The Impact of Lifestyle Factors on Health Insurance Premiums
ODEYEMI Bukunmi. S
2026-02-19
About Dataset
This dataset contains detailed information about insurance customers, including their age, sex, body mass index (BMI), number of children, smoking status, region and the insurance charges paid in dollars($). This dataset was gotten from kaggle and the location and time frame was not given.
Aim
To explore and analyze the demographic and health factors that most significantly influence individual medical insurance charges.
Objectives
Investigate how age, Body Mass Index (BMI), number of children, and region relate to charges.
Visualize the difference in charges between smokers and non-smokers.
Explore the relationship between age,BMI,and smoking status.
Loading Necessary Libraries
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.5.2## Warning: package 'ggplot2' was built under R version 4.5.2## Warning: package 'tidyr' was built under R version 4.5.2## Warning: package 'readr' was built under R version 4.5.2## Warning: package 'purrr' was built under R version 4.5.2## Warning: package 'forcats' was built under R version 4.5.2## Warning: package 'lubridate' was built under R version 4.5.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.1 ✔ stringr 1.5.2
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
library(summarytools)## Warning: package 'summarytools' was built under R version 4.5.2##
## Attaching package: 'summarytools'
##
## The following object is masked from 'package:tibble':
##
## viewlibrary(dplyr)Import the data
mydata<- read.csv("C:\\Users\\HP ELITE BOOK\\Desktop\\insurance.csv")
View(mydata)Data cleaning
head(mydata)## index age sex bmi children smoker region charges
## 1 0 19 female 27.900 0 yes southwest 16884.924
## 2 1 18 male 33.770 1 no southeast 1725.552
## 3 2 28 male 33.000 3 no southeast 4449.462
## 4 3 33 male 22.705 0 no northwest 21984.471
## 5 4 32 male 28.880 0 no northwest 3866.855
## 6 5 31 female 25.740 0 no southeast 3756.622str(mydata)## 'data.frame': 1338 obs. of 8 variables:
## $ index : int 0 1 2 3 4 5 6 7 8 9 ...
## $ age : int 19 18 28 33 32 31 46 37 37 60 ...
## $ sex : chr "female" "male" "male" "male" ...
## $ bmi : num 27.9 33.8 33 22.7 28.9 ...
## $ children: int 0 1 3 0 0 0 1 3 2 0 ...
## $ smoker : chr "yes" "no" "no" "no" ...
## $ region : chr "southwest" "southeast" "southeast" "northwest" ...
## $ charges : num 16885 1726 4449 21984 3867 ...#check for missing value
colSums(is.na(mydata))## index age sex bmi children smoker region charges
## 0 0 0 0 0 0 0 0summary(mydata)## index age sex bmi
## Min. : 0.0 Min. :18.00 Length:1338 Min. :15.96
## 1st Qu.: 334.2 1st Qu.:27.00 Class :character 1st Qu.:26.30
## Median : 668.5 Median :39.00 Mode :character Median :30.40
## Mean : 668.5 Mean :39.21 Mean :30.66
## 3rd Qu.:1002.8 3rd Qu.:51.00 3rd Qu.:34.69
## Max. :1337.0 Max. :64.00 Max. :53.13
## children smoker region charges
## Min. :0.000 Length:1338 Length:1338 Min. : 1122
## 1st Qu.:0.000 Class :character Class :character 1st Qu.: 4740
## Median :1.000 Mode :character Mode :character Median : 9382
## Mean :1.095 Mean :13270
## 3rd Qu.:2.000 3rd Qu.:16640
## Max. :5.000 Max. :63770mydata$sex<- factor(x= mydata$sex, levels = c("male","female"))
mydata$smoker<- factor(x=mydata$smoker, levels= c("yes","no"))
mydata$region<- factor(x= mydata$region, levels= c("northeast","northwest","southwest","southeast") )
str(mydata)## 'data.frame': 1338 obs. of 8 variables:
## $ index : int 0 1 2 3 4 5 6 7 8 9 ...
## $ age : int 19 18 28 33 32 31 46 37 37 60 ...
## $ sex : Factor w/ 2 levels "male","female": 2 1 1 1 1 2 2 2 1 2 ...
## $ bmi : num 27.9 33.8 33 22.7 28.9 ...
## $ children: int 0 1 3 0 0 0 1 3 2 0 ...
## $ smoker : Factor w/ 2 levels "yes","no": 1 2 2 2 2 2 2 2 2 2 ...
## $ region : Factor w/ 4 levels "northeast","northwest",..: 3 4 4 2 2 4 4 2 1 2 ...
## $ charges : num 16885 1726 4449 21984 3867 ...table(mydata$sex)##
## male female
## 676 662table(mydata$smoker)##
## yes no
## 274 1064table(mydata$region)##
## northeast northwest southwest southeast
## 324 325 325 364descr(mydata[, c("age", "bmi", "children", "charges")])## Descriptive Statistics
## mydata
## N: 1338
##
## age bmi charges children
## ----------------- --------- --------- ---------- ----------
## Mean 39.21 30.66 13270.42 1.09
## Std.Dev 14.05 6.10 12110.01 1.21
## Min 18.00 15.96 1121.87 0.00
## Q1 27.00 26.29 4738.27 0.00
## Median 39.00 30.40 9382.03 1.00
## Q3 51.00 34.70 16657.72 2.00
## Max 64.00 53.13 63770.43 5.00
## MAD 17.79 6.20 7440.81 1.48
## IQR 24.00 8.40 11899.63 2.00
## CV 0.36 0.20 0.91 1.10
## Skewness 0.06 0.28 1.51 0.94
## SE.Skewness 0.07 0.07 0.07 0.07
## Kurtosis -1.25 -0.06 1.59 0.19
## N.Valid 1338.00 1338.00 1338.00 1338.00
## N 1338.00 1338.00 1338.00 1338.00
## Pct.Valid 100.00 100.00 100.00 100.00Distribution by Sex
sex_count<-mydata%>%
count(sex)
sex_count## sex n
## 1 male 676
## 2 female 662ggplot(sex_count, aes(x=sex,y=n,fill=sex))+
scale_fill_manual(values = c("male" = "blue", "female" = "red"))+
geom_col()+
geom_text(aes(label=n),vjust=-0.2)+
labs(title = "Dstribution by sex",
x = "sex",
y = "count")+
theme_minimal()
1i. Relationship between Age and charges
ggplot(mydata, aes(x = age, y = charges)) +
geom_point(alpha = 0.6, color = "blue") +
geom_smooth(method = "lm", se = FALSE, color = "red") +
labs(title = "Relationship between Age and Charges",
x = "Age", y = "Charges($)")## `geom_smooth()` using formula = 'y ~ x'
As age increases, insurance charges tend to rise, suggesting that older people have higher medical costs. However, even in the same age group, charges vary, which shows that variation is caused by other factors.
ii. Relationship between BMI and charges
ggplot(mydata, aes(x = bmi, y = charges)) +
geom_point(alpha = 0.6, color = "darkgreen") +
geom_smooth(method = "lm", se = FALSE, color = "red") +
labs(title = "Relationship between BMI and Charges",
x = "BMI", y = "Charges($)")## `geom_smooth()` using formula = 'y ~ x'
As BMI charges increases, insurance charges tend to rise. We could also see that people with same BMI have varying charges, but the case is a little different starting from BMI 30 and above, we could see that there is a wide variation in the charges paid from BMI 30 and above(Those in BMI 30 and above are in the obese class).
iii. Relationship between number of children and charges
# Summary statistics
mydata %>%
group_by(children) %>%
summarise(mean_charges = mean(charges),
max_charges= max(charges),
min_charges= min(charges))## # A tibble: 6 × 4
## children mean_charges max_charges min_charges
## <int> <dbl> <dbl> <dbl>
## 1 0 12366. 63770. 1122.
## 2 1 12731. 58571. 1711.
## 3 2 15074. 49578. 2304.
## 4 3 15355. 60021. 3443.
## 5 4 13851. 40182. 4505.
## 6 5 8786. 19023. 4688.ggplot(mydata, aes(x = factor(children), y = charges, fill = factor(children))) +
geom_boxplot() +
labs(title = "Relationship between number of
children and charges ",
x = "Number of Children",
y = "Charges($)") +
theme_minimal()
This box plot shows the distribution of charges across different family sizes. Unlike the clear upward trend we see with BMI, the relationship here is not linear. We can see that the median charge remains relatively stable regardless of whether a person has 0, 1, 2, 3, or more children.
iv. Relationship between Region and charges
# Summary statistics
library(gt)## Warning: package 'gt' was built under R version 4.5.2mydata %>%
group_by(region) %>%
summarise(mean_charges = mean(charges),
max_charges= max(charges),
min_charges= min(charges))## # A tibble: 4 × 4
## region mean_charges max_charges min_charges
## <fct> <dbl> <dbl> <dbl>
## 1 northeast 13406. 58571. 1695.
## 2 northwest 12418. 60021. 1621.
## 3 southwest 12347. 52591. 1242.
## 4 southeast 14735. 63770. 1122.ggplot(mydata, aes(x = region, y = charges, fill = region)) +
geom_boxplot() +
labs(title = "Charges by Region", x = "Region", y = "Charges($)")
The box plot shows that the distribution of charges is relatively consistent across all four regions, given that the median is closely related. The Southeast region displays a slightly higher median, but there is no distinct high cost.
2. Relationship between Smoking status and charges
# Summary statistics
mydata %>%
group_by(smoker) %>%
summarise(mean_charges = mean(charges),
max_charges= max(charges),
min_charges= min(charges))## # A tibble: 2 × 4
## smoker mean_charges max_charges min_charges
## <fct> <dbl> <dbl> <dbl>
## 1 yes 32050. 63770. 12829.
## 2 no 8434. 36911. 1122.ggplot(mydata, aes(x = smoker, y = charges, fill = smoker)) +
geom_boxplot() +
labs(title = "Charges by Smoking Status", x = "Smoker", y = "Charges($)")
Unlike the Region or Children charts, where the boxes overlapped, here we see a complete separation. The median cost for smokers is drastically higher, and the entire distribution is shifted upwards
average_charges <- mydata %>%
group_by(smoker) %>%
summarise(mean_charges = mean(charges))
ggplot(average_charges, aes(x = smoker, y = mean_charges, fill = smoker)) +
geom_col() +
labs(title = "Average Charges by Smoker Status", x = "Smoker", y = "Average Charges($)") +
theme_minimal()
Also,when we look at the average charges of smokers compared to non-smokers, it is observed that there is a big difference.
3. Age vs Charges (Smoker vs Non-Smoker)
ggplot(mydata, aes(x = age, y = charges, color = smoker)) +
geom_point(alpha = 0.9) +
labs(title = "Age vs Charges (Smoker vs Non-Smoker)", x = "age", y = "Charges($)")
Since those in the same age group have varying charges, I decided to further check the age against charges, putting into consideration smoking status. Smokers consistently occupy a much higher price than non-smokers of the same age
BMI vs Charges (Smoker vs Non-Smoker)
ggplot(mydata, aes(x = bmi, y = charges, color = smoker)) +
geom_point(alpha = 0.9) +
labs(title = "BMI vs Charges (Smoker vs Non-Smoker)", x = "BMI", y = "Charges($)")
I did the same with BMI since those in the same BMI group have varying charges. I also checked the BMI against charges taking into consideration smoking status, and it is very obvious that those who pay high in the same BMI group are the smokers, and the charges were extremely high for obese (BMI>= 30)people who are smokers.
Conclusion
In conclusion, it is observed that there is minimal variation in insurance premiums across different regions or family sizes. Conversely, the highest charges are consistently incurred by individuals who smoke, possess a high Body Mass Index (obese), and are advanced in age.