Please Review My Work on the US Medical Insurance Project!

Projects Portfolio Project: U.S. Medical Insurance Costs
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Hello! Thank you for clicking on this topic!

I started the Data Science Path because I want to get some programming experience while I aim for a role in the IT sector. I spent the past week working on this project and figuring out where I wanted to take it. I definitely feel like I could have done more, but I didn’t want to spend too long working on this. I would greatly appreciate seeing any insight that you may have! I’m also very new to GitHub, so please let me know if you see something that doesn’t look quite right.

The link is: GitHub - KaranChauhan00/Codecademy-US-Medical-Portfolio: My version of Codecademy's U.S. Medical Insurance Costs Project

Thank you so much!

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Congratulations on completing the project. You put a lot of work into this.

A few thoughts:

  • it might be a good idea to comment out the print() so people don’t have to look at a wall of text (like you mentioned).

  • When you look at ages not represented in the data: Because insurance is tied to employment (not including Medicare or ACA) and most kids are on peoples’ insurance plans until age ~24, so, you’re not going to see someone who’s under 18 and has insurance.

  • it’s good that you used a histogram to visualize the charges–b/c you’ll see that there are outliers and that would influence the mean. So, it’s better to look at the median.

  • When you say, ‘People in the Southeast tend to pay more. But is that down to the people who live there or is the insurance cost putting them at a disadvantage?’ You’re only showing the numbers of people who are represented from each region, not what they’re paying. (Unless you left something out?)

Ex: if you look at the median charges paid by region in the data, ppl in the dataset in the NE pay the most:

df[['region', 'charges']].groupby('region').median().round(2)
>>	charges
region	
northeast	10057.65
northwest	8965.79
southeast	9294.13
southwest	8798.59
  • It might be a good idea to label the bar charts b/c it’s difficult to read what the numbers are for each bar.

Good work. Keep at it! :partying_face:

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Thank you for all of your comments! I appreciate your insight. The way that insurance worked is new to me and that helps to explain the ages! I see that labelling is also a good idea now too!

You’re absolutely right about this! I don’t know how I missed that!

Thank you for your advice!

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