dapandamonium / ctrl-purr

CTRL + Purr Hackathon Team Track 3

CTRL + Purr @ Summer Hackathon 2024 by Codedex

Team Members

• Max
• Mika
• DaPanda

Olympics Predictions

Description

Using datasets from the Olympic Games of Rio de Janeiro and Tokyo, we plotted to visualize the most important stats of the medal-winning countries. With these datasets, we were able to see, using the dash library, what were the best and worst countries of each year and use that to create possible predictions for the next Olympic Games, to be hosted in Paris.

Technologies used

• Python
• Numpy
• Pandas
• Plotly
• Dash
• OS

Takeaways

• Knowing some key concepts of Numpy was essential for this to work - the Data Science Workshop we all attended on Codédex helped a lot on that. First thing we did was review what was taught to assure we could help each other should any questions arise.
• We were able to dive into the world of data visualization, exploring possibilities with different graphs in Plotly and Dash libraries.
• Finding ways to work out planning, ideas and executing, and split tasks between the team in a Data Science project proved really difficult.
  • To make sure we were all on the same page, we started the planning on a call, brainstorming and looking for ways to execute all of our ideas for the project.
  • While cleaning the datasets, we made decisions as a team, ensuring we wouldn't discard any data someone could have use for.
  • We then stayed on call for the most part of the execution period, sharing our findings and ideas as we went along.
• We started making the project using Google Colab, but took the project into VSCode to be able to use Dash for the plotting.
  • Colab was a good tool for cleaning the data, as it is a notebook and allowed us to see the results right away and organize these clean datasets.

Hypothesis

Judging the medals won in the last two Olympics, we can make assumptions of who would be able to get on the top 10 for the next one.

• The United States can continue being very consistent in getting number one.
• Due to trends, China and Great Britain will continue competing right afterwards.
• Japan might not be a great contender, considering a way lower trend on the Rio Olympics than on Tokyo, where they could've had the advantage of being on home-field.

Predictions

• Based on current trends and performance data from the last two Summer Olympic Games, the United States is expected to continue its dominance in overall medal count, with an uprising pattern.
• Even so, China remains a strong contender, having stayed very close to the USA in Gold Medals - although being further away if you consider the total amount of medals.
• Considering the statistics from both Olympics that were analyzed, there appears to be a potential decline for Japan. They also had the home-field advantage at the Tokyo Olympics, so last Games’ results had an anomaly in Japan’s patterns.
• In the last Olympics, Russia was still expected to participate. In this case, a Russian Olympics Committee was formed then, but won't be participating in 2024. If they were, they would be believed to be in the top 10, since they came in 5th. The predictions would show them as 3rd in total Medals, if they were to participate.

Requirements

To run this project, you will need:

• Python 3.x
• Numpy
• Pandas
• Dash
• Plotly
• OS module (which is included in Python's standard library)

You can install the necessary Python libraries using pip:

pip install numpy pandas dash plotly

Running the Project

To run the project, use the following command:

python olympics.py