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Project 2 - EDA on Vehicle Insurance Customer Data

KKothapally Ravinder5 Mar 2022

import pandas as pd

In [92]:
df = pd.DataFrame(columns=['customer_id','Gender','Age','Driving license present','Region code','previously insured','vehicle Age','vehicle Damage'])
In [93]:
df
Out[93]:
customer_id Gender Age Driving license present Region code previously insured vehicle Age vehicle Damage
In [94]:
# Column Name for customer_policy table:
df1 = pd.DataFrame(columns = ['customer_id','annual premium(in RS)','sales channel code','vintage and response'])
In [95]:
df1
Out[95]:
customer_id annual premium(in RS) sales channel code vintage and response
In [96]:
# Loading data from files into DataFrames
​
df = pd.read_csv('Downloads\EDA on V I C D.csv')
In [97]:
df
Out[97]:
Customer_id Gender Age driving license present region code previously insured vehicle age vehicle Damage
0 1 Male 44 1 28 0 > 2 Years Yes
1 2 Male 76 1 3 0 1-2 Year No
2 3 Male 47 1 28 0 > 2 Years Yes
3 4 Male 21 1 11 1 < 1 Year No
4 5 Female 29 1 41 1 < 1 Year No
5 6 Female 24 1 33 0 < 1 Year Yes
6 7 Male 23 1 11 0 < 1 Year Yes
7 8 Female 56 1 28 0 1-2 Year Yes
8 9 Female 24 1 3 1 < 1 Year No
9 10 Female 32 1 6 1 < 1 Year No
10 11 Female 47 1 35 0 1-2 Year Yes
11 12 Female 24 1 50 1 < 1 Year No
12 13 Female 41 1 15 1 1-2 Year No
13 14 Male 76 1 28 0 1-2 Year Yes
14 15 Male 71 1 28 1 1-2 Year No
15 16 Male 37 1 6 0 1-2 Year Yes
16 17 Female 25 1 45 0 < 1 Year Yes
17 18 Female 25 1 35 1 < 1 Year No
18 19 Male 42 1 28 0 1-2 Year Yes
19 20 Female 60 1 33 0 1-2 Year Yes
20 21 Male 65 1 28 0 1-2 Year Yes
21 22 Male 49 1 28 0 1-2 Year Yes
22 23 Male 23 1 50 1 < 1 Year No
23 24 Male 44 1 28 0 1-2 Year Yes
24 25 Male 34 1 15 1 1-2 Year No
25 26 Female 21 1 28 1 < 1 Year No
26 27 Female 51 1 28 0 1-2 Year Yes
27 28 Male 26 1 6 1 < 1 Year No
28 29 Female 25 1 8 1 < 1 Year No
29 30 Male 57 1 11 0 1-2 Year Yes
30 31 Female 26 1 8 0 < 1 Year No
31 32 Male 79 1 28 0 1-2 Year Yes
32 33 Female 48 1 28 0 1-2 Year Yes
33 34 Female 24 1 36 0 < 1 Year Yes
34 35 Female 32 1 30 1 < 1 Year No
35 36 Male 41 1 36 1 1-2 Year No
36 37 Male 71 1 8 1 1-2 Year No
37 38 Female 25 1 28 1 < 1 Year No
38 39 Male 45 1 8 0 1-2 Year Yes
39 40 Male 72 1 41 1 1-2 Year

No

 
df1 = pd.read_csv('Downloads\EDA on V I C D 1.csv')
In [99]:
df1
Out[99]:
Customer_id annual premium (inRs) sales channel code vintage response
0 0 1 2 3 4
1 1 40454 26 217 1
2 2 33536 26 183 0
3 3 38294 26 27 1
4 4 28619 152 203 0
5 5 27496 152 39 0
6 6 2630 160 176 0
7 7 23367 152 249 0
8 8 32031 26 72 1
9 9 27619 152 28 0
10 10 28771 152 80 0
11 11 47576 124 46 1
12 12 48699 152 289 0
13 13 31409 14 221 0
14 14 36770 13 15 0
15 15 46818 30 58 0
16 16 2630 156 147 1
17 17 26218 160 256 0
18 18 46622 152 299 0
19 19 33667 124 158 0
20 20 32363 124 102 1
21 21 41184 124 116 0
22 22 50791 124 177 0
23 23 45283 152 232 0
24 24 41852 163 60 0
25 25 38111 152 180 0
26 26 61964 152 72 0
27 27 38341 124 49 1
28 28 19135 152 57 0
29 29 27946 152 223 0
30 30 25679 157 232 1
31 31 2630 160 136 0
32 32 57444 26 222 0
33 33 36951 122 149 0
34 34 43129 152 49 0
35 35 27638 152 169 0
36 36 30039 124 88 0
37 37 40903 19 253 0
38 38 76251 152 107 0
39 39 42297 124 264 0
40 40 32210 152 233 0

df.isnull()

Customer_id Gender Age driving license present region code previously insured vehicle age vehicle Damage
0 False False False False False False False False
1 False False False False False False False False
2 False False False False False False False False
3 False False False False False False False False
4 False False False False False False False False
5 False False False False False False False False
6 False False False False False False False False
7 False False False False False False False False
8 False False False False False False False False
9 False False False False False False False False
10 False False False False False False False False
11 False False False False False False False False
12 False False False False False False False False
13 False False False False False False False False
14 False False False False False False False False
15 False False False False False False False False
16 False False False False False False False False
17 False False False False False False False False
18 False False False False False False False False
19 False False False False False False False False
20 False False False False False False False False
21 False False False False False False False False
22 False False False False False False False False
23 False False False False False False False False
24 False False False False False False False False
25 False False False False False False False False
26 False False False False False False False False
27 False False False False False False False False
28 False False False False False False False False
29 False False False False False False False False
30 False False False False False False False False
31 False False False False False False False False
32 False False False False False False False False
33 False False False False False False False False
34 False False False False False False False False
35 False False False False False False False False
36 False False False False False False False False
37 False False False False False False False False
38 False False False False False False False False
39 False False False False False False False False
In [101]:
df["Customer_id"].fillna(df["Customer_id"].mean())
Out[101]:
0      1
1      2
2      3
3      4
4      5
5      6
6      7
7      8
8      9
9     10
10    11
11    12
12    13
13    14
14    15
15    16
16    17
17    18
18    19
19    20
20    21
21    22
22    23
23    24
24    25
25    26
26    27
27    28
28    29
29    30
30    31
31    32
32    33
33    34
34    35
35    36
36    37
37    38
38    39
39    40
Name: Customer_id, dtype: int64
In [102]:
df1.isnull()
Out[102]:
Customer_id annual premium (inRs) sales channel code vintage response
0 False False False False False
1 False False False False False
2 False False False False False
3 False False False False False
4 False False False False False
5 False False False False False
6 False False False False False
7 False False False False False
8 False False False False False
9 False False False False False
10 False False False False False
11 False False False False False
12 False False False False False
13 False False False False False
14 False False False False False
15 False False False False False
16 False False False False False
17 False False False False False
18 False False False False False
19 False False False False False
20 False False False False False
21 False False False False False
22 False False False False False
23 False False False False False
24 False False False False False
25 False False False False False
26 False False False False False
27 False False False False False
28 False False False False False
29 False False False False False
30 False False False False False
31 False False False False False
32 False False False False False
33 False False False False False
34 False False False False False
35 False False False False False
36 False False False False False
37 False False False False False
38 False False False False False
39 False False False False False
40 False False False False False
In [103]:
df1["Customer_id"].fillna(df1["Customer_id"].mean())
Out[103]:
0      0
1      1
2      2
3      3
4      4
5      5
6      6
7      7
8      8
9      9
10    10
11    11
12    12
13    13
14    14
15    15
16    16
17    17
18    18
19    19
20    20
21    21
22    22
23    23
24    24
25    25
26    26
27    27
28    28
29    29
30    30
31    31
32    32
33    33
34    34
35    35
36    36
37    37
38    38
39    39
40    40
Name: Customer_id, dtype: int64
In [105]:
df1.dropna()
Out[105]:
Customer_id annual premium (inRs) sales channel code vintage response
0 0 1 2 3 4
1 1 40454 26 217 1
2 2 33536 26 183 0
3 3 38294 26 27 1
4 4 28619 152 203 0
5 5 27496 152 39 0
6 6 2630 160 176 0
7 7 23367 152 249 0
8 8 32031 26 72 1
9 9 27619 152 28 0
10 10 28771 152 80 0
11 11 47576 124 46 1
12 12 48699 152 289 0
13 13 31409 14 221 0
14 14 36770 13 15 0
15 15 46818 30 58 0
16 16 2630 156 147 1
17 17 26218 160 256 0
18 18 46622 152 299 0
19 19 33667 124 158 0
20 20 32363 124 102 1
21 21 41184 124 116 0
22 22 50791 124 177 0
23 23 45283 152 232 0
24 24 41852 163 60 0
25 25 38111 152 180 0
26 26 61964 152 72 0
27 27 38341 124 49 1
28 28 19135 152 57 0
29 29 27946 152 223 0
30 30 25679 157 232 1
31 31 2630 160 136 0
32 32 57444 26 222 0
33 33 36951 122 149 0
34 34 43129 152 49 0
35 35 27638 152 169 0
36 36 30039 124 88 0
37 37 40903 19 253 0
38 38 76251 152 107 0
39 39 42297 124 264 0
40 40 32210 152 233 0
In [106]:
df1.dropna(axis=1)
Out[106]:
Customer_id annual premium (inRs) sales channel code vintage response
0 0 1 2 3 4
1 1 40454 26 217 1
2 2 33536 26 183 0
3 3 38294 26 27 1
4 4 28619 152 203 0
5 5 27496 152 39 0
6 6 2630 160 176 0
7 7 23367 152 249 0
8 8 32031 26 72 1
9 9 27619 152 28 0
10 10 28771 152 80 0
11 11 47576 124 46 1
12 12 48699 152 289 0
13 13 31409 14 221 0
14 14 36770 13 15 0
15 15 46818 30 58 0
16 16 2630 156 147 1
17 17 26218 160 256 0
18 18 46622 152 299 0
19 19 33667 124 158 0
20 20 32363 124 102 1
21 21 41184 124 116 0
22 22 50791 124 177 0
23 23 45283 152 232 0
24 24 41852 163 60 0
25 25 38111 152 180 0
26 26 61964 152 72 0
27 27 38341 124 49 1
28 28 19135 152 57 0
29 29 27946 152 223 0
30 30 25679 157 232 1
31 31 2630 160 136 0
32 32 57444 26 222 0
33 33 36951 122 149 0
34 34 43129 152 49 0
35 35 27638 152 169 0
36 36 30039 124 88 0
37 37 40903 19 253 0
38 38 76251 152 107 0
39 39 42297 124 264 0
40 40 32210 152 233 0
In [107]:
df.dropna()
Out[107]:
Customer_id Gender Age driving license present region code previously insured vehicle age vehicle Damage
0 1 Male 44 1 28 0 > 2 Years Yes
1 2 Male 76 1 3 0 1-2 Year No
2 3 Male 47 1 28 0 > 2 Years Yes
3 4 Male 21 1 11 1 < 1 Year No
4 5 Female 29 1 41 1 < 1 Year No
5 6 Female 24 1 33 0 < 1 Year Yes
6 7 Male 23 1 11 0 < 1 Year Yes
7 8 Female 56 1 28 0 1-2 Year Yes
8 9 Female 24 1 3 1 < 1 Year No
9 10 Female 32 1 6 1 < 1 Year No
10 11 Female 47 1 35 0 1-2 Year Yes
11 12 Female 24 1 50 1 < 1 Year No
12 13 Female 41 1 15 1 1-2 Year No
13 14 Male 76 1 28 0 1-2 Year Yes
14 15 Male 71 1 28 1 1-2 Year No
15 16 Male 37 1 6 0 1-2 Year Yes
16 17 Female 25 1 45 0 < 1 Year Yes
17 18 Female 25 1 35 1 < 1 Year No
18 19 Male 42 1 28 0 1-2 Year Yes
19 20 Female 60 1 33 0 1-2 Year Yes
20 21 Male 65 1 28 0 1-2 Year Yes
21 22 Male 49 1 28 0 1-2 Year Yes
22 23 Male 23 1 50 1 < 1 Year No
23 24 Male 44 1 28 0 1-2 Year Yes
24 25 Male 34 1 15 1 1-2 Year No
25 26 Female 21 1 28 1 < 1 Year No
26 27 Female 51 1 28 0 1-2 Year Yes
27 28 Male 26 1 6 1 < 1 Year No
28 29 Female 25 1 8 1 < 1 Year No
29 30 Male 57 1 11 0 1-2 Year Yes
30 31 Female 26 1 8 0 < 1 Year No
31 32 Male 79 1 28 0 1-2 Year Yes
32 33 Female 48 1 28 0 1-2 Year Yes
33 34 Female 24 1 36 0 < 1 Year Yes
34 35 Female 32 1 30 1 < 1 Year No
35 36 Male 41 1 36 1 1-2 Year No
36 37 Male 71 1 8 1 1-2 Year No
37 38 Female 25 1 28 1 < 1 Year No
38 39 Male 45 1 8 0 1-2 Year Yes
39 40 Male 72 1 41 1 1-2 Year No
In [108]:
df1.dropna(axis=1)
Out[108]:
Customer_id annual premium (inRs) sales channel code vintage response
0 0 1 2 3 4
1 1 40454 26 217 1
2 2 33536 26 183 0
3 3 38294 26 27 1
4 4 28619 152 203 0
5 5 27496 152 39 0
6 6 2630 160 176 0
7 7 23367 152 249 0
8 8 32031 26 72 1
9 9 27619 152 28 0
10 10 28771 152 80 0
11 11 47576 124 46 1
12 12 48699 152 289 0
13 13 31409 14 221 0
14 14 36770 13 15 0
15 15 46818 30 58 0
16 16 2630 156 147 1
17 17 26218 160 256 0
18 18 46622 152 299 0
19 19 33667 124 158 0
20 20 32363 124 102 1
21 21 41184 124 116 0
22 22 50791 124 177 0
23 23 45283 152 232 0
24 24 41852 163 60 0
25 25 38111 152 180 0
26 26 61964 152 72 0
27 27 38341 124 49 1
28 28 19135 152 57 0
29 29 27946 152 223 0
30 30 25679 157 232 1
31 31 2630 160 136 0
32 32 57444 26 222 0
33 33 36951 122 149 0
34 34 43129 152 49 0
35 35 27638 152 169 0
36 36 30039 124 88 0
37 37 40903 19 253 0
38 38 76251 152 107 0
39 39 42297 124 264 0
40 40 32210 152 233 0

catCols = [col for col in df.columns if df[col].dtype=="O"]

# merging the dataframes
df

Customer_id Gender Age driving license present region code previously insured vehicle age vehicle Damage
0 1 Male 44 1 28 0 > 2 Years Yes
1 2 Male 76 1 3 0 1-2 Year No
2 3 Male 47 1 28 0 > 2 Years Yes
3 4 Male 21 1 11 1 < 1 Year No
4 5 Female 29 1 41 1 < 1 Year No
5 6 Female 24 1 33 0 < 1 Year Yes
6 7 Male 23 1 11 0 < 1 Year Yes
7 8 Female 56 1 28 0 1-2 Year Yes
8 9 Female 24 1 3 1 < 1 Year No
9 10 Female 32 1 6 1 < 1 Year No
10 11 Female 47 1 35 0 1-2 Year Yes
11 12 Female 24 1 50 1 < 1 Year No
12 13 Female 41 1 15 1 1-2 Year No
13 14 Male 76 1 28 0 1-2 Year Yes
14 15 Male 71 1 28 1 1-2 Year No
15 16 Male 37 1 6 0 1-2 Year Yes
16 17 Female 25 1 45 0 < 1 Year Yes
17 18 Female 25 1 35 1 < 1 Year No
18 19 Male 42 1 28 0 1-2 Year Yes
19 20 Female 60 1 33 0 1-2 Year Yes
20 21 Male 65 1 28 0 1-2 Year Yes
21 22 Male 49 1 28 0 1-2 Year Yes
22 23 Male 23 1 50 1 < 1 Year No
23 24 Male 44 1 28 0 1-2 Year Yes
24 25 Male 34 1 15 1 1-2 Year No
25 26 Female 21 1 28 1 < 1 Year No
26 27 Female 51 1 28 0 1-2 Year Yes
27 28 Male 26 1 6 1 < 1 Year No
28 29 Female 25 1 8 1 < 1 Year No
29 30 Male 57 1 11 0 1-2 Year Yes
30 31 Female 26 1 8 0 < 1 Year No
31 32 Male 79 1 28 0 1-2 Year Yes
32 33 Female 48 1 28 0 1-2 Year Yes
33 34 Female 24 1 36 0 < 1 Year Yes
34 35 Female 32 1 30 1 < 1 Year No
35 36 Male 41 1 36 1 1-2 Year No
36 37 Male 71 1 8 1 1-2 Year No
37 38 Female 25 1 28 1 < 1 Year No
38 39 Male 45 1 8 0 1-2 Year Yes
39 40 Male 72 1 41 1 1-2 Year No
In [114]:
 
df1
Out[114]:
Customer_id annual premium (inRs) sales channel code vintage response
0 0 1 2 3 4
1 1 40454 26 217 1
2 2 33536 26 183 0
3 3 38294 26 27 1
4 4 28619 152 203 0
5 5 27496 152 39 0
6 6 2630 160 176 0
7 7 23367 152 249 0
8 8 32031 26 72 1
9 9 27619 152 28 0
10 10 28771 152 80 0
11 11 47576 124 46 1
12 12 48699 152 289 0
13 13 31409 14 221 0
14 14 36770 13 15 0
15 15 46818 30 58 0
16 16 2630 156 147 1
17 17 26218 160 256 0
18 18 46622 152 299 0
19 19 33667 124 158 0
20 20 32363 124 102 1
21 21 41184 124 116 0
22 22 50791 124 177 0
23 23 45283 152 232 0
24 24 41852 163 60 0
25 25 38111 152 180 0
26 26 61964 152 72 0
27 27 38341 124 49 1
28 28 19135 152 57 0
29 29 27946 152 223 0
30 30 25679 157 232 1
31 31 2630 160 136 0
32 32 57444 26 222 0
33 33 36951 122 149 0
34 34 43129 152 49 0
35 35 27638 152 169 0
36 36 30039 124 88 0
37 37 40903 19 253 0
38 38 76251 152 107 0
39 39 42297 124 264 0
40 40 32210 152 233 0
In [115]:
merge_pd = pd.merge(df,df1,on = 'Customer_id')
In [116]:
merge_pd
Out[116]:
​
Customer_id Gender Age driving license present region code previously insured vehicle age vehicle Damage annual premium (inRs) sales channel code vintage response
0 1 Male 44 1 28 0 > 2 Years Yes 40454 26 217 1
1 2 Male 76 1 3 0 1-2 Year No 33536 26 183 0
2 3 Male 47 1 28 0 > 2 Years Yes 38294 26 27 1
3 4 Male 21 1 11 1 < 1 Year No 28619 152 203 0
4 5 Female 29 1 41 1 < 1 Year No 27496 152 39 0
5 6 Female 24 1 33 0 < 1 Year Yes 2630 160 176 0
6 7 Male 23 1 11 0 < 1 Year Yes 23367 152 249 0
7 8 Female 56 1 28 0 1-2 Year Yes 32031 26 72 1
8 9 Female 24 1 3 1 < 1 Year No 27619 152 28 0
9 10 Female 32 1 6 1 < 1 Year No 28771 152 80 0
10 11 Female 47 1 35 0 1-2 Year Yes 47576 124 46 1
11 12 Female 24 1 50 1 < 1 Year No 48699 152 289 0
12 13 Female 41 1 15 1 1-2 Year No 31409 14 221 0
13 14 Male 76 1 28 0 1-2 Year Yes 36770 13 15 0
14 15 Male 71 1 28 1 1-2 Year No 46818 30 58 0
15 16 Male 37 1 6 0 1-2 Year Yes 2630 156 147 1
16 17 Female 25 1 45 0 < 1 Year Yes 26218 160 256 0
17 18 Female 25 1 35 1 < 1 Year No 46622 152 299 0
18 19 Male 42 1 28 0 1-2 Year Yes 33667 124 158 0
19 20 Female 60 1 33 0 1-2 Year Yes 32363 124 102 1
20 21 Male 65 1 28 0 1-2 Year Yes 41184 124 116 0
21 22 Male 49 1 28 0 1-2 Year Yes 50791 124 177 0
22 23 Male 23 1 50 1 < 1 Year No 45283 152 232 0
23 24 Male 44 1 28 0 1-2 Year Yes 41852 163 60 0
24 25 Male 34 1 15 1 1-2 Year No 38111 152 180 0
25 26 Female 21 1 28 1 < 1 Year No 61964 152 72 0
26 27 Female 51 1 28 0 1-2 Year Yes 38341 124 49 1
27 28 Male 26 1 6 1 < 1 Year No 19135 152 57 0
28 29 Female 25 1 8 1 < 1 Year No 27946 152 223 0
29 30 Male 57 1 11 0 1-2 Year Yes 25679 157 232 1
30 31 Female 26 1 8 0 < 1 Year No 2630 160 136 0
31 32 Male 79 1 28 0 1-2 Year Yes 57444 26 222 0
32 33 Female 48 1 28 0 1-2 Year Yes 36951 122 149 0
33 34 Female 24 1 36 0 < 1 Year Yes 43129 152 49 0
34 35 Female 32 1 30 1 < 1 Year No 27638 152 169 0
35 36 Male 41 1 36 1 1-2 Year No 30039 124 88 0
36 37 Male 71 1 8 1 1-2 Year No 40903 19 253 0
37 38 Female 25 1 28 1 < 1 Year No 76251 152 107 0
38 39 Male 45 1 8 0 1-2 Year Yes 42297 124 264 0
39 40 Male 72 
merge_right = pd.merge(df,df1,on = 'Customer_id',how = 'right')
In [118]:
merge_right
Out[118]:
Customer_id Gender Age driving license present region code previously insured vehicle age vehicle Damage annual premium (inRs) sales channel code vintage response
0 0 NaN NaN NaN NaN NaN NaN NaN 1 2 3 4
1 1 Male 44.0 1.0 28.0 0.0 > 2 Years Yes 40454 26 217 1
2 2 Male 76.0 1.0 3.0 0.0 1-2 Year No 33536 26 183 0
3 3 Male 47.0 1.0 28.0 0.0 > 2 Years Yes 38294 26 27 1
4 4 Male 21.0 1.0 11.0 1.0 < 1 Year No 28619 152 203 0
5 5 Female 29.0 1.0 41.0 1.0 < 1 Year No 27496 152 39 0
6 6 Female 24.0 1.0 33.0 0.0 < 1 Year Yes 2630 160 176 0
7 7 Male 23.0 1.0 11.0 0.0 < 1 Year Yes 23367 152 249 0
8 8 Female 56.0 1.0 28.0 0.0 1-2 Year Yes 32031 26 72 1
9 9 Female 24.0 1.0 3.0 1.0 < 1 Year No 27619 152 28 0
10 10 Female 32.0 1.0 6.0 1.0 < 1 Year No 28771 152 80 0
11 11 Female 47.0 1.0 35.0 0.0 1-2 Year Yes 47576 124 46 1
12 12 Female 24.0 1.0 50.0 1.0 < 1 Year No 48699 152 289 0
13 13 Female 41.0 1.0 15.0 1.0 1-2 Year No 31409 14 221 0
14 14 Male 76.0 1.0 28.0 0.0 1-2 Year Yes 36770 13 15 0
15 15 Male 71.0 1.0 28.0 1.0 1-2 Year No 46818 30 58 0
16 16 Male 37.0 1.0 6.0 0.0 1-2 Year Yes 2630 156 147 1
17 17 Female 25.0 1.0 45.0 0.0 < 1 Year Yes 26218 160 256 0
18 18 Female 25.0 1.0 35.0 1.0 < 1 Year No 46622 152 299 0
19 19 Male 42.0 1.0 28.0 0.0 1-2 Year Yes 33667 124 158 0
20 20 Female 60.0 1.0 33.0 0.0 1-2 Year Yes 32363 124 102 1
21 21 Male 65.0 1.0 28.0 0.0 1-2 Year Yes 41184 124 116 0
22 22 Male 49.0 1.0 28.0 0.0 1-2 Year Yes 50791 124 177 0
23 23 Male 23.0 1.0 50.0 1.0 < 1 Year No 45283 152 232 0
24 24 Male 44.0 1.0 28.0 0.0 1-2 Year Yes 41852 163 60 0
25 25 Male 34.0 1.0 15.0 1.0 1-2 Year No 38111 152 180 0
26 26 Female 21.0 1.0 28.0 1.0 < 1 Year No 61964 152 72 0
27 27 Female 51.0 1.0 28.0 0.0 1-2 Year Yes 38341 124 49 1
28 28 Male 26.0 1.0 6.0 1.0 < 1 Year No 19135 152 57 0
29 29 Female 25.0 1.0 8.0 1.0 < 1 Year No 27946 152 223 0
30 30 Male 57.0 1.0 11.0 0.0 1-2 Year Yes 25679 157 232 1
31 31 Female 26.0 1.0 8.0 0.0 < 1 Year No 2630 160 136 0
32 32 Male 79.0 1.0 28.0 0.0 1-2 Year Yes 57444 26 222 0
33 33 Female 48.0 1.0 28.0 0.0 1-2 Year Yes 36951 122 149 0
34 34 Female 24.0 1.0 36.0 0.0 < 1 Year Yes 43129 152 49 0
35 35 Female 32.0 1.0 30.0 1.0 < 1 Year No 27638 152 169 0
36 36 Male 41.0 1.0 36.0 1.0 1-2 Year No 30039 124 88 0
37 37 Male 71.0 1.0 8.0 1.0 1-2 Year No 40903 19 253 0
38 38 Female 25.0 1.0 28.0 1.0 < 1 Year No 76251 152 107 0
39 39 Male 45.0 1.0 8.0 0.0 1-2 Year Yes 42297 124 264 0
40 40 Male 72.0 1.0 41.0 1.0 1-2 Year No 32210 152 233 0
In [119]:
​
df.corr()
Out[119]:
Customer_id Age driving license present region code previously insured
Customer_id 1.000000 0.098316 NaN 0.030587 0.148012
Age 0.098316 1.000000 NaN -0.030601 -0.336847
driving license present NaN NaN NaN NaN NaN
region code 0.030587 -0.030601 NaN 1.000000 -0.000767
previously insured 0.148012 -0.336847 NaN -0.000767 1.000000
In [122]:
df1.corr()
Out[122]:
Customer_id annual premium (inRs) sales channel code vintage response
Customer_id 1.000000 0.302619 0.323043 0.200656 -0.361591
annual premium (inRs) 0.302619 1.000000 -0.075585 0.078983 -0.355881
sales channel code 0.323043 -0.075585 1.000000 0.157623 -0.381405
vintage 0.200656 0.078983 0.157623 1.000000 -0.337793
response -0.361591 -0.355881 -0.381405 -0.337793

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