age nr.employed job marital education housing default emp.var.rate cons.price.idx cons.conf.idx
1 30 5099.1 blue-collar married basic.9y yes no -1.8 92.893 -46.2
2 39 5191.0 services single high.school no no 1.1 93.994 -36.4
3 25 5228.1 services married high.school yes no 1.4 94.465 -41.8
4 38 5228.1 services married basic.9y unknown no 1.4 94.465 -41.8
5 47 5195.8 admin. married university.degree yes no -0.1 93.200 -42.0
6 32 4963.6 services single university.degree no no -1.1 94.199 -37.5
7 32 4963.6 admin. single university.degree yes no -1.1 94.199 -37.5
8 41 5195.8 entrepreneur married university.degree yes unknown -0.1 93.200 -42.0
9 31 5195.8 services divorced professional.course no no -0.1 93.200 -42.0
10 35 5191.0 blue-collar married basic.9y no unknown 1.1 93.994 -36.4
Remove from the data frame survey
all observations that contain NA
values for housing
, education
and job
. Store the result in a data frame called survey_cleaned
.