management and training strategies.



### LITERATURE REVIEW


![image](https://github.com/krishnaura45/Olympics_Data_Dive/assets/118080140/127d42c4-429f-4a3c-9472-2b18752033b8)



### OBJECTIVES


- Explore historical performance trends.

- Study data analytics using tools such as Power BI  

- Develop interactive dashboards for intuitive exploration.

- Utilize Python for statistical analysis and modeling.

- Build interactive app



### WORKING


Step 1: Collection of Required Data

- Utilized our newly constructed dataset ‘Olympics Legacy: 1896-2020’.

- It includes comprehensive data spanning 124 years of Olympics.

- It’s primary file has 12 features and 2,86,238 records.



Dataset Link - Olympics Legacy



Main csv file all_athete_games.csv



Step 2: Data Analysis and Dashboard Creation using Power BI

- Transform Data: Into a final dataframe by

  - Removing columns

  - Defining relationships / Merging

  - Other measures

   

- Analyzing Olympics data using various charts such as-

  - Table chart: Medal Tally

  - Ribbon chart: Age-wise Performance

  - Pie chart: Gender-wise participation

  - Cards for specific stats



Step 3: Python Analysis

- Performed operations such as:

  - Merging files on the basis of specific features

  - Extracting summer olympics data

  - Calculating number and names of countries participated

  - Handling missing and duplicate values

  - One Hot Encoding of Medals

  - Grouping encoded data along with original on the basis of specific features

  - Calculating two different medal tallies with respect to accuracy

 

- Performed four types of analysis:

  - *Medal Tally Analysis*

    1) ***Overall Tally***: Displays the total medal count for all countries across all years.

    2) ***Year-wise Tally***: Shows the medal count for all countries for a particular year.

    3) ***Year-over-Year Tally***: Presents the medal count for all countries over multiple years.

    4) ***Country-specific Tally***: Provides the medal count for a particular year and country.



  - *Athlete-wise Analysis*

    1) ***Distribution of Age vs. Medals***: Examines the distribution of athlete ages concerning the number of medals won.

    2) ***Distribution of Age vs. Sports (Gold Medalist)***: Analyzes the age distribution of gold medalists across different sports.

    3) ***Men vs. Women Participation Over Years***: Visualizes the participation trends of men and women athletes over various editions of the Olympics.



  - *Country-wise Analysis*

    1) ***Medal Tally Over Years***: Visualizes the medal tally for a specific country across different editions of the Olympics.

    2) ***Sports Excellence***: Identifies the sports in which a particular country excels based on medal counts.

    3) ***Top 10 Athletes***: Highlights the top 10 athletes from a specific country based on their performance in the Olympics.



  - *Overall Analysis*

    1) ***Top Statistics***: Evaluates key metrics such as the number of editions, hosting countries, sports, events, nations participated, and athletes.

    2) ***Participating Nations Over Years***: Visualizes the trend of participating nations over different editions of the Olympics.

    3) ***Events Over Years***: Illustrates the evolution of Olympic events over time using line plots.

    4) ***Athletes Over Years***: Depicts the growth in the number of athletes participating in the Olympics across editions.

    5) ***Number of Events Over Time*** and ***Most Successful Athletes***



Step 4: Web App Development

- Developed web app using ***Streamlit***, simplifying interactive data exploration with minimal code.

- Scripted Python functions for preprocessing, analysis, and visualization, ***enhancing modularity***.

- Created helper modules (helper.py and preprocessor.py) for ***streamlined data manipulation*** and ***maintenance***.

- Utilized Streamlit's intuitive interface for ***user-friendly data visualization*** and dashboard creation.



Step 5: Deployment

- Prepare the locally developed web app for deployment on a cloud platform, prioritizing ***Heroku*** for its ***user-friendly interface*** and ***Python support***.

- Create necessary files including ***requirements.txt*** and ***Procfile*** to ensure Heroku can install dependencies and execute the application seamlessly.

- Push the application code and required files to a Git repository for version control and collaboration.

- 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***.



RESULTS AND VISUALIZATIONS


Complete Power BI Dashboard - Overview






Web App Interface - Overall Medal Tally






Overall Analysis Page






Country specific Analysis (India)






India's Overall Performance






### CONCLUSIONS/OUTCOMES


- **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.



- **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.

  

- **Enriched understanding via Python analysis**, delving into medal tallies, overall trends, country-specific performances, and athlete characteristics.

- 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.

- **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.



### FUTURE SCOPE


- Analyze data through Tableau.

- Enabling dynamic and up-to-date analysis.

- Enhance predictive modeling capabilities to forecast athlete performances.



### REFERENCES


1) 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.

2) P. Johnson and S. Lee, "The Evolution of Gender Parity in the Olympic Games," Gender & Sport, vol. 8, no. 1, pp. 17-28, 2018.

3) M. Garcia and F. Rodriguez, "Impact of Hosting the Olympics on National Performance," J. Sport Econ., vol. 20, no. 4, pp. 301-315, 2019.

4) 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.

5) Y. Kim and J. Park, "Climate and Its Effect on Olympic Performance," Clim. Change Sports, vol. 5, no. 3, pp. 210-225, 2021.



### TECH STACKS INVOLVED


- Python

- Power BI

- Streamlit



# TEAM THE BOYS


Krishna Dubey (Data Collection, Dashboard and Analysis), Pankaj Kumar Giri (Data Collection), Nayandeep (Android)