Medical expenses — insurance • qacr

Predicting medical expenses

insurance

Format

A data frame with 1338 rows and 7 variables:

age: integer. age of primary beneficiary.
sex: character. insurance contractor gender, female, male.
bmi: double. Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9.
children: integer. Number of children covered by health insurance / Number of dependents.
smoker: character. Smoking (yes, no)
region: character. the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.
charges: double. Individual medical costs billed by health insurance (in dollars).

Source

From Machine Learning in R by Brett Lantz. Data downloaded from GitHub.

Examples

summary(insurance)
#>       age            sex                 bmi           children    
#>  Min.   :18.00   Length:1338        Min.   :15.96   Min.   :0.000  
#>  1st Qu.:27.00   Class :character   1st Qu.:26.30   1st Qu.:0.000  
#>  Median :39.00   Mode  :character   Median :30.40   Median :1.000  
#>  Mean   :39.21                      Mean   :30.66   Mean   :1.095  
#>  3rd Qu.:51.00                      3rd Qu.:34.69   3rd Qu.:2.000  
#>  Max.   :64.00                      Max.   :53.13   Max.   :5.000  
#>     smoker             region             charges     
#>  Length:1338        Length:1338        Min.   : 1122  
#>  Class :character   Class :character   1st Qu.: 4740  
#>  Mode  :character   Mode  :character   Median : 9382  
#>                                        Mean   :13270  
#>                                        3rd Qu.:16640  
#>                                        Max.   :63770