Guided Projects with DQ Community

Hi All,

Beginning a new thread as instructed by @Elena_Kosourova where I’ll be posting the guided projects starting with Statistics Fundamentals. Requesting your valuable feedback on the same.

Guided Project - Investigating Fandango Movie Ratings.ipynb (112.3 KB)

https://app.dataquest.io/m/288/guided-project%3A-investigating-fandango-movie-ratings/8/next-steps

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

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Hi @ashwin86rajan,

Thanks for sharing your project with the Community!

By the way, I’d recommend you to create a new thread for each of your guided projects in future. The drawback of this approach is that the projects will be distributed in several posts, so you’ll have to search for them, when you need to revise any of them in future, for example. But the advantage is that each thread will be your own and dedicated to a specific topic, so other students, doing any of those topics, will easily find your project (due to the tags autamatically added by the system) for reviewing / suggesting / getting some inspiration from your work. And this advantage is rather important :slightly_smiling_face: Hence, every time when you compete a guided project, just go to the link “Share you project with the Community” (or something like this) that you’ll find on the last mission screen of that project, create a new topic with the name of your work and share your notebook directly there.

Now about this your project. You’ve done a good job, congratulations! Interesting and eye-catching title, project goal description (including the updated one), good emphasizing throughout the project, clean and easily readable code.

What I can suggest to you:

  • It’s better to re-run the already ready project to have all the code cells in order and starting from 1.
  • Please add a link on the original dataset in the introduction.
  • A good practice is to import all the libraries together (at least the “famous” ones, and it’s exactly the case of your project) in the 1st code cell.
  • Avoid too long, too wordy, too evident or multiline code comments. They should be as concise as possible while still technically informative. You might find useful this article about the best practices of code commenting in DS.
  • Avoid unnecessary empty lines in code cells (like the code cell [157]), they only increase the vertical length of a project.
  • When naming variables or dataframes, you’d better select a descriptive name to each, otherwise it can be difficult to manipulate all those df1, df2, etc. in future, especially if you have many of them.
  • It’s better to rotate x-tick labels horizontally on the last graph.
  • The adjacent code cells without any output or markdown explanations between them can be combined in one (like [164]+[165] or [166]+[167]).
  • Probably, it’s better to add more information in the conclusion, more insights obtained while doing this project.

Hope my ideas were useful. Good luck with your future projects!

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Hi @Elena_Kosourova,

Thanks for your feedback. Will implement good practices in the coming projects.

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