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Project on exploring_ebay_car_sales

Hi all, I am uploading the project on 'Exploring ebay car sales Data'. Open to all kinds of feedback, especially on 'overall project analysis'


[Exploring EBay Car Sales Data | Dataquest]( [ebay_car_sales.ipynb|attachment](upload://ghNbPcPG2dD1oWQlqse56Kdm7py.ipynb) (126.7 KB)

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@Bhagyashree your project cannot be viewed. You may want to reconsider uploading it again.

Hi @jesmaxavier, I am uploading the file down below. Hope you will be able to view it. Please get back to me if you still find any issue viewing the file. Thanks

ebay_car_sales.ipynb (126.7 KB)

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

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@Bhagyashree congrats on completing this project and may I say your effort in including the extra questions is appreciable :+1:. Below are a few pointers I have to hopefully improve your work

Presentation Style
  • Once you feel you are done with the project, re-run the entire project so that the cells start from [1]. This makes it easier for reviewers like me to refer to cells. image
  • You can avoid very technical details like the encoding of the dataset. A non-dev reader might find such details overbearing.
Coding Style
  • I like this bit of code in [326]. Its not something I was aware of when I was doing this project.
#swap the key and value in the above dictionary and sort the values
dict_unique_brand_swap = dict([(value, key) for key, value in dict_unique_brand.items()])
values = dict_unique_brand_swap.items()
sorted_values = sorted(values, reverse=True)
  • You seem to have commented on each step trying to explain what you are trying to do. This can be avoided in its entirety in the final iteration (i.e. when you clean up this project and put this out). Your current comments should help during that review. In the final version you could put down simple comments like #Calculate average price of each model for cell [325] so its helpful to both dev and non-dev readers.
  • You do not need to include details such as the name of the function you are about to use in the comments as its already in the code.
  • I feel its good practice that your round your outputs instead of outing the non-rounded values like in cell [325]. A simple numpy.round() should help with this regard.
  • There are some spelling mistakes that I noticed that you could clear out in the next iteration
  • In the next iteration I would recommend that you take a second look at the price column. Your current price range starts from $100.00. I think you could increase it further if analyze further. Hint: look for the word ‘schlachten’ in the dataset
  • Once you have gotten a hold on visualizations. I recommend that you re-do this project and add a couple of visualizations .
  • Once you have the visualizations in order, may be take a second look at this statement.

We observe that the relation between mean price and mean mileage does not follow any pattern.

  • I recommend that in the next iteration, you put all the cleaning together and follow that with the analysis. This could help with bringing in some continuity in the project.

Hope this is helpful. You are on the right track :racing_car:

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Hi @jesmaxavier ,
Thanks for the detailed suggestions. I will go through each of the comments and try incorporating them in my project and keep them in mind for my future analysis.


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