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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 1 new columns ({'ProdTaken'}) and 18 missing columns ({'Gender', 'Age', 'DurationOfPitch', 'NumberOfChildrenVisiting', 'TypeofContact', 'NumberOfPersonVisiting', 'PreferredPropertyStar', 'NumberOfTrips', 'NumberOfFollowups', 'Passport', 'CityTier', 'PitchSatisfactionScore', 'ProductPitched', 'Occupation', 'Designation', 'OwnCar', 'MaritalStatus', 'MonthlyIncome'}).
This happened while the csv dataset builder was generating data using
hf://datasets/lcsekar/tourism-project-data/y_train.csv (at revision d68eb56790d5e8ede65b589fe882a8894c1374d0)
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
ProdTaken: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 377
to
{'Age': Value('float64'), 'DurationOfPitch': Value('float64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'NumberOfTrips': Value('float64'), 'NumberOfChildrenVisiting': Value('float64'), 'MonthlyIncome': Value('float64'), 'CityTier': Value('int64'), 'PreferredPropertyStar': Value('float64'), 'PitchSatisfactionScore': Value('int64'), 'Designation': Value('string'), 'Gender': Value('string'), 'TypeofContact': Value('string'), 'Occupation': Value('string'), 'ProductPitched': Value('string'), 'MaritalStatus': Value('string'), 'Passport': Value('int64'), 'OwnCar': Value('int64')}
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 1339, 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 972, 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 1 new columns ({'ProdTaken'}) and 18 missing columns ({'Gender', 'Age', 'DurationOfPitch', 'NumberOfChildrenVisiting', 'TypeofContact', 'NumberOfPersonVisiting', 'PreferredPropertyStar', 'NumberOfTrips', 'NumberOfFollowups', 'Passport', 'CityTier', 'PitchSatisfactionScore', 'ProductPitched', 'Occupation', 'Designation', 'OwnCar', 'MaritalStatus', 'MonthlyIncome'}).
This happened while the csv dataset builder was generating data using
hf://datasets/lcsekar/tourism-project-data/y_train.csv (at revision d68eb56790d5e8ede65b589fe882a8894c1374d0)
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
float64 | DurationOfPitch
float64 | NumberOfPersonVisiting
int64 | NumberOfFollowups
float64 | NumberOfTrips
float64 | NumberOfChildrenVisiting
float64 | MonthlyIncome
float64 | CityTier
int64 | PreferredPropertyStar
float64 | PitchSatisfactionScore
int64 | Designation
string | Gender
string | TypeofContact
string | Occupation
string | ProductPitched
string | MaritalStatus
string | Passport
int64 | OwnCar
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
55
| 17
| 4
| 4
| 8
| 1
| 23,118
| 1
| 5
| 1
|
Manager
|
Female
|
Self Enquiry
|
Small Business
|
Deluxe
|
Unmarried
| 1
| 0
|
39
| 9
| 3
| 4
| 7
| 2
| 22,622
| 1
| 3
| 4
|
Executive
|
Male
|
Self Enquiry
|
Salaried
|
Basic
|
Unmarried
| 1
| 0
|
42
| 8
| 3
| 1
| 1
| 2
| 21,272
| 2
| 5
| 2
|
Manager
|
Male
|
Company Invited
|
Small Business
|
Deluxe
|
Divorced
| 0
| 0
|
37
| 12
| 3
| 5
| 2
| 1
| 98,678
| 1
| 5
| 2
|
Executive
|
Female
|
Self Enquiry
|
Salaried
|
Basic
|
Divorced
| 1
| 1
|
23
| 7
| 3
| 5
| 8
| 1
| 23,453
| 1
| 3
| 2
|
Manager
|
Male
|
Self Enquiry
|
Salaried
|
Deluxe
|
Divorced
| 0
| 1
|
33
| 31
| 4
| 4
| 3
| 1
| 23,987
| 1
| 3
| 4
|
Manager
|
Male
|
Company Invited
|
Salaried
|
Deluxe
|
Divorced
| 0
| 1
|
38
| 24
| 2
| 5
| 4
| 1
| 20,811
| 1
| 3
| 5
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Married
| 1
| 0
|
60
| 9
| 4
| 5
| 5
| 3
| 32,404
| 1
| 3
| 5
|
AVP
|
Female
|
Self Enquiry
|
Salaried
|
Super Deluxe
|
Single
| 1
| 0
|
53
| 8
| 2
| 4
| 3
| 0
| 22,525
| 3
| 4
| 1
|
Senior Manager
|
Female
|
Company Invited
|
Small Business
|
Standard
|
Married
| 0
| 1
|
37
| 33
| 4
| 4
| 8
| 1
| 24,025
| 1
| 3
| 3
|
Manager
|
Male
|
Self Enquiry
|
Salaried
|
Deluxe
|
Married
| 0
| 1
|
60
| 34
| 3
| 4
| 5
| 0
| 25,266
| 3
| 5
| 1
|
Senior Manager
|
Female
|
Company Invited
|
Small Business
|
Standard
|
Married
| 0
| 1
|
43
| 36
| 3
| 6
| 6
| 2
| 22,950
| 1
| 3
| 3
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Unmarried
| 0
| 1
|
35
| 22
| 2
| 1
| 1
| 1
| 17,426
| 1
| 4
| 4
|
Executive
|
Male
|
Self Enquiry
|
Small Business
|
Basic
|
Married
| 0
| 1
|
43
| 10
| 4
| 2
| 4
| 1
| 23,909
| 1
| 3
| 5
|
Manager
|
Female
|
Self Enquiry
|
Salaried
|
Deluxe
|
Married
| 1
| 1
|
52
| 34
| 2
| 1
| 3
| 0
| 28,247
| 1
| 3
| 4
|
AVP
|
Female
|
Company Invited
|
Small Business
|
Super Deluxe
|
Divorced
| 1
| 0
|
59
| 9
| 3
| 5
| 2
| 1
| 21,058
| 1
| 3
| 2
|
Executive
|
Male
|
Company Invited
|
Salaried
|
Basic
|
Married
| 1
| 0
|
36
| 33
| 3
| 3
| 7
| 0
| 20,237
| 1
| 3
| 3
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Divorced
| 0
| 1
|
29
| 23
| 3
| 4
| 3
| 1
| 20,822
| 1
| 3
| 3
|
Executive
|
Male
|
Company Invited
|
Small Business
|
Basic
|
Single
| 0
| 0
|
37
| 16
| 3
| 5
| 4
| 2
| 27,525
| 1
| 4
| 4
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Married
| 1
| 0
|
38
| 8
| 2
| 3
| 1
| 1
| 21,553
| 1
| 3
| 2
|
Manager
|
Male
|
Self Enquiry
|
Salaried
|
Deluxe
|
Divorced
| 0
| 0
|
31
| 6
| 2
| 5
| 2
| 1
| 16,359
| 3
| 3
| 3
|
Executive
|
Female
|
Company Invited
|
Salaried
|
Basic
|
Single
| 0
| 1
|
46
| 16
| 4
| 4
| 6
| 1
| 29,439
| 3
| 5
| 2
|
Senior Manager
|
Male
|
Self Enquiry
|
Small Business
|
Standard
|
Married
| 1
| 1
|
41
| 14
| 3
| 4
| 3
| 1
| 23,339
| 3
| 4
| 5
|
Executive
|
Male
|
Self Enquiry
|
Small Business
|
Basic
|
Unmarried
| 0
| 0
|
35
| 13
| 3
| 3
| 2
| 1
| 20,363
| 1
| 4
| 3
|
Executive
|
Male
|
Self Enquiry
|
Salaried
|
Basic
|
Single
| 1
| 1
|
29
| 16
| 3
| 3
| 2
| 0
| 17,642
| 3
| 3
| 4
|
Executive
|
Male
|
Self Enquiry
|
Salaried
|
Basic
|
Single
| 0
| 1
|
51
| 27
| 3
| 3
| 1
| 2
| 20,441
| 3
| 3
| 5
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Single
| 1
| 0
|
39
| 6
| 2
| 2
| 1
| 0
| 24,613
| 1
| 3
| 3
|
Senior Manager
|
Male
|
Self Enquiry
|
Small Business
|
Standard
|
Married
| 0
| 1
|
37
| 22
| 3
| 4
| 5
| 2
| 21,334
| 3
| 3
| 5
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Married
| 0
| 1
|
33
| 23
| 2
| 3
| 2
| 1
| 32,444
| 3
| 3
| 3
|
AVP
|
Male
|
Company Invited
|
Salaried
|
Super Deluxe
|
Single
| 0
| 1
|
51
| 19
| 4
| 4
| 6
| 3
| 27,886
| 3
| 3
| 5
|
Senior Manager
|
Female
|
Company Invited
|
Small Business
|
Standard
|
Unmarried
| 0
| 1
|
42
| 12
| 3
| 2
| 5
| 1
| 25,548
| 1
| 4
| 5
|
Manager
|
Male
|
Self Enquiry
|
Salaried
|
Deluxe
|
Unmarried
| 0
| 1
|
33
| 15
| 4
| 5
| 3
| 1
| 23,906
| 3
| 4
| 2
|
Manager
|
Female
|
Self Enquiry
|
Large Business
|
Deluxe
|
Divorced
| 1
| 1
|
30
| 17
| 4
| 4
| 2
| 1
| 21,969
| 1
| 4
| 5
|
Executive
|
Female
|
Company Invited
|
Salaried
|
Basic
|
Married
| 0
| 1
|
41
| 7
| 3
| 6
| 4
| 1
| 26,135
| 3
| 3
| 3
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Divorced
| 1
| 1
|
38
| 12
| 3
| 2
| 2
| 1
| 22,178
| 1
| 3
| 5
|
Executive
|
Male
|
Company Invited
|
Large Business
|
Basic
|
Unmarried
| 0
| 1
|
28
| 9
| 3
| 6
| 5
| 2
| 23,749
| 3
| 3
| 4
|
Manager
|
Male
|
Company Invited
|
Salaried
|
Deluxe
|
Unmarried
| 0
| 1
|
27
| 24
| 4
| 6
| 3
| 3
| 20,983
| 1
| 3
| 3
|
Executive
|
Male
|
Self Enquiry
|
Small Business
|
Basic
|
Married
| 0
| 0
|
27
| 11
| 2
| 3
| 2
| 1
| 17,478
| 1
| 4
| 3
|
Executive
|
Female
|
Self Enquiry
|
Salaried
|
Basic
|
Single
| 1
| 0
|
24
| 11
| 3
| 2
| 4
| 2
| 21,497
| 1
| 5
| 4
|
Executive
|
Male
|
Self Enquiry
|
Small Business
|
Basic
|
Married
| 0
| 0
|
34
| 22
| 3
| 4
| 2
| 2
| 17,553
| 1
| 3
| 5
|
Executive
|
Female
|
Company Invited
|
Salaried
|
Basic
|
Single
| 0
| 1
|
37
| 17
| 3
| 5
| 2
| 1
| 25,772
| 3
| 5
| 5
|
Senior Manager
|
Male
|
Self Enquiry
|
Small Business
|
Standard
|
Married
| 0
| 0
|
34
| 7
| 3
| 4
| 1
| 0
| 20,343
| 1
| 5
| 1
|
Manager
|
Male
|
Company Invited
|
Small Business
|
Deluxe
|
Single
| 0
| 0
|
30
| 32
| 2
| 4
| 6
| 1
| 21,696
| 3
| 5
| 2
|
Manager
|
Female
|
Company Invited
|
Small Business
|
Deluxe
|
Unmarried
| 0
| 0
|
27
| 23
| 2
| 3
| 1
| 0
| 18,058
| 1
| 4
| 4
|
Executive
|
Male
|
Self Enquiry
|
Large Business
|
Basic
|
Married
| 1
| 0
|
36
| 9
| 3
| 5
| 4
| 1
| 28,952
| 1
| 4
| 4
|
Senior Manager
|
Male
|
Self Enquiry
|
Salaried
|
Standard
|
Married
| 0
| 1
|
40
| 30
| 3
| 3
| 2
| 1
| 18,319
| 1
| 3
| 3
|
Manager
|
Male
|
Self Enquiry
|
Large Business
|
Deluxe
|
Married
| 0
| 1
|
38
| 7
| 3
| 4
| 6
| 2
| 26,169
| 1
| 3
| 5
|
Senior Manager
|
Female
|
Self Enquiry
|
Large Business
|
Standard
|
Unmarried
| 0
| 1
|
33
| 9
| 3
| 5
| 2
| 1
| 28,585
| 3
| 4
| 1
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Single
| 1
| 1
|
30
| 16
| 2
| 5
| 2
| 1
| 22,661
| 1
| 3
| 1
|
Executive
|
Male
|
Self Enquiry
|
Salaried
|
Basic
|
Unmarried
| 0
| 1
|
52
| 6
| 3
| 3
| 3
| 2
| 32,099
| 1
| 3
| 1
|
AVP
|
Male
|
Self Enquiry
|
Salaried
|
Super Deluxe
|
Married
| 0
| 1
|
33
| 7
| 3
| 6
| 8
| 2
| 25,413
| 3
| 4
| 3
|
Manager
|
Male
|
Self Enquiry
|
Salaried
|
Deluxe
|
Unmarried
| 0
| 0
|
20
| 17
| 4
| 5
| 3
| 3
| 20,537
| 1
| 4
| 5
|
Executive
|
Female
|
Company Invited
|
Small Business
|
Basic
|
Single
| 1
| 0
|
38
| 29
| 2
| 4
| 1
| 0
| 24,526
| 1
| 3
| 3
|
Senior Manager
|
Male
|
Company Invited
|
Salaried
|
Standard
|
Unmarried
| 0
| 0
|
31
| 17
| 2
| 3
| 4
| 0
| 17,356
| 1
| 3
| 3
|
Executive
|
Male
|
Self Enquiry
|
Salaried
|
Basic
|
Married
| 1
| 0
|
52
| 11
| 3
| 4
| 2
| 2
| 21,139
| 1
| 3
| 2
|
Executive
|
Male
|
Self Enquiry
|
Salaried
|
Basic
|
Divorced
| 1
| 1
|
39
| 10
| 3
| 4
| 5
| 1
| 22,995
| 1
| 3
| 5
|
Manager
|
Female
|
Self Enquiry
|
Large Business
|
Deluxe
|
Unmarried
| 1
| 1
|
40
| 11
| 3
| 5
| 6
| 2
| 24,580
| 3
| 3
| 5
|
Manager
|
Female
|
Self Enquiry
|
Salaried
|
Deluxe
|
Married
| 0
| 1
|
26
| 26
| 4
| 4
| 5
| 3
| 22,347
| 1
| 3
| 5
|
Executive
|
Male
|
Self Enquiry
|
Small Business
|
Basic
|
Divorced
| 0
| 1
|
47
| 15
| 2
| 5
| 1
| 1
| 27,936
| 3
| 3
| 5
|
AVP
|
Male
|
Company Invited
|
Salaried
|
Super Deluxe
|
Married
| 0
| 1
|
28
| 16
| 3
| 3
| 2
| 2
| 16,052
| 3
| 4
| 5
|
Executive
|
Male
|
Self Enquiry
|
Small Business
|
Basic
|
Married
| 0
| 0
|
19
| 15
| 4
| 4
| 3
| 1
| 20,582
| 1
| 3
| 5
|
Executive
|
Male
|
Company Invited
|
Small Business
|
Basic
|
Single
| 0
| 0
|
52
| 9
| 2
| 4
| 2
| 0
| 31,856
| 3
| 5
| 5
|
AVP
|
Male
|
Self Enquiry
|
Small Business
|
Super Deluxe
|
Married
| 0
| 1
|
20
| 7
| 4
| 6
| 2
| 2
| 21,003
| 3
| 5
| 3
|
Executive
|
Female
|
Company Invited
|
Large Business
|
Basic
|
Single
| 0
| 1
|
43
| 15
| 3
| 4
| 2
| 2
| 25,503
| 3
| 4
| 3
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Divorced
| 0
| 0
|
30
| 8
| 4
| 4
| 3
| 3
| 22,438
| 1
| 3
| 1
|
Executive
|
Female
|
Self Enquiry
|
Salaried
|
Basic
|
Married
| 0
| 1
|
51
| 7
| 4
| 4
| 2
| 2
| 25,406
| 3
| 3
| 3
|
Manager
|
Male
|
Company Invited
|
Salaried
|
Deluxe
|
Married
| 0
| 1
|
41
| 16
| 4
| 5
| 2
| 2
| 23,554
| 1
| 3
| 5
|
Manager
|
Male
|
Company Invited
|
Salaried
|
Deluxe
|
Married
| 0
| 0
|
33
| 15
| 3
| 4
| 3
| 2
| 27,676
| 3
| 3
| 4
|
Senior Manager
|
Female
|
Company Invited
|
Small Business
|
Standard
|
Unmarried
| 0
| 1
|
22
| 16
| 3
| 4
| 3
| 1
| 21,288
| 3
| 3
| 4
|
Executive
|
Male
|
Company Invited
|
Small Business
|
Basic
|
Unmarried
| 0
| 0
|
40
| 16
| 2
| 1
| 4
| 1
| 17,213
| 1
| 3
| 3
|
Executive
|
Female
|
Self Enquiry
|
Salaried
|
Basic
|
Married
| 1
| 0
|
53
| 6
| 2
| 3
| 1
| 1
| 23,381
| 3
| 5
| 1
|
Manager
|
Female
|
Self Enquiry
|
Small Business
|
Deluxe
|
Unmarried
| 0
| 1
|
29
| 9
| 3
| 5
| 2
| 1
| 21,239
| 1
| 5
| 4
|
Executive
|
Male
|
Company Invited
|
Small Business
|
Basic
|
Single
| 0
| 0
|
44
| 16
| 4
| 4
| 5
| 3
| 24,357
| 1
| 3
| 3
|
Manager
|
Male
|
Company Invited
|
Small Business
|
Deluxe
|
Married
| 1
| 1
|
23
| 13
| 4
| 4
| 2
| 1
| 21,451
| 1
| 3
| 2
|
Executive
|
Male
|
Self Enquiry
|
Small Business
|
Basic
|
Divorced
| 0
| 1
|
43
| 36
| 3
| 6
| 6
| 1
| 22,950
| 1
| 3
| 3
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Unmarried
| 0
| 1
|
33
| 23
| 2
| 3
| 2
| 0
| 32,444
| 3
| 3
| 3
|
AVP
|
Male
|
Company Invited
|
Salaried
|
Super Deluxe
|
Single
| 0
| 1
|
37
| 7
| 3
| 4
| 6
| 2
| 25,331
| 3
| 3
| 1
|
Manager
|
Female
|
Company Invited
|
Small Business
|
Deluxe
|
Unmarried
| 0
| 1
|
37
| 16
| 2
| 1
| 2
| 1
| 28,744
| 1
| 3
| 1
|
Senior Manager
|
Female
|
Self Enquiry
|
Salaried
|
Standard
|
Married
| 1
| 0
|
40
| 10
| 3
| 4
| 6
| 2
| 23,916
| 3
| 3
| 4
|
Manager
|
Female
|
Self Enquiry
|
Small Business
|
Deluxe
|
Married
| 1
| 1
|
36
| 7
| 3
| 2
| 5
| 2
| 21,184
| 1
| 3
| 3
|
Executive
|
Female
|
Self Enquiry
|
Salaried
|
Basic
|
Single
| 0
| 1
|
50
| 23
| 4
| 4
| 6
| 2
| 21,265
| 1
| 5
| 1
|
Executive
|
Female
|
Self Enquiry
|
Small Business
|
Basic
|
Married
| 1
| 1
|
21
| 6
| 3
| 4
| 2
| 2
| 17,174
| 3
| 4
| 5
|
Executive
|
Female
|
Company Invited
|
Large Business
|
Basic
|
Single
| 1
| 1
|
28
| 9
| 4
| 6
| 4
| 2
| 21,195
| 3
| 4
| 5
|
VP
|
Female
|
Self Enquiry
|
Small Business
|
King
|
Single
| 1
| 1
|
52
| 15
| 3
| 5
| 7
| 2
| 31,168
| 1
| 4
| 3
|
Senior Manager
|
Male
|
Self Enquiry
|
Salaried
|
Standard
|
Divorced
| 0
| 1
|
40
| 14
| 3
| 4
| 2
| 2
| 24,094
| 1
| 3
| 4
|
Executive
|
Male
|
Self Enquiry
|
Small Business
|
Basic
|
Unmarried
| 1
| 1
|
29
| 12
| 2
| 3
| 2
| 1
| 18,131
| 1
| 3
| 3
|
Executive
|
Female
|
Self Enquiry
|
Small Business
|
Basic
|
Married
| 0
| 0
|
35
| 17
| 3
| 4
| 3
| 1
| 24,884
| 1
| 5
| 5
|
Senior Manager
|
Male
|
Company Invited
|
Small Business
|
Standard
|
Divorced
| 1
| 1
|
38
| 13
| 4
| 4
| 6
| 1
| 25,180
| 3
| 3
| 3
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Married
| 0
| 1
|
51
| 6
| 1
| 4
| 4
| 0
| 22,484
| 1
| 5
| 2
|
Senior Manager
|
Female
|
Company Invited
|
Small Business
|
Standard
|
Unmarried
| 0
| 1
|
22
| 16
| 3
| 4
| 3
| 1
| 21,288
| 3
| 3
| 4
|
Executive
|
Male
|
Company Invited
|
Small Business
|
Basic
|
Unmarried
| 0
| 1
|
36
| 19
| 2
| 3
| 5
| 1
| 17,143
| 2
| 4
| 3
|
Executive
|
Male
|
Self Enquiry
|
Salaried
|
Basic
|
Married
| 0
| 1
|
31
| 17
| 3
| 3
| 2
| 1
| 21,833
| 1
| 5
| 1
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Married
| 1
| 1
|
28
| 16
| 3
| 4
| 3
| 2
| 22,783
| 3
| 3
| 1
|
Manager
|
Male
|
Self Enquiry
|
Small Business
|
Deluxe
|
Unmarried
| 0
| 0
|
50
| 7
| 3
| 5
| 2
| 1
| 32,642
| 1
| 3
| 3
|
AVP
|
Female
|
Self Enquiry
|
Large Business
|
Super Deluxe
|
Single
| 1
| 0
|
28
| 13
| 3
| 5
| 3
| 2
| 21,217
| 1
| 3
| 1
|
Executive
|
Male
|
Self Enquiry
|
Salaried
|
Basic
|
Married
| 0
| 1
|
40
| 14
| 3
| 3
| 3
| 0
| 21,516
| 1
| 5
| 1
|
Manager
|
Female
|
Self Enquiry
|
Salaried
|
Deluxe
|
Married
| 1
| 0
|
29
| 21
| 2
| 3
| 2
| 0
| 17,340
| 1
| 3
| 3
|
Executive
|
Male
|
Self Enquiry
|
Salaried
|
Basic
|
Single
| 0
| 0
|
40
| 17
| 4
| 4
| 2
| 1
| 32,142
| 1
| 3
| 3
|
Senior Manager
|
Male
|
Self Enquiry
|
Small Business
|
Standard
|
Single
| 0
| 1
|
29
| 7
| 3
| 4
| 2
| 1
| 20,832
| 1
| 3
| 4
|
Executive
|
Male
|
Company Invited
|
Small Business
|
Basic
|
Single
| 1
| 0
|
31
| 8
| 4
| 4
| 2
| 3
| 22,257
| 1
| 4
| 4
|
Executive
|
Male
|
Self Enquiry
|
Small Business
|
Basic
|
Married
| 1
| 1
|
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