Employee Exit Surveys Guided Project

Hi everyone,

I’m currently going back and redoing the guided projects including the next steps and would love your feedback on my (hopefully) improved work.


Guided Project Clean And Analyze Employee Exit Surveys.ipynb (418.3 KB)

Thanks in advance,

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


I like your project so much, I have a lot to learn, thank you for sharing.


Thanks for the kind words, @jemartinezm1!

Good luck on your learning journey!


good job I liked the way you present your plots. you can check mine too here


Hi Ivelina,

Very good and interesting project, great job! I liked a lot your idea to investigate also other factors (position, employment status) and especially their combinations, like gender+age. Below are some of my suggestions, hope they will be useful.

  • It’s better to re-launch all the cells when the project is already finished, for the cells to be in order and starting from 1.
  • The code cell [226] - probably it should be explained somewhere in a markdown cell why we decided to drop exactly those columns.
  • You can add the titles to your plots to improve the visual perception.
  • Some of the code cells (for example, [262], [269], [271], [277], [278], [279], [282], [283], [286]) can be written in one line.
  • I was curious about the last line of your conclusion and checked it. But there’re only 3 of them! :grinning: These 100% values are always a little bit suspicious in terms of possible undersampling.

Happy coding with your future projects!


Hi Elena,

Thanks for the detailed and constructive feedback! Very valid points, not just for this project but for any project in general! :slight_smile:

Happy coding to you too!



Hi Ivelina,

It is my first time that i’m giving feedback,
Overall is very good presentation, but i have to agree with Elena in some code cells there are missing markdown explanations, very good work!


Hi Ivelina,

Your notebook was very helpful to me. Thank you. I tried to summarize it below.

  • Import required modules (pandas, numpy, matplotlib.pyplot)
  • Read the data sets
  • Review the data sets (info, head)
  • Check missing values (“Not Stated” => NaN)
  • Drop irrelevant columns
  • Rename columns (same names for mutual columns)
  • Drop irrelevant rows (keep only employees resigned)
  • Create “institute_service” column for DETE data (it keeps data of years worked)
  • Find employees resigned bacause of dissatisfaction
  • Combine the data sets
  • Categorize data in “institute_service” (years => categories)
  • Analyze the data
    • Dissatisfied by service category
    • Dissatisfied by age
    • Dissatisfied by institute
    • Dissatisfied by gender
    • Dissatisfied by employment status
    • Dissatisfied by positions
    • Dissatisfied by age and gender
  • Conclude

By the way, this is my first comment in DQ. I hope there is nothing wrong with the comment. :blush:


Very good summary, @cenkbursali! :slight_smile:

Happy learning,