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My fourth Guided Project: Finding Heavy Traffic Indicators on I-94

Data Visualization Fundamentals (Guided Project)

Hello!
All feedback comments for the improvement of this project are welcome.

https://app.dataquest.io/c/95/m/524/guided-project%3A-finding-heavy-traffic-indicators-on-i-94/10/next-steps

Guided Project_ Finding Heavy Traffic Indicators on I-94.ipynb (622.4 KB)

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

1 Like

@johnndammy you’ve put some serious effort into your project and it shows. You are definitely on the right track with regards to your explanations so you should keep that going. The following are a couple of the pointers I have to offer:

Presentation Style
  • Include a proper introduction and conclusion section.
  • Add a link to where the reader can access the dataset
  • You’ve included the shape of both day and night dataframes. I would recommend giving some context to the same with a printspecifying what those numbers mean. This is recommended for all such outputs. It will be like a title.
  • I recommend the use of the marker parameter for your line plots, they help to clearly identify the points in the x-y axis instead of getting the user to guess the points.
  • Variety is the spice of life so don’t shy away from adding color to your plots, it’ll keep the project interesting! Thankfully, the color parameter is all you need for the same.
  • Ensure to add plot.show() at the end of all your plots to keep the presentation clean. e.g. cell [22]
  • I noticed this line which I would avoid if this is meant to be a draft for the final. It makes you the analyst look like you are unsure. If you are not sure don’t inlude it.

around october to December the line plot looks zigzagly in nature. Is there anything special about July? Is traffic significantly less heavy in July each year?

Coding Style
  • A suggestion here is to use the simple present tense for the verbs in the comments. I noticed that DQ does it. I’ve taken to doing the same.
Bugs/Inaccuracies
  • After cells [18], [33] you mention strong correlations between traffic volume and elements like temperature which I believe are incorrect. For example the correlation between traffic and temperature is just 0.09, this is considered weak. Something above 0.5 usually would be of some interest albeit with a bit of caution.
  • In cell [31] you present night time traffic for weekends and weekdays. Notice how traffic volume suddenly slumps from 6AM till 8PM. This gives the wrong idea. Obviously this happened because this is night time data. I overcame this issue by using the above mentioned markersto indicate traffic between 12AM-6 AM and 8PM-11PM. Although not the best solution, it still indicates that the line in between has no relevant value.
  • The heading above cell [23] must be corrected to

Analyzing the night time dataset to know the causes of traffic

or you could go for

Night time traffic analysis.

Just a suggestion

Miscellaneous
  • When you come around to improve this later, you could improve the plots by removing the ticks and adding titles to more plots.

That was my 2 cents. Hope it is helpful and keep projecting :rocket: away