K mean clustering Insurance

dataQ1 <- read.csv("C:\\Users\\Harisha\\Desktop\\Datascience Assignments\\K mean clustering\\Insurance Dataset.csv")

Data <- scale(dataQ1)

View(Data)


plot(Data)

text(Data,plot(Data),rownames(Data))

km <- kmeans(Data,4) #kmeans clustering
str(km)

## List of 9
##  $ cluster     : int [1:100] 2 1 1 1 1 1 1 1 1 2 ...
##  $ centers     : num [1:4, 1:5] -0.781 -0.512 1.286 0.765 -0.598 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:4] "1" "2" "3" "4"
##   .. ..$ : chr [1:5] "Premiums.Paid" "Age" "Days.to.Renew" "Claims.made" ...
##  $ totss       : num 495
##  $ withinss    : num [1:4] 42 45.9 34.2 75.5
##  $ tot.withinss: num 198
##  $ betweenss   : num 297
##  $ size        : int [1:4] 36 20 9 35
##  $ iter        : int 4
##  $ ifault      : int 0
##  - attr(*, "class")= chr "kmeans"

km$centers

##   Premiums.Paid         Age Days.to.Renew   Claims.made     Income
## 1    -0.7806637 -0.59797374    -0.6663706 -0.5973153393 -0.7659800
## 2    -0.5124264 -0.04392395     1.1651707 -0.0003423441 -0.5980389
## 3     1.2863898  0.74414687     1.6926142  2.5400484134  1.0244982
## 4     0.7649975  0.44880605    -0.4156457 -0.0385781892  0.8661592

km$cluster

##   [1] 2 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2
##  [36] 3 2 2 2 1 4 1 1 1 1 4 1 2 2 2 2 1 1 1 1 4 4 4 4 1 4 4 1 4 4 4 4 4 3 2
##  [71] 2 3 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 4 4 4 4 4 4 4 4 4 4 3 3

finalmodel <- data.frame(km$cluster,dataQ1)

View(finalmodel)

x <- aggregate(dataQ1[,1:5],by=list(km$cluster),FUN=mean)

x

##   Group.1 Premiums.Paid      Age Days.to.Renew Claims.made    Income
## 1       1      7240.972 37.80556      61.72222    4398.218  68916.67
## 2       2      9062.500 45.50000     223.00000   12574.305  76225.00
## 3       3     21277.778 56.44444     269.44444   47367.260 146833.33
## 4       4     17737.143 52.34286      83.80000   12050.630 139942.86