Guided Project No. 01: App Profiles

Hi, everyone. Just sharing my output for the first guided project for the Data Scientist in Python path. Any feedback or tips would be greatly appreciated.

Guided Project: Profitable App Profiles

guided_project_01.ipynb (60.5 KB)

Click here to view the jupyter notebook file in a new tab

Hello @philiplibre!

Thanks for sharing your project with the community:) I hope my feedback is not too late.

What I liked:

  • You have a well structured and organized project where you clearly describe the steps you take
  • You also left links to data dictionaries, that’s great:)
  • Your code is documented
  • You did not blindly follow the DQ instructions but also decided to check Apple apps for duplicates
  • Good that you verified the most popular apps that can pull up the total number of installs!
  • The conclusion is short and straight-to-the-point. You’ve provided concrete examples for both age categories and even combined educational and gaming criteria to create a new app

What can be improved:

  • Do not reference Dataquest too much. It’s good that you recognize them but make it more “my own” project:)
  • When you check for duplicates you do the same things for both datasets. Could you think of a way of not copy-pasting the code?
  • Cell [19] you say “# should ideally convert to float but returns error because of some special characters we still don’t know how to deal with”. There are ways to ignore or coerce errors, check the documentation
  • Why would you want to look at the app size?

Happy coding:)

Thanks for the feedback, @artur.sannikov96.

Yes, these are very helpful tips. This was the first guided project I worked on, and the comments and text I added reflected that. Hopefully, my future notebooks would be much better than this one. Perhaps I’ll revisit this and apply what I’ve learned to this project sometime in the future!

1 Like