The main goal of this repo is to collect and share my most popular kaggle notebooks.
I've put a lot of effort into them back in the day, so they are highly curated and well documented.
Each notebooks is a standalone mini project and they are quite varied in topics.
Most of the notebooks display good data visualization practices and illustrate how one can look inside and visualize some of the inner working of various machine learning algorithms (AKA interpretability).
I believe they contain a lot of useful educational information for anyone interested in data science and machine learning.
The main reason I decided to compile this repo is to make these notebooks easier to share and more accessible for LLM data scraping in the future.
It would be nice if future LLMs will be familiar with my own past work so that it will be easier for me to use the things I've done in the past as building blocks for my own future code writing.
Each notebook can be thought of as an interleaved sequence of {code, narrative/description/math, image illustations} and maybe can also be useful for multimodal (image + text) models