Guided project: Clean and Analyze the Employee Surveys (Australia)

Hi everyone,

I hope everyone is doing great :slightly_smiling_face:. I am sharing my work to get some feedback, I really would appreciate it if you can take a moment to review and rectify its flaws (I’m sure there are many of them). One flaw I feel is that my explanation is too wordy rather than precise. Please let me know what you think about it? Again, I’d appreciate your time.

I also have one confession to make I spent 3 weeks completing this project, I think I spent way too much time than required on the project.

Lastly, thanks to @Elena_Kosourova for sharing your work for inspiration. The way you dealt with missing values helped me increase my knowledge a ton. It was done logically rather than rigorously.

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

EDITED based on feedback:
Click here to view the jupyter notebook file in a new tab

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hi @m.awon

Great work on this project :+1:! The plots before the conclusion section are just beautiful! :heart_eyes_cat:

The table in section 3. hasn’t been rendered properly, you might wanna update that.

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Hi Muhammad,

I’m really happy to know that my work was of use for creating such a cool project! :dizzy: :rocket: You’ve done a very thorough job, and I’m not surprised that it took you a lot of time. Great overall project stricture, comprehensive storytelling, excellent data cleaning and analysis, nice pretty-printed outputs. And yes, agree with @Rucha, the final visualizations are just amazing! :star_struck:

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Thank you for taking the time to review my work.

You are right the table has not been rendered for some reason, I’m trying to figure out how to remove the uploaded file and replace it with the new one but still no luck finding the way :slight_smile:

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Thank you for the great words they mean a lot to me. I’m glad that I did research and found your project to handle the missing data part more efficiently. This will stick with me forever that dropna() or fillna() should be used more logical in most cases.

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Hi @m.awon

you are the original post creator for the first post. So click on the edit button and reupload the file. It should create a link again to NBViewer to display your work.

Once in edit mode, you might see a section like this which is the file attachment. You can just delete this portion after you have successfully uploaded your updated version. Ensure the file references are different!

[Click here to view the jupyter notebook file in a new tab]( https://nbviewer.jupyter.org/urls/community.dataquest.io/uploads/short-url/hsZtQwhvTVZy9QR6wnEFdG5yqop.ipynb )

Or

you can simply create another post on the same topic and upload your revised work if any (no need to create a new topic, just continue this one with another post!)

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Hi Muhammad,

If for some technical reason you don’t manage to edit your initial post following the first suggestion of Rucha, then just follow her second suggestion and create a new post in this thread below :slightly_smiling_face: In this case, I can copy your updated notebook and insert it into your initial post, specifying that this is the updated version of your notebook based on the received feedback.

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Thank you @Elena_Kosourova, below is the attachment of my notebook.

clean-and-analyze-employee-exit-surveys.ipynb (843.7 KB)

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

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I figured out that the extra white spacing between characters was causing the issue. That’s why the table wasn’t rendered correctly.

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Perfect! I updated your initial post :ok_hand:

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Hey @Elena_Kosourova

Apologies, I completely forgot to mention to @m.awon that once he uploads the updated version, I can modify it if he finds difficulty in doing so. Yet you had to do this as well. :frowning:

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Hi @Rucha,

No problem, it wasn’t a big deal for me! :grinning: But ok, next time it will be you :stuck_out_tongue_winking_eye:

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Hi @m.awon

Thanks for sharing your nicely worked out project.
I really liked all the decriptions and your graphs.

It’s a minor remark, but I feel that it could further boost the understanding of the data furnished by your beautifuk graphs.
Can you apply the same x-Axis limits to your horizontal bar plots comparing the TAFE vs DETE data?
This would allow to recognize the gap of almost ~20%.
DETE dissatisfaction being ~40% where as TAFE is more around 20%.

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Great idea, I was not thinking it that way. Thanks for pointing it out.

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Hello @m.awon ,
Great energy you put in there. Very detailed comments and superb cleaning procedure.
I have a few light observations on the visuals.

a. It could be my colour perception but where do you want me to pay attention at first glance? This drives to the colour of the columns/bars. The main column/bar for consumption could maintain the present “purple” while the rest are made less strong or different colour.
b. Have you tried taking out the grid lines on the columns/bars and then, the background ?
c. The horizontgal axis top mark of 50 for “Unhappy Australian Emp…”
has the percentage symbol while others do not have it, then, the category numbers at the top of the columns do not have the % sign as seen on other visuals. Just thinking consistency here.
In all, your 3 weeks is also commendable cause better to have a good analysis/result than otherwise. I learned something from your notebook.

Again, great accomplishment by you.

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Thank you for taking the time and looking at the project. I needed this feedback, I didn’t even realize the inconsistency in my graphs. Certainly, your points are a great help. Also thanks for the encouraging words, I’m glad you got to benefit from the work I did.

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