Since this December, I've been exploring codeforces data to answer the question of how best to improve your rating. Today, I'm presenting to you all the final project!
- This project was made for a course at Carnegie Mellon, but a lot of the work, mainly gathering data, was outside of scope of the class.
The first problem in trying to do data-driven analysis is trying to get data. Codeforces does have an API, but it's quite difficult to get data from it at a large scale. So first, I cleaned everything up and created two big datasets, and I've published them to kaggle. So, if you think my analysis is garbage, you can download the dataset yourself and fix it!
Dataset: The submissions and contests results for 60k recently active users
Dataset: The final standings for every codeforces contest
After getting the data, I began doing analysis and creating some charts. I looked at a lot of features, such as first solve time, rate of getting incorrect answers, and the difficulty of problems. For many of the features, I could not find great insights, but there are still quite a few interesting graphs in here! I hope you enjoy this data analysis that I've been working on!
