About the Portfolio Project: U.S. Medical Insurance Costs category

dres_code · March 16, 2023, 7:20pm

hi
I started this yesterday and had a tough time getting going not working on the Codecademy website, I ended up using Jupyter notebook, please any feedback as to where I can work, I do have visual code installed but I have a hard time operating it.

GitHub ill also have to put more hours into…

Nevertheless I got it done I ended up using pandas to import the data, I also included the “import csv” function at the bottom: GitHub - dredomecode/medical_insurance_final_ish

Any feedback on the code is appreciated; I am severely new at coding and could use any inputs.
I am 58% through the data science foundation path.

py3200829472 · April 15, 2023, 5:54am

This was my first project that I’ve done so far without having to look at any solutions. I am pretty proud of this milestone. Would appreciate any feedback.

gist.github.com

https://gist.github.com/cheycodes23/6c6089fd8d52a4cc7ab8625a8c341d3f

Python Dictionaries: Medical Insurance Project

#1
medical_costs = {}
#2
medical_costs["Marina"] = 6607.0
medical_costs["Vinay"] = 3225.0
#3
medical_costs.update({"Connie": 8888.6, "Isaac": 16444.0, "Valentina": 6420.0})
#4
print(medical_costs)
#5

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coachhuma · May 7, 2023, 9:12pm

my code is here: GitHub - coachhuma/huma
please review if you would like.
Thank you smiley:

you-shanchung · May 9, 2023, 7:45am

Hi all,

I wonder what should I do to let the website know I’ve finished the project so that I can continue?

Thanks a lot!

makkprogv2.0 · May 16, 2023, 12:28am

Hello everyone,
the experience was fun, although was kind of tiring as I started working things on my own without any guidance and to check what I want to find and how is it going to be beneficial and what should I do
it took several hours at least and I wanted to make it as neat as possible but it is kind of messy. But for a first portfolio. It was a nice experience overall
This is the link to the code in my repository

philippschramm900272 · May 19, 2023, 12:40pm

Hello there,

this exercise was challenging but also quite rewarding. Realised how much easier analysis during uni years could have been. The project had a span over couple of days and also included some significant trial and error. One challenge I encountered was trying to create a regional map of the US where the regions are heat mapped according to their average medical costs. I experimented with shp files. But did not manage to implement it, therefore I did not include it in my code example.
Feel free to have a look at my GitHub repository. , where I collect all my Codecademy Portfolio Projects.

Feedback is always welcome!

theredknight2023 · May 22, 2023, 1:46pm

Hi, I want your Feedback. Am I missing something? What needs improvement?

U.S. Medical Insurance GitHub Repository:

github.com

13ouncyKnight/US_Medical_Insurance_Portfolio_Project/blob/main/us-medical-insurance-costs(Saved).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# U.S. Medical Insurance Costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this Porject, we are given a csv file **insurance.csv** that contains the dataset we will be working on for our analysis. The first step is to import the data from csv into our notebook file (ipnyb). <br> \n",
    "\n",
    "*See code below for importing, reading and iterating through the csv file to store values into variables.*"
   ]
  },
  {
   "cell_type": "code",

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adeizaazeez · May 22, 2023, 2:58pm

Hi,
I have uploaded my code using the link below.
Your feedback will be highly appreciated.
Thank you.

javarockstar50210 · May 24, 2023, 6:53pm

github.com

DDomogala3/Codeacademy/blob/main/us-medical-insurance-costs_DD_edit_0524_1336.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },

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Above is the link to my notebook.
I enjoyed the exercise, I applied a linear regression, although I hope I did it the correct way.
This is a very clean dataset, many groups are evenly distributed.

I tried to write a brief summary for every result.
Please feel free to give feedback.

javarockstar50210 · May 24, 2023, 6:59pm

I like how you annotated your results, you were very thorough.
I used the statistics package in python.
Feel free to look through my notebook.
Great job

javarockstar50210 · May 24, 2023, 7:07pm

I like your code, I would try and use a Jupyer notebook or you can use Jupyter lite.

It’s a much better environment for development.

I had trouble downloading jupyter on my work station.

theredknight2023 · May 25, 2023, 6:12am

I need more work. I am working with a person who have way more advanced knowledge than me. I’m just about to start Pandas to catch up. Maybe, redo all projects once I know more libraries.

javarockstar50210 · June 1, 2023, 5:17pm

Hi, I created a dictionary and then appended it to a list. Hope this helps.
See here.

age_list = [] insurance_dict = {} numerical_id_list = [] insurance_list = [] with open("insurance.csv",newline = "") as insurance_data: reader = csv.DictReader(insurance_data) for row in reader: #print(row['age']) #numerical_ID = range(len( age = row['age'] # numerical_ID = len(row["age"]) sex = row['sex'] children = row['children'] BMI = row["bmi"] smoker = row["smoker"] region = row["region"] charges = row["charges"] #print(numerical_ID) #print(sex) #print(children) age_list.append(int(age)) numerical_ID_len = len(age_list) insurance_dict = {"age":int(age),"sex":sex, "children":int(children),"BMI": float(BMI),"smoker": smoker,"region": region,"charges": float(charges)} #insurance_dict[age] = {"age":int(age),"sex":sex, "children":int(children),"BMI": float(BMI),"smoker": smoker,"region": region,"charges": float(charges)} insurance_list.append(insurance_dict)

josefroslan · July 8, 2023, 3:52am

Hello World

Here is my solution to the US Medical Insurance Cost

github.com

josefroslan/usmedicalinsurancecost/blob/main/us-medical-insurance-costs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# U.S. Medical Insurance Costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# U.S Medical Insurance Costs Analysis Project\n",
    "\n",
    "Hello,\n",
    "\n",
    "My name is Josef and I am going to analyze a real dataset of U.S. Medical Insurance Costs provided by Codecademy.\n",
    "\n",
    "Let us begin!\n",

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Appreciate any feedback!

linhcly · August 9, 2023, 7:11pm

Hi all!

This is my solution. This took me a day, good recap exercise all everything we’ve learned on dictionary.
Please let me know if anybody have trouble seeing my file.

github.com

linhcly/codecademy/blob/main/portfolio-projects/US-medical-insurance/medical_analysis.py

import csv
smoker = []
sex = []
age = []
children = []
charges = []
region = []
bmi = []
insurance_data = []

with open('insurance.csv') as insurance_csv:
    insurance_reader = csv.DictReader(insurance_csv,delimiter=',')
    for row in insurance_reader:
        smoker.append(row['smoker'])
        sex.append(row['sex'])
        age.append(row['age'])
        children.append(row['children'])
        charges.append(row['charges'])
        region.append(row['region'])
        bmi.append(row['bmi'])

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array3795274618 · September 8, 2023, 12:20pm

Hi @linhcly
I’ve checked the code and, despite being a beginner coder, it all looks correct.
It seems you solved the whole analysis with lists and haven’t used any dictionary, maybe you can add (just for a matter of practice) some further using them.

If possible have a look at my solution and tell me what you think.

gist.github.com

https://gist.github.com/Piricuchito/b46d4b43f9c86fca21e275620d725d74

gistfile1.py

# import the module to work with .cvs files
import csv 

# having looked at the file I see 7 columns; age,sex,bmi,children,smoker,region,charges so I'll create
# empty list for each catergory so I can later add/append the data from the file to each list
age = []
sex = []
bmi = []
children = []
smoker = []

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Thanks in advance!

micro7016923991 · September 9, 2023, 10:35pm

Hello there, this is my solution

https://github.com/SGB996/CodeCademy-Portfolio

micro7016923991 · September 9, 2023, 10:36pm

Nice solution. Aftewards using pandas python will be much easy to analyse the data