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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'CustomerID', 'Unnamed: 0', 'ProdTaken', 'NumberOfChildrenVisiting'})
This happened while the csv dataset builder was generating data using
hf://datasets/SandeepMM/GL-MLOps-VisitWithUs/tourism.csv (at revision 4d23bf6432ae31dde315d5833922c950fed86424)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Unnamed: 0: int64
CustomerID: int64
ProdTaken: int64
Age: double
TypeofContact: string
CityTier: int64
DurationOfPitch: double
Occupation: string
Gender: string
NumberOfPersonVisiting: int64
NumberOfFollowups: double
ProductPitched: string
PreferredPropertyStar: double
MaritalStatus: string
NumberOfTrips: double
Passport: int64
PitchSatisfactionScore: int64
OwnCar: int64
NumberOfChildrenVisiting: double
Designation: string
MonthlyIncome: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2881
to
{'Age': Value('int64'), 'TypeofContact': Value('string'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), 'Occupation': Value('string'), 'Gender': Value('string'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('int64'), 'ProductPitched': Value('string'), 'PreferredPropertyStar': Value('int64'), 'MaritalStatus': Value('string'), 'NumberOfTrips': Value('int64'), 'Passport': Value('int64'), 'PitchSatisfactionScore': Value('int64'), 'OwnCar': Value('int64'), 'Designation': Value('string'), 'MonthlyIncome': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1455, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1054, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'CustomerID', 'Unnamed: 0', 'ProdTaken', 'NumberOfChildrenVisiting'})
This happened while the csv dataset builder was generating data using
hf://datasets/SandeepMM/GL-MLOps-VisitWithUs/tourism.csv (at revision 4d23bf6432ae31dde315d5833922c950fed86424)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Age
int64 | TypeofContact
string | CityTier
int64 | DurationOfPitch
float64 | Occupation
string | Gender
string | NumberOfPersonVisiting
int64 | NumberOfFollowups
int64 | ProductPitched
string | PreferredPropertyStar
int64 | MaritalStatus
string | NumberOfTrips
int64 | Passport
int64 | PitchSatisfactionScore
int64 | OwnCar
int64 | Designation
string | MonthlyIncome
float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
46
|
Self Enquiry
| 3
| 8
|
Small Business
|
Female
| 2
| 3
|
King
| 5
|
Unmarried
| 4
| 0
| 2
| 0
|
VP
| 33,947
|
23
|
Self Enquiry
| 3
| 13
|
Salaried
|
Male
| 2
| 3
|
Basic
| 3
|
Married
| 2
| 1
| 1
| 1
|
Executive
| 17,275
|
36
|
Self Enquiry
| 1
| 11
|
Salaried
|
Female
| 2
| 3
|
Standard
| 5
|
Married
| 5
| 0
| 4
| 1
|
Senior Manager
| 23,008
|
37
|
Company Invited
| 1
| 15
|
Salaried
|
Female
| 4
| 3
|
Standard
| 5
|
Divorced
| 2
| 0
| 3
| 0
|
Senior Manager
| 30,391
|
59
|
Self Enquiry
| 1
| 24
|
Small Business
|
Male
| 3
| 3
|
Standard
| 3
|
Unmarried
| 7
| 0
| 3
| 1
|
Senior Manager
| 25,512
|
25
|
Company Invited
| 1
| 9
|
Large Business
|
Female
| 3
| 4
|
Basic
| 3
|
Unmarried
| 3
| 1
| 3
| 1
|
Executive
| 22,438
|
43
|
Company Invited
| 3
| 33
|
Small Business
|
Female
| 4
| 4
|
Super Deluxe
| 4
|
Divorced
| 7
| 0
| 3
| 1
|
AVP
| 32,203
|
32
|
Self Enquiry
| 1
| 31
|
Small Business
|
Female
| 4
| 5
|
Deluxe
| 5
|
Unmarried
| 3
| 0
| 5
| 1
|
Manager
| 25,490
|
33
|
Self Enquiry
| 1
| 20
|
Small Business
|
Male
| 2
| 3
|
Basic
| 3
|
Married
| 6
| 1
| 1
| 1
|
Executive
| 17,436
|
52
|
Company Invited
| 3
| 9
|
Small Business
|
Male
| 3
| 4
|
Standard
| 4
|
Divorced
| 4
| 0
| 4
| 0
|
Senior Manager
| 29,274
|
35
|
Company Invited
| 3
| 9
|
Small Business
|
Female
| 4
| 4
|
Basic
| 3
|
Divorced
| 8
| 0
| 5
| 1
|
Executive
| 20,909
|
46
|
Self Enquiry
| 1
| 9
|
Salaried
|
Female
| 4
| 5
|
Basic
| 3
|
Unmarried
| 3
| 0
| 3
| 1
|
Executive
| 20,952
|
30
|
Company Invited
| 3
| 32
|
Small Business
|
Female
| 2
| 4
|
Deluxe
| 5
|
Unmarried
| 6
| 0
| 1
| 1
|
Manager
| 21,696
|
46
|
Company Invited
| 1
| 30
|
Salaried
|
Male
| 3
| 4
|
Deluxe
| 5
|
Divorced
| 3
| 1
| 2
| 1
|
Manager
| 22,311
|
27
|
Company Invited
| 3
| 26
|
Salaried
|
Female
| 2
| 3
|
Deluxe
| 3
|
Unmarried
| 2
| 0
| 1
| 1
|
Manager
| 24,981
|
28
|
Self Enquiry
| 1
| 24
|
Large Business
|
Male
| 3
| 4
|
Basic
| 4
|
Married
| 2
| 1
| 4
| 0
|
Executive
| 21,736
|
27
|
Self Enquiry
| 1
| 13
|
Salaried
|
Female
| 4
| 4
|
Basic
| 3
|
Divorced
| 3
| 1
| 2
| 0
|
Executive
| 21,337
|
38
|
Self Enquiry
| 1
| 6
|
Salaried
|
Female
| 2
| 3
|
Basic
| 5
|
Unmarried
| 4
| 0
| 2
| 1
|
Executive
| 17,619
|
35
|
Self Enquiry
| 3
| 9
|
Salaried
|
Male
| 3
| 3
|
Standard
| 3
|
Married
| 7
| 0
| 5
| 0
|
Senior Manager
| 22,823
|
39
|
Self Enquiry
| 3
| 21
|
Salaried
|
Male
| 4
| 4
|
Deluxe
| 4
|
Married
| 2
| 0
| 5
| 1
|
Manager
| 28,602
|
37
|
Self Enquiry
| 1
| 13
|
Small Business
|
Male
| 1
| 3
|
Standard
| 3
|
Unmarried
| 5
| 0
| 2
| 0
|
Senior Manager
| 28,664
|
27
|
Self Enquiry
| 1
| 14
|
Small Business
|
Female
| 3
| 5
|
Standard
| 5
|
Married
| 2
| 1
| 4
| 1
|
Senior Manager
| 21,553
|
54
|
Self Enquiry
| 3
| 7
|
Small Business
|
Female
| 3
| 4
|
Deluxe
| 5
|
Unmarried
| 2
| 0
| 1
| 1
|
Manager
| 27,059
|
38
|
Self Enquiry
| 1
| 7
|
Large Business
|
Female
| 3
| 4
|
Standard
| 3
|
Unmarried
| 6
| 0
| 5
| 1
|
Senior Manager
| 26,169
|
42
|
Self Enquiry
| 1
| 9
|
Salaried
|
Female
| 3
| 1
|
Deluxe
| 3
|
Divorced
| 3
| 1
| 5
| 0
|
Manager
| 20,231
|
55
|
Self Enquiry
| 1
| 6
|
Small Business
|
Male
| 2
| 1
|
Super Deluxe
| 3
|
Married
| 3
| 0
| 5
| 1
|
AVP
| 29,732
|
34
|
Self Enquiry
| 3
| 17
|
Small Business
|
Male
| 3
| 2
|
Standard
| 3
|
Married
| 2
| 1
| 4
| 1
|
Senior Manager
| 27,058
|
38
|
Self Enquiry
| 1
| 18
|
Small Business
|
Female
| 4
| 6
|
Deluxe
| 4
|
Divorced
| 7
| 0
| 4
| 1
|
Manager
| 23,455
|
45
|
Self Enquiry
| 1
| 11
|
Small Business
|
Male
| 3
| 4
|
Deluxe
| 4
|
Unmarried
| 2
| 0
| 2
| 1
|
Manager
| 24,611
|
46
|
Self Enquiry
| 3
| 8
|
Large Business
|
Female
| 3
| 5
|
Super Deluxe
| 4
|
Divorced
| 4
| 1
| 5
| 1
|
AVP
| 31,872
|
41
|
Self Enquiry
| 1
| 26
|
Small Business
|
Male
| 2
| 4
|
Deluxe
| 3
|
Married
| 2
| 1
| 1
| 1
|
Manager
| 21,419
|
30
|
Self Enquiry
| 3
| 17
|
Small Business
|
Female
| 3
| 5
|
Deluxe
| 4
|
Married
| 3
| 1
| 5
| 0
|
Manager
| 26,946
|
40
|
Self Enquiry
| 1
| 26
|
Large Business
|
Male
| 3
| 3
|
Standard
| 3
|
Divorced
| 5
| 0
| 3
| 1
|
Senior Manager
| 25,322
|
53
|
Company Invited
| 3
| 8
|
Small Business
|
Female
| 2
| 4
|
Standard
| 4
|
Divorced
| 3
| 0
| 2
| 0
|
Senior Manager
| 22,525
|
28
|
Self Enquiry
| 3
| 17
|
Small Business
|
Female
| 4
| 5
|
Deluxe
| 3
|
Divorced
| 3
| 1
| 3
| 1
|
Manager
| 24,447
|
32
|
Self Enquiry
| 3
| 9
|
Salaried
|
Male
| 4
| 4
|
Deluxe
| 3
|
Unmarried
| 6
| 1
| 5
| 1
|
Manager
| 25,260
|
49
|
Self Enquiry
| 1
| 10
|
Small Business
|
Male
| 2
| 4
|
King
| 3
|
Married
| 3
| 0
| 3
| 0
|
VP
| 33,711
|
37
|
Self Enquiry
| 1
| 16
|
Small Business
|
Male
| 4
| 5
|
Basic
| 3
|
Divorced
| 2
| 1
| 2
| 0
|
Executive
| 22,066
|
29
|
Self Enquiry
| 3
| 16
|
Small Business
|
Male
| 2
| 4
|
Deluxe
| 3
|
Unmarried
| 1
| 0
| 4
| 1
|
Manager
| 20,869
|
42
|
Self Enquiry
| 3
| 14
|
Small Business
|
Male
| 2
| 3
|
Deluxe
| 4
|
Divorced
| 1
| 0
| 3
| 0
|
Manager
| 21,825
|
45
|
Self Enquiry
| 3
| 8
|
Small Business
|
Female
| 3
| 5
|
King
| 4
|
Unmarried
| 3
| 1
| 5
| 0
|
VP
| 33,824
|
39
|
Self Enquiry
| 3
| 10
|
Salaried
|
Female
| 2
| 4
|
Deluxe
| 3
|
Divorced
| 5
| 0
| 5
| 1
|
Manager
| 20,902
|
40
|
Self Enquiry
| 2
| 9
|
Salaried
|
Female
| 3
| 5
|
Deluxe
| 3
|
Married
| 2
| 0
| 3
| 1
|
Manager
| 23,882
|
39
|
Company Invited
| 1
| 9
|
Small Business
|
Female
| 3
| 5
|
Basic
| 4
|
Unmarried
| 3
| 0
| 1
| 1
|
Executive
| 21,118
|
34
|
Self Enquiry
| 3
| 8
|
Salaried
|
Male
| 2
| 3
|
Deluxe
| 3
|
Unmarried
| 2
| 0
| 5
| 0
|
Manager
| 21,274
|
35
|
Company Invited
| 1
| 8
|
Salaried
|
Female
| 3
| 3
|
Deluxe
| 5
|
Married
| 3
| 0
| 4
| 0
|
Manager
| 20,093
|
32
|
Company Invited
| 1
| 10
|
Salaried
|
Female
| 3
| 4
|
Basic
| 3
|
Unmarried
| 3
| 0
| 4
| 1
|
Executive
| 22,762
|
19
|
Self Enquiry
| 3
| 27
|
Salaried
|
Male
| 2
| 4
|
Basic
| 4
|
Unmarried
| 2
| 1
| 2
| 0
|
Executive
| 17,121
|
27
|
Company Invited
| 1
| 9
|
Small Business
|
Male
| 3
| 4
|
Basic
| 3
|
Married
| 2
| 1
| 2
| 0
|
Executive
| 17,566
|
31
|
Self Enquiry
| 3
| 19
|
Large Business
|
Female
| 3
| 4
|
Deluxe
| 3
|
Unmarried
| 2
| 0
| 2
| 1
|
Manager
| 25,255
|
40
|
Self Enquiry
| 1
| 8
|
Small Business
|
Female
| 2
| 1
|
Deluxe
| 3
|
Married
| 6
| 0
| 3
| 1
|
Manager
| 21,377
|
26
|
Company Invited
| 1
| 6
|
Salaried
|
Female
| 2
| 3
|
Deluxe
| 4
|
Married
| 2
| 0
| 5
| 1
|
Manager
| 21,397
|
35
|
Company Invited
| 1
| 9
|
Salaried
|
Male
| 3
| 5
|
Deluxe
| 3
|
Married
| 3
| 0
| 4
| 0
|
Manager
| 28,225
|
49
|
Self Enquiry
| 1
| 9
|
Large Business
|
Male
| 4
| 2
|
Basic
| 4
|
Unmarried
| 7
| 0
| 2
| 0
|
Executive
| 21,237
|
37
|
Company Invited
| 3
| 17
|
Small Business
|
Male
| 3
| 4
|
Standard
| 3
|
Unmarried
| 3
| 0
| 1
| 1
|
Senior Manager
| 28,658
|
36
|
Company Invited
| 3
| 7
|
Small Business
|
Female
| 4
| 4
|
Standard
| 3
|
Unmarried
| 3
| 0
| 5
| 1
|
Senior Manager
| 27,467
|
20
|
Self Enquiry
| 1
| 5
|
Salaried
|
Male
| 2
| 4
|
Basic
| 3
|
Unmarried
| 2
| 0
| 3
| 0
|
Executive
| 18,033
|
51
|
Self Enquiry
| 1
| 9
|
Small Business
|
Female
| 3
| 3
|
Super Deluxe
| 4
|
Unmarried
| 4
| 0
| 5
| 1
|
AVP
| 28,734
|
56
|
Self Enquiry
| 3
| 33
|
Small Business
|
Male
| 3
| 5
|
King
| 3
|
Married
| 3
| 0
| 3
| 1
|
VP
| 36,698.308013
|
33
|
Company Invited
| 1
| 9
|
Salaried
|
Male
| 4
| 4
|
Basic
| 3
|
Unmarried
| 2
| 0
| 5
| 1
|
Executive
| 21,746
|
36
|
Self Enquiry
| 2
| 14
|
Salaried
|
Male
| 3
| 4
|
Basic
| 5
|
Married
| 1
| 0
| 1
| 0
|
Executive
| 17,342
|
45
|
Self Enquiry
| 1
| 17
|
Salaried
|
Male
| 3
| 4
|
Deluxe
| 3
|
Divorced
| 4
| 1
| 3
| 1
|
Manager
| 25,143
|
29
|
Self Enquiry
| 3
| 8
|
Small Business
|
Male
| 3
| 4
|
Deluxe
| 4
|
Married
| 3
| 0
| 4
| 1
|
Manager
| 21,644
|
60
|
Self Enquiry
| 3
| 32
|
Salaried
|
Female
| 3
| 4
|
Standard
| 5
|
Unmarried
| 2
| 0
| 3
| 1
|
Senior Manager
| 26,315
|
31
|
Self Enquiry
| 3
| 9
|
Large Business
|
Male
| 4
| 4
|
Basic
| 4
|
Married
| 3
| 0
| 3
| 1
|
Executive
| 21,154
|
36
|
Company Invited
| 3
| 14
|
Large Business
|
Male
| 2
| 3
|
Deluxe
| 3
|
Married
| 5
| 0
| 3
| 1
|
Manager
| 20,079
|
41
|
Self Enquiry
| 1
| 6
|
Salaried
|
Male
| 3
| 3
|
Basic
| 3
|
Married
| 4
| 0
| 1
| 0
|
Executive
| 17,782
|
32
|
Self Enquiry
| 3
| 14
|
Large Business
|
Female
| 3
| 4
|
Deluxe
| 4
|
Married
| 2
| 1
| 1
| 1
|
Manager
| 20,228
|
47
|
Self Enquiry
| 1
| 8
|
Small Business
|
Female
| 3
| 3
|
Deluxe
| 3
|
Married
| 6
| 0
| 2
| 0
|
Manager
| 20,070
|
45
|
Self Enquiry
| 3
| 7
|
Salaried
|
Male
| 3
| 4
|
Deluxe
| 5
|
Married
| 2
| 0
| 4
| 1
|
Manager
| 33,061
|
41
|
Self Enquiry
| 1
| 18
|
Large Business
|
Female
| 2
| 3
|
King
| 3
|
Divorced
| 2
| 0
| 4
| 1
|
VP
| 34,545
|
30
|
Self Enquiry
| 2
| 13
|
Small Business
|
Male
| 3
| 5
|
Basic
| 4
|
Married
| 3
| 0
| 3
| 1
|
Executive
| 21,482
|
37
|
Self Enquiry
| 1
| 9
|
Small Business
|
Male
| 4
| 4
|
Basic
| 3
|
Unmarried
| 6
| 0
| 5
| 1
|
Executive
| 21,197
|
46
|
Self Enquiry
| 1
| 27
|
Small Business
|
Male
| 4
| 4
|
Super Deluxe
| 3
|
Divorced
| 2
| 1
| 3
| 0
|
AVP
| 32,174
|
54
|
Self Enquiry
| 1
| 28
|
Small Business
|
Female
| 3
| 3
|
Super Deluxe
| 3
|
Married
| 4
| 0
| 1
| 1
|
AVP
| 32,426
|
37
|
Self Enquiry
| 1
| 33
|
Salaried
|
Male
| 4
| 4
|
Deluxe
| 3
|
Married
| 8
| 0
| 3
| 1
|
Manager
| 24,025
|
56
|
Company Invited
| 1
| 9
|
Salaried
|
Male
| 4
| 4
|
Standard
| 4
|
Divorced
| 5
| 0
| 2
| 1
|
Senior Manager
| 29,654
|
35
|
Company Invited
| 1
| 9
|
Salaried
|
Male
| 4
| 4
|
Deluxe
| 3
|
Unmarried
| 4
| 0
| 4
| 1
|
Manager
| 22,711
|
33
|
Self Enquiry
| 3
| 11
|
Salaried
|
Female
| 3
| 4
|
Basic
| 4
|
Married
| 4
| 0
| 3
| 1
|
Executive
| 22,609
|
18
|
Self Enquiry
| 3
| 15
|
Small Business
|
Male
| 2
| 3
|
Basic
| 3
|
Unmarried
| 2
| 0
| 5
| 0
|
Executive
| 16,611
|
34
|
Company Invited
| 3
| 14
|
Salaried
|
Female
| 2
| 4
|
Deluxe
| 4
|
Divorced
| 2
| 0
| 4
| 1
|
Manager
| 22,980
|
38
|
Self Enquiry
| 1
| 9
|
Salaried
|
Male
| 4
| 5
|
Basic
| 3
|
Unmarried
| 8
| 1
| 3
| 0
|
Executive
| 20,768
|
35
|
Self Enquiry
| 1
| 7
|
Salaried
|
Female
| 4
| 2
|
Basic
| 3
|
Unmarried
| 4
| 0
| 1
| 1
|
Executive
| 21,958
|
31
|
Company Invited
| 1
| 11
|
Small Business
|
Male
| 3
| 3
|
Deluxe
| 5
|
Unmarried
| 1
| 0
| 1
| 0
|
Manager
| 24,936
|
37
|
Company Invited
| 3
| 25
|
Small Business
|
Male
| 2
| 3
|
Standard
| 4
|
Unmarried
| 2
| 1
| 5
| 0
|
Senior Manager
| 22,642
|
23
|
Self Enquiry
| 1
| 7
|
Salaried
|
Male
| 3
| 5
|
Deluxe
| 3
|
Married
| 8
| 0
| 1
| 1
|
Manager
| 23,453
|
57
|
Company Invited
| 3
| 13
|
Salaried
|
Female
| 3
| 3
|
Super Deluxe
| 5
|
Divorced
| 2
| 0
| 3
| 1
|
AVP
| 31,890
|
45
|
Self Enquiry
| 1
| 6
|
Large Business
|
Male
| 3
| 3
|
Standard
| 4
|
Married
| 2
| 0
| 3
| 1
|
Senior Manager
| 22,441
|
27
|
Self Enquiry
| 3
| 17
|
Small Business
|
Female
| 3
| 1
|
Basic
| 3
|
Divorced
| 1
| 0
| 3
| 1
|
Executive
| 17,534
|
59
|
Self Enquiry
| 3
| 31
|
Salaried
|
Female
| 4
| 3
|
Standard
| 5
|
Unmarried
| 1
| 0
| 3
| 1
|
Senior Manager
| 22,637
|
34
|
Company Invited
| 3
| 15
|
Salaried
|
Female
| 3
| 5
|
Basic
| 3
|
Unmarried
| 2
| 0
| 2
| 1
|
Executive
| 21,020
|
19
|
Company Invited
| 1
| 15
|
Small Business
|
Male
| 4
| 4
|
Basic
| 3
|
Unmarried
| 3
| 0
| 5
| 0
|
Executive
| 20,582
|
41
|
Company Invited
| 3
| 16
|
Small Business
|
Male
| 2
| 5
|
Deluxe
| 4
|
Unmarried
| 2
| 0
| 1
| 1
|
Manager
| 21,151
|
18
|
Self Enquiry
| 1
| 9
|
Small Business
|
Male
| 2
| 3
|
Basic
| 3
|
Unmarried
| 2
| 0
| 4
| 1
|
Executive
| 16,420
|
25
|
Self Enquiry
| 3
| 10
|
Salaried
|
Female
| 4
| 4
|
Deluxe
| 3
|
Unmarried
| 2
| 0
| 1
| 1
|
Manager
| 23,255
|
42
|
Self Enquiry
| 1
| 30
|
Small Business
|
Male
| 2
| 3
|
Standard
| 5
|
Divorced
| 2
| 1
| 2
| 1
|
Senior Manager
| 22,406
|
20
|
Self Enquiry
| 3
| 28
|
Salaried
|
Male
| 3
| 5
|
Basic
| 4
|
Unmarried
| 3
| 1
| 2
| 0
|
Executive
| 20,799
|
28
|
Self Enquiry
| 1
| 27
|
Small Business
|
Male
| 2
| 3
|
Basic
| 3
|
Married
| 2
| 0
| 1
| 0
|
Executive
| 17,713
|
40
|
Self Enquiry
| 3
| 8
|
Small Business
|
Female
| 3
| 3
|
Deluxe
| 4
|
Married
| 4
| 0
| 3
| 1
|
Manager
| 20,677
|
38
|
Self Enquiry
| 1
| 31
|
Salaried
|
Female
| 2
| 4
|
Standard
| 4
|
Married
| 4
| 0
| 3
| 0
|
Senior Manager
| 27,061
|
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