healthcare cost

Part 1: The Real World Problem

Healthcare Insurance charges have dramatically increased, especially since COVID-19 . There are a few known factors that may influence how much insurance costs, such whether an individual receives insurance through their job via corporate insurance or if they utilize government healthcare programs such as Medicare. But are there other individual factors to blame for higher insurance prices? Things we may not have control over -such as sex, gender, and BMI- may be the reason you may pay more for healthcare insurance in the future compared to your fellow classmates.

There is existing evidence that such factors can influence insurance charges. It makes sense that medical charges increase with age, as the severity of many medical conditions worsen with time and require more intensive care. Additionally, a higher BMI is medically linked to higher obesity rates, which ultimately lead to more health complications and higher medical bills). However, exactly how influential are these factors on insurance prices and is their influence reasonable?

Part 2: Loading the Dataset

import pandas as pd

# opening file 
df_insurance = pd.read_csv('insurance.csv')
df_insurance

	index	age	sex	bmi	children	smoker	region	charges
0	0	19	female	27.900	0	yes	southwest	16884.92400
1	1	18	male	33.770	1	no	southeast	1725.55230
2	2	28	male	33.000	3	no	southeast	4449.46200
3	3	33	male	22.705	0	no	northwest	21984.47061
4	4	32	male	28.880	0	no	northwest	3866.85520
...	...	...	...	...	...	...	...	...
1333	1333	50	male	30.970	3	no	northwest	10600.54830
1334	1334	18	female	31.920	0	no	northeast	2205.98080
1335	1335	18	female	36.850	0	no	southeast	1629.83350
1336	1336	21	female	25.800	0	no	southwest	2007.94500
1337	1337	61	female	29.070	0	yes	northwest	29141.36030

1338 rows × 8 columns

insurance_dict = {'age': 'age of patient', 'sex': 'gender of patient', 'bmi': 'body mass index of patient', 'children':'number of children patient has', 'smoker': 'indicates whether patient is a smoker', 'region': 'where in the United States a patient lives', 'charges': 'amount of money insurance is charging patient for their services'}

insurance_dict

{'age': 'age of patient',
 'sex': 'gender of patient',
 'bmi': 'body mass index of patient',
 'children': 'number of children patient has',
 'smoker': 'indicates whether patient is a smoker',
 'region': 'where in the United States a patient lives',
 'charges': 'amount of money insurance is charging patient for their services'}

Part 3: Explanation of Project Methods

In order to find out the influence of individual factors on healthcare insurance prices, we will group the data together by factor and analyze the charges associated with each respective factor. In doing so, we can easily discover which individual factors have the highest influence on healthcare pricing and then compare these trends across all factors.

In the grand scheme of the project, one could utilize this findings of this project to help create a predictor of healthcare prices based on the factors presented. This could be extremely useful for both patients and doctors when deciding the best course of action to take when in medical need.