Congrats on completing the project.
This is part of the Data Science path, yes? It seems like you have a good handle on how to maneuver around a csv file and create classes and functions.
The main issue that I will point out is that anyone reading the notebook cannot see any output from the functions you’ve created. It might be helpful to use Jupyter Notebook or an alternative would be Google Colab & then upload that notebook to your GitHub repo. If you could use either of those, one could see the output of the code analysis.
Data analysts and scientists are storytellers and give a voice to the data. A notebook of an analysis tells a story and gives the audience an idea of the purpose of the study/the questions you’re trying to answer–if possible–from the data. (This data set is primarily for EDA purposes—exploring insurance costs in different regions of the country and what, if any, potential variables might affect those costs. It’s a data set that is used at several different phases of the DS path and, as one gains more skills those skills are applied to the project in order to glean more insights from the data. So, as you learn about other Python libraries like Pandas, SciPy, etc. you can do more analysis like hypothesis testing and correlation between variables.)