🔥 Olympics Data Analysis 🔥

In Data Science field, there is a big topic before creating a model for future prediction is Data Analysis. We can find out the hidden stories of the data when we visualize it. Also sometimes it helps that which type of algorithm will be great for that data. So I build a website using StreamLit python library to visualize the 120 Years of Olympics history.

🏄 Info of this project:

These 4 step analysis are done in this project:

• Medal tally (No. of total medals, No. of Gold Medals, No. of Silver Medals and No. of Bronze Medals) over the years of different countries.
• Overall analysis like how many sports are played, how many countries are participated, how many cities hosted and so on. And there is a graph on participating nations over the years, graph on events over the years, graph on number of athletes participated over the years, heatmap on number of events and top 15 successful athletes on different sports.
• Then did country wise analysis like graph many medals won through the years, heatmap on how many medals won through out the years in different sports and top 15 athletes of the countries.
• And last, athletes wise analysis like distribution of winning Gold, Silver and Bronze Medals on the basis of athletes' age, distribution of age with respect to sports of Gold Medalist as well as height vs weight graph of different sports.

💡 Data Source & Deployment

Dataset Source: For this data analysis, I used this kaggle dataset -> https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results

Website Demo: This website build using StreamLit is deployed in Streamlit Share medium. The link of the website -> https://share.streamlit.io/sayan-roy-729/olympic-history-analysis/main/app.py

🔓 Install

First download this github repository to your local machine. Then create a python virtual environment using the following command and activate it.

virtualenv venv

Install required libraries by following the below command.

pip install -r requirements.txt

To run the website on your local machine, execute the below command

streamlit run app.py

📪 Connect with me

If you like my work then please give a ⭐ from your side. And you can connect with with on

• Linkedin
• Email