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