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int64
Age
float64
TypeofContact
string
CityTier
int64
DurationOfPitch
float64
Occupation
string
Gender
string
NumberOfPersonVisiting
int64
NumberOfFollowups
float64
ProductPitched
string
PreferredPropertyStar
float64
MaritalStatus
string
NumberOfTrips
float64
Passport
int64
PitchSatisfactionScore
int64
OwnCar
int64
NumberOfChildrenVisiting
float64
Designation
string
MonthlyIncome
float64
ProdTaken
int64
4,279
47
company invited
1
14
small business
female
2
4
deluxe
3
married
4
0
5
1
1
manager
23,936
0
384
36
self enquiry
3
8
small business
male
3
4
standard
3
divorced
2
0
2
1
0
senior manager
22,596
0
2,125
33
self enquiry
2
9
salaried
male
2
3
basic
4
married
4
1
5
0
1
executive
17,277
0
669
39
self enquiry
1
12
small business
male
3
3
basic
5
divorced
1
1
2
1
1
executive
17,404
1
3,051
50
self enquiry
3
18
small business
female
3
4
standard
3
divorced
3
0
4
0
2
senior manager
26,332
1
2,767
29
self enquiry
1
9
small business
male
3
6
basic
3
divorced
7
1
3
1
1
executive
22,142
1
1,818
42
self enquiry
1
29
salaried
female
2
3
super deluxe
3
single
3
0
3
0
0
avp
30,992
0
636
35
company invited
1
31
salaried
female
3
3
basic
4
married
2
1
3
0
0
executive
17,022
1
1,373
38
company invited
1
14
small business
female
2
4
deluxe
4
married
2
1
1
1
0
manager
17,133
0
877
46
self enquiry
1
8
large business
male
2
3
deluxe
3
married
3
0
2
1
0
manager
22,379
0
485
27
self enquiry
1
12
small business
female
3
4
basic
3
married
1
0
2
1
1
executive
17,187
0
3,587
46
company invited
3
33
small business
male
3
5
deluxe
5
married
3
0
3
0
1
manager
23,063
0
3,955
28
self enquiry
1
9
salaried
female
3
4
basic
5
married
3
0
3
1
2
executive
21,019
0
4,805
45
self enquiry
1
9
salaried
female
4
2
basic
3
married
3
0
4
1
3
executive
20,689
0
704
35
self enquiry
3
9
salaried
male
3
3
standard
3
married
7
0
5
0
1
senior manager
22,823
0
1,543
32
self enquiry
1
6
salaried
male
3
3
deluxe
4
married
2
0
3
1
0
manager
21,220
0
1,327
46
self enquiry
3
9
large business
female
2
1
super deluxe
5
married
3
0
3
1
0
avp
28,225
0
1,838
40
self enquiry
3
28
small business
male
3
4
deluxe
3
married
3
1
3
1
0
manager
21,380
1
2,562
37
self enquiry
1
10
salaried
female
3
4
basic
3
married
7
0
2
1
1
executive
21,513
0
4,172
52
self enquiry
1
7
salaried
male
4
4
basic
3
married
3
0
4
1
1
executive
21,401
0
3,911
28
self enquiry
1
11
salaried
male
3
2
deluxe
4
married
3
1
4
1
1
manager
24,820
0
3,303
30
self enquiry
1
22
salaried
female
4
6
basic
3
divorced
2
1
5
1
1
executive
20,846
0
1,982
27
self enquiry
1
9
small business
male
2
4
basic
3
single
1
0
1
1
1
executive
17,045
0
938
58
self enquiry
1
29
salaried
male
2
3
basic
3
married
2
0
3
1
1
executive
17,372
0
2,726
30
company invited
3
9
salaried
male
3
4
deluxe
3
unmarried
3
0
2
0
1
manager
23,232
0
1,340
36
self enquiry
3
16
large business
female
3
4
deluxe
3
married
2
1
1
1
2
manager
20,673
0
160
22
self enquiry
1
25
small business
male
3
3
basic
3
divorced
2
0
2
0
1
executive
17,323
0
2,491
38
self enquiry
1
26
salaried
male
4
4
basic
4
divorced
6
0
4
1
2
executive
21,700
0
106
50
company invited
1
6
salaried
female
3
3
king
4
divorced
4
1
2
0
2
vp
33,172
0
475
26
self enquiry
3
34
small business
male
3
3
deluxe
4
divorced
2
0
3
1
0
manager
21,272
0
47
37
self enquiry
1
25
salaried
male
3
3
basic
4
divorced
5
0
4
1
1
executive
18,022
0
4,563
54
self enquiry
1
30
salaried
female
4
4
super deluxe
3
single
8
0
5
1
2
avp
32,953
0
318
27
self enquiry
1
11
salaried
female
2
3
basic
4
single
2
1
3
0
1
executive
17,478
0
4,644
31
self enquiry
1
15
salaried
female
4
2
standard
5
married
2
1
1
0
3
senior manager
30,094
0
1,735
29
self enquiry
3
8
small business
male
3
4
deluxe
4
married
3
0
4
1
0
manager
21,644
0
1,960
38
self enquiry
1
6
salaried
female
2
3
basic
5
single
4
0
1
0
1
executive
17,619
0
858
53
self enquiry
1
13
small business
female
2
3
king
3
married
4
0
2
1
0
vp
33,606
0
1,127
23
self enquiry
1
16
large business
male
2
1
basic
3
married
3
0
1
1
0
executive
17,073
0
414
24
self enquiry
3
6
large business
female
3
3
basic
4
single
2
1
4
1
2
executive
18,202
1
3,642
34
company invited
3
23
salaried
female
3
4
deluxe
3
married
3
1
1
0
1
manager
24,046
0
600
32
self enquiry
1
10
salaried
female
3
4
deluxe
3
divorced
2
0
4
1
0
manager
21,162
0
4,771
45
self enquiry
1
17
salaried
male
4
4
basic
3
unmarried
3
1
3
1
1
executive
21,614
1
2,021
39
self enquiry
2
9
salaried
female
2
1
deluxe
4
married
1
0
1
0
0
manager
21,389
0
1,945
26
self enquiry
3
34
small business
male
3
3
deluxe
4
married
2
0
3
0
0
manager
21,272
0
4,839
43
company invited
1
8
salaried
female
4
2
standard
4
single
3
0
3
1
1
senior manager
21,050
1
2,589
31
self enquiry
1
12
large business
female
3
4
basic
5
married
7
0
5
1
2
executive
21,882
0
2,940
22
self enquiry
3
29
large business
male
3
4
basic
3
unmarried
3
0
2
1
2
executive
22,125
0
4,321
46
self enquiry
1
17
salaried
male
4
4
basic
3
married
5
0
5
0
3
executive
21,332
0
1,161
38
self enquiry
1
8
small business
female
2
4
standard
3
married
4
1
5
1
1
senior manager
22,756
0
1,887
40
self enquiry
3
8
salaried
female
3
3
king
3
married
1
0
3
1
0
vp
33,041
0
1,418
38
company invited
1
12
salaried
male
3
4
deluxe
3
married
3
0
4
1
1
manager
20,321
0
774
55
company invited
1
8
small business
male
2
4
super deluxe
5
single
1
0
3
1
1
avp
31,756
0
1,628
40
self enquiry
3
10
small business
male
3
4
standard
3
married
1
0
1
1
1
senior manager
25,855
0
4,436
38
self enquiry
1
17
salaried
male
4
2
basic
3
unmarried
5
0
4
1
3
executive
23,358
0
3,271
29
company invited
1
13
salaried
male
3
5
basic
3
married
3
1
4
1
1
executive
21,381
0
2,005
41
self enquiry
2
16
salaried
male
3
4
king
3
married
4
0
1
1
1
vp
34,141
0
2,788
36
self enquiry
1
12
large business
male
4
4
standard
4
unmarried
2
0
3
0
1
senior manager
26,773
0
1,532
50
self enquiry
1
13
small business
female
2
4
king
3
married
6
1
4
1
1
vp
33,740
0
4,484
53
company invited
1
26
small business
male
3
4
basic
3
married
2
0
3
1
1
executive
22,936
0
1,061
24
company invited
3
19
salaried
female
3
3
basic
4
divorced
2
0
1
0
0
executive
17,033
0
1,150
35
self enquiry
1
24
salaried
male
2
1
deluxe
5
married
2
0
3
0
1
manager
20,208
0
3,401
41
company invited
1
23
salaried
male
4
4
basic
3
married
2
0
3
1
3
executive
22,222
0
1,337
28
self enquiry
3
9
small business
female
3
3
basic
4
married
2
0
1
1
0
executive
17,856
0
2,229
45
self enquiry
2
30
small business
male
2
3
basic
4
single
2
0
4
0
1
executive
17,177
0
2,748
40
self enquiry
1
7
small business
male
3
3
standard
3
married
2
0
3
1
1
senior manager
28,291
0
1,392
35
self enquiry
3
13
salaried
female
2
3
deluxe
3
married
2
1
4
1
1
manager
20,204
0
3,234
34
self enquiry
1
12
salaried
male
3
5
standard
3
married
6
0
3
0
1
senior manager
25,797
0
4,716
34
company invited
3
14
salaried
female
2
4
deluxe
4
married
2
0
4
0
1
manager
22,980
0
4,776
45
company invited
1
24
salaried
male
4
6
basic
3
single
4
0
4
1
2
executive
20,968
0
3,144
59
self enquiry
1
9
large business
male
3
4
basic
3
single
6
0
2
1
2
executive
21,157
1
4,328
25
self enquiry
3
7
large business
female
4
4
basic
4
unmarried
3
1
4
0
1
executive
21,880
1
821
49
self enquiry
3
14
large business
male
2
4
super deluxe
4
divorced
7
0
4
1
0
avp
28,120
0
2,238
40
self enquiry
3
32
small business
male
3
3
deluxe
5
married
2
0
1
1
2
manager
23,396
0
2,948
46
company invited
3
33
salaried
female
4
4
deluxe
5
divorced
4
0
2
0
1
manager
22,964
1
2,878
32
self enquiry
1
16
salaried
male
3
4
standard
4
married
3
0
3
1
2
senior manager
29,326
0
1,135
46
self enquiry
3
6
salaried
male
2
1
super deluxe
5
single
2
0
4
1
1
avp
32,567
0
2,073
45
self enquiry
1
8
salaried
female
3
4
basic
3
single
2
0
3
1
0
executive
17,274
0
3,306
45
company invited
1
24
salaried
male
4
6
basic
3
single
4
0
4
1
1
executive
20,968
0
4,802
36
company invited
3
15
small business
female
4
5
standard
3
married
6
1
5
1
3
senior manager
29,055
0
303
31
self enquiry
3
13
large business
male
3
2
deluxe
3
divorced
5
0
2
1
0
manager
21,929
0
1,574
37
company invited
1
8
salaried
male
3
4
deluxe
3
married
6
0
1
1
2
manager
20,163
0
3,997
39
company invited
1
13
small business
female
4
6
deluxe
3
married
2
1
1
0
3
manager
24,007
0
3,849
32
company invited
1
17
small business
female
3
4
standard
3
married
6
0
1
1
2
senior manager
29,709
0
2,837
35
company invited
3
11
small business
female
4
4
deluxe
3
divorced
2
0
2
1
1
manager
25,216
0
3,422
41
self enquiry
1
9
small business
female
3
2
deluxe
3
married
7
0
3
1
1
manager
25,055
0
1,317
27
company invited
1
11
salaried
female
2
3
basic
3
single
1
1
1
1
1
executive
17,379
0
3,387
47
self enquiry
2
7
salaried
female
3
4
basic
5
unmarried
8
0
5
1
1
executive
22,101
0
2,828
37
self enquiry
3
9
small business
male
4
5
standard
3
divorced
3
0
2
1
1
senior manager
26,274
0
1,646
33
self enquiry
1
8
salaried
male
2
3
basic
3
single
1
0
3
1
0
executive
17,500
0
4,639
36
self enquiry
1
21
salaried
male
3
5
basic
4
married
3
1
5
1
2
executive
22,421
1
2,585
46
self enquiry
1
36
small business
male
3
4
basic
3
unmarried
7
0
2
1
1
executive
22,130
0
3,174
31
self enquiry
1
15
salaried
female
4
2
standard
5
divorced
2
1
2
0
3
senior manager
30,094
0
4,124
33
self enquiry
3
11
small business
male
3
6
standard
3
married
3
0
1
1
1
senior manager
29,078
1
2,615
20
company invited
3
7
large business
female
4
6
basic
5
single
2
0
3
1
2
executive
21,003
1
1,713
40
self enquiry
1
30
large business
male
3
3
deluxe
3
married
2
0
3
1
1
manager
18,319
0
702
30
self enquiry
3
14
salaried
male
3
3
standard
3
married
6
0
3
1
0
senior manager
22,264
0
3,719
52
self enquiry
1
10
large business
female
3
4
standard
3
married
4
0
4
1
2
senior manager
31,794
0
420
29
self enquiry
1
9
small business
male
3
4
standard
3
divorced
2
0
2
1
2
senior manager
26,935
0
4,395
35
self enquiry
1
7
salaried
male
3
5
basic
3
married
3
1
1
1
2
executive
22,300
0
4,810
32
self enquiry
1
31
small business
fe male
4
5
deluxe
5
unmarried
3
0
5
1
1
manager
25,490
0
End of preview. Expand in Data Studio

VisitWithUs – Wellness Tourism Customer Dataset

This dataset belongs to Visit With Us, a leading travel company launching a Wellness Tourism Package.
The objective is to build a predictive model that determines whether a customer is likely to purchase the package.

Dataset Description

Target Column:

  • ProdTaken
    • 0 → Customer did not purchase
    • 1 → Customer purchased

Customer Attributes:

  • CustomerID
  • Age
  • Gender
  • CityTier
  • Designation
  • MonthlyIncome
  • Occupation
  • TypeofContact
  • PreferredPropertyStar
  • MaritalStatus
  • NumberOfPersonVisiting
  • ... (complete list)

Interaction Attributes:

  • PitchSatisfactionScore
  • NumberOfFollowups
  • DurationOfPitch
  • ProductPitched

Usage Example (Python)

from datasets import load_dataset

dataset = load_dataset("AkhilRaja/visitwithus-wellness-customer-dataset")
df = dataset['train'].to_pandas()
print(df.head())
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