day 7 hw

remove(list=ls())

library(visdat)
library(psych)
library(caret)

Loading required package: ggplot2

Attaching package: 'ggplot2'

The following objects are masked from 'package:psych':

    %+%, alpha

Loading required package: lattice

df <- read.csv("~/Downloads/Day 7/car_crashes/insurance-testing-data2.csv", row.names=1)
vis_dat(df)

str(df)

'data.frame':   1633 obs. of  25 variables:
 $ TARGET_FLAG: int  0 0 1 0 0 0 1 0 1 1 ...
 $ TARGET_AMT : num  0 0 3627 0 0 ...
 $ KIDSDRIV   : int  0 0 0 0 0 0 0 0 1 0 ...
 $ AGE        : int  51 54 43 52 38 47 40 56 45 33 ...
 $ HOMEKIDS   : int  0 0 0 0 0 0 0 0 1 4 ...
 $ YOJ        : int  14 NA 13 8 11 8 11 16 14 12 ...
 $ INCOME     : chr  "" "$18,755" "$37,214" "$51,278" ...
 $ PARENT1    : chr  "No" "No" "No" "No" ...
 $ HOME_VAL   : chr  "$306,251" "" "" "$230,340" ...
 $ MSTATUS    : chr  "Yes" "Yes" "Yes" "Yes" ...
 $ SEX        : chr  "M" "z_F" "M" "z_F" ...
 $ EDUCATION  : chr  "<High School" "<High School" "<High School" "Bachelors" ...
 $ JOB        : chr  "z_Blue Collar" "z_Blue Collar" "z_Blue Collar" "Professional" ...
 $ TRAVTIME   : int  32 33 52 37 47 35 20 30 50 46 ...
 $ CAR_USE    : chr  "Private" "Private" "Commercial" "Private" ...
 $ BLUEBOOK   : chr  "$15,440" "$8,780" "$26,560" "$1,500" ...
 $ TIF        : int  7 1 1 4 1 6 4 13 6 13 ...
 $ CAR_TYPE   : chr  "Minivan" "z_SUV" "Panel Truck" "z_SUV" ...
 $ RED_CAR    : chr  "yes" "no" "yes" "no" ...
 $ OLDCLAIM   : chr  "$0" "$0" "$0" "$0" ...
 $ CLM_FREQ   : int  0 0 0 0 0 2 1 0 2 3 ...
 $ REVOKED    : chr  "No" "No" "No" "No" ...
 $ MVR_PTS    : int  0 0 3 1 2 5 13 0 0 0 ...
 $ CAR_AGE    : int  6 1 1 10 9 NA 6 6 13 1 ...
 $ URBANICITY : chr  "Highly Urban/ Urban" "Highly Urban/ Urban" "Highly Urban/ Urban" "Highly Urban/ Urban" ...