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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 3 new columns ({'CustomerID', 'Unnamed: 0', 'ProdTaken'})
This happened while the csv dataset builder was generating data using
hf://datasets/dabirsagar/tourism-dataset/tourism.csv (at revision 66b60adacb04a78f5a1456e0099ff28802f679d0), [/tmp/hf-datasets-cache/medium/datasets/55127391758857-config-parquet-and-info-dabirsagar-tourism-datase-a79e2272/hub/datasets--dabirsagar--tourism-dataset/snapshots/66b60adacb04a78f5a1456e0099ff28802f679d0/Xtest.csv (origin=hf://datasets/dabirsagar/tourism-dataset@66b60adacb04a78f5a1456e0099ff28802f679d0/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/55127391758857-config-parquet-and-info-dabirsagar-tourism-datase-a79e2272/hub/datasets--dabirsagar--tourism-dataset/snapshots/66b60adacb04a78f5a1456e0099ff28802f679d0/Xtrain.csv (origin=hf://datasets/dabirsagar/tourism-dataset@66b60adacb04a78f5a1456e0099ff28802f679d0/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/55127391758857-config-parquet-and-info-dabirsagar-tourism-datase-a79e2272/hub/datasets--dabirsagar--tourism-dataset/snapshots/66b60adacb04a78f5a1456e0099ff28802f679d0/tourism.csv (origin=hf://datasets/dabirsagar/tourism-dataset@66b60adacb04a78f5a1456e0099ff28802f679d0/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/55127391758857-config-parquet-and-info-dabirsagar-tourism-datase-a79e2272/hub/datasets--dabirsagar--tourism-dataset/snapshots/66b60adacb04a78f5a1456e0099ff28802f679d0/ytest.csv (origin=hf://datasets/dabirsagar/tourism-dataset@66b60adacb04a78f5a1456e0099ff28802f679d0/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/55127391758857-config-parquet-and-info-dabirsagar-tourism-datase-a79e2272/hub/datasets--dabirsagar--tourism-dataset/snapshots/66b60adacb04a78f5a1456e0099ff28802f679d0/ytrain.csv (origin=hf://datasets/dabirsagar/tourism-dataset@66b60adacb04a78f5a1456e0099ff28802f679d0/ytrain.csv)]
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 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, 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('float64'), 'CityTier': Value('int64'), 'NumberOfPersonVisiting': Value('int64'), 'PreferredPropertyStar': Value('float64'), 'NumberOfTrips': Value('float64'), 'NumberOfChildrenVisiting': Value('float64'), 'MonthlyIncome': Value('float64'), 'PitchSatisfactionScore': Value('int64'), 'NumberOfFollowups': Value('float64'), 'DurationOfPitch': Value('float64'), 'TypeofContact': Value('string'), 'Occupation': Value('string'), 'Gender': Value('string'), 'MaritalStatus': Value('string'), 'Designation': Value('string'), 'ProductPitched': 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 1347, 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 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, 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 1889, 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 3 new columns ({'CustomerID', 'Unnamed: 0', 'ProdTaken'})
This happened while the csv dataset builder was generating data using
hf://datasets/dabirsagar/tourism-dataset/tourism.csv (at revision 66b60adacb04a78f5a1456e0099ff28802f679d0), [/tmp/hf-datasets-cache/medium/datasets/55127391758857-config-parquet-and-info-dabirsagar-tourism-datase-a79e2272/hub/datasets--dabirsagar--tourism-dataset/snapshots/66b60adacb04a78f5a1456e0099ff28802f679d0/Xtest.csv (origin=hf://datasets/dabirsagar/tourism-dataset@66b60adacb04a78f5a1456e0099ff28802f679d0/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/55127391758857-config-parquet-and-info-dabirsagar-tourism-datase-a79e2272/hub/datasets--dabirsagar--tourism-dataset/snapshots/66b60adacb04a78f5a1456e0099ff28802f679d0/Xtrain.csv (origin=hf://datasets/dabirsagar/tourism-dataset@66b60adacb04a78f5a1456e0099ff28802f679d0/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/55127391758857-config-parquet-and-info-dabirsagar-tourism-datase-a79e2272/hub/datasets--dabirsagar--tourism-dataset/snapshots/66b60adacb04a78f5a1456e0099ff28802f679d0/tourism.csv (origin=hf://datasets/dabirsagar/tourism-dataset@66b60adacb04a78f5a1456e0099ff28802f679d0/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/55127391758857-config-parquet-and-info-dabirsagar-tourism-datase-a79e2272/hub/datasets--dabirsagar--tourism-dataset/snapshots/66b60adacb04a78f5a1456e0099ff28802f679d0/ytest.csv (origin=hf://datasets/dabirsagar/tourism-dataset@66b60adacb04a78f5a1456e0099ff28802f679d0/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/55127391758857-config-parquet-and-info-dabirsagar-tourism-datase-a79e2272/hub/datasets--dabirsagar--tourism-dataset/snapshots/66b60adacb04a78f5a1456e0099ff28802f679d0/ytrain.csv (origin=hf://datasets/dabirsagar/tourism-dataset@66b60adacb04a78f5a1456e0099ff28802f679d0/ytrain.csv)]
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 | CityTier
int64 | NumberOfPersonVisiting
int64 | PreferredPropertyStar
float64 | NumberOfTrips
float64 | NumberOfChildrenVisiting
float64 | MonthlyIncome
float64 | PitchSatisfactionScore
int64 | NumberOfFollowups
float64 | DurationOfPitch
float64 | TypeofContact
string | Occupation
string | Gender
string | MaritalStatus
string | Designation
string | ProductPitched
string | Passport
int64 | OwnCar
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
44
| 1
| 3
| 3
| 2
| 0
| 22,879
| 4
| 1
| 8
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Senior Manager
|
Standard
| 1
| 1
|
35
| 3
| 3
| 3
| 3
| 2
| 27,306
| 1
| 4
| 20
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Senior Manager
|
Standard
| 0
| 1
|
47
| 3
| 4
| 5
| 3
| 2
| 29,131
| 2
| 4
| 7
|
Self Enquiry
|
Small Business
|
Female
|
Married
|
Senior Manager
|
Standard
| 0
| 1
|
32
| 1
| 3
| 4
| 2
| 0
| 21,220
| 3
| 3
| 6
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Manager
|
Deluxe
| 0
| 1
|
59
| 1
| 3
| 3
| 6
| 2
| 21,157
| 2
| 4
| 9
|
Self Enquiry
|
Large Business
|
Male
|
Single
|
Executive
|
Basic
| 0
| 1
|
44
| 3
| 2
| 4
| 1
| 1
| 33,213
| 5
| 3
| 11
|
Self Enquiry
|
Small Business
|
Male
|
Divorced
|
VP
|
King
| 0
| 1
|
32
| 1
| 2
| 4
| 2
| 0
| 17,837
| 3
| 4
| 35
|
Self Enquiry
|
Salaried
|
Female
|
Single
|
Executive
|
Basic
| 0
| 1
|
27
| 3
| 3
| 3
| 3
| 2
| 23,974
| 5
| 4
| 7
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Manager
|
Deluxe
| 0
| 0
|
38
| 3
| 2
| 3
| 4
| 1
| 20,249
| 5
| 4
| 8
|
Company Invited
|
Salaried
|
Male
|
Divorced
|
Manager
|
Deluxe
| 0
| 1
|
32
| 1
| 3
| 3
| 2
| 1
| 23,499
| 4
| 4
| 12
|
Self Enquiry
|
Large Business
|
Male
|
Married
|
Executive
|
Basic
| 1
| 1
|
40
| 1
| 3
| 3
| 2
| 1
| 18,319
| 3
| 3
| 30
|
Self Enquiry
|
Large Business
|
Male
|
Married
|
Manager
|
Deluxe
| 0
| 1
|
38
| 1
| 3
| 3
| 3
| 1
| 22,963
| 1
| 4
| 20
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Manager
|
Deluxe
| 0
| 0
|
35
| 3
| 3
| 3
| 2
| 0
| 23,789
| 5
| 3
| 6
|
Company Invited
|
Small Business
|
Fe Male
|
Unmarried
|
Senior Manager
|
Standard
| 0
| 1
|
35
| 1
| 3
| 5
| 2
| 1
| 17,074
| 1
| 3
| 8
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Executive
|
Basic
| 1
| 1
|
34
| 1
| 3
| 3
| 2
| 1
| 22,086
| 5
| 6
| 17
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Executive
|
Basic
| 0
| 0
|
33
| 1
| 3
| 4
| 3
| 1
| 21,515
| 3
| 5
| 36
|
Self Enquiry
|
Salaried
|
Female
|
Unmarried
|
Executive
|
Basic
| 0
| 1
|
51
| 1
| 3
| 3
| 4
| 0
| 17,075
| 3
| 3
| 15
|
Self Enquiry
|
Salaried
|
Male
|
Divorced
|
Executive
|
Basic
| 0
| 1
|
29
| 3
| 2
| 5
| 2
| 1
| 16,091
| 3
| 1
| 30
|
Company Invited
|
Large Business
|
Male
|
Single
|
Executive
|
Basic
| 0
| 1
|
34
| 3
| 3
| 3
| 1
| 2
| 20,304
| 2
| 2
| 25
|
Company Invited
|
Small Business
|
Male
|
Single
|
Manager
|
Deluxe
| 1
| 1
|
38
| 1
| 2
| 3
| 6
| 1
| 32,342
| 2
| 4
| 14
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Senior Manager
|
Standard
| 0
| 0
|
46
| 1
| 3
| 5
| 1
| 0
| 24,396
| 2
| 3
| 6
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Senior Manager
|
Standard
| 0
| 0
|
54
| 2
| 2
| 4
| 3
| 0
| 25,725
| 3
| 3
| 25
|
Self Enquiry
|
Small Business
|
Male
|
Divorced
|
Senior Manager
|
Standard
| 0
| 1
|
56
| 1
| 2
| 3
| 1
| 0
| 26,103
| 4
| 3
| 15
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
AVP
|
Super Deluxe
| 0
| 0
|
30
| 1
| 2
| 3
| 19
| 1
| 17,285
| 4
| 3
| 10
|
Company Invited
|
Large Business
|
Male
|
Single
|
Executive
|
Basic
| 1
| 1
|
26
| 1
| 3
| 5
| 1
| 2
| 17,867
| 5
| 3
| 6
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Executive
|
Basic
| 0
| 1
|
33
| 1
| 2
| 3
| 1
| 0
| 26,691
| 4
| 3
| 13
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Senior Manager
|
Standard
| 0
| 1
|
24
| 1
| 3
| 4
| 2
| 1
| 17,127
| 3
| 4
| 23
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Executive
|
Basic
| 0
| 1
|
30
| 1
| 4
| 3
| 2
| 3
| 25,062
| 5
| 6
| 36
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Manager
|
Deluxe
| 0
| 1
|
33
| 3
| 3
| 4
| 1
| 0
| 20,147
| 1
| 3
| 8
|
Company Invited
|
Small Business
|
Female
|
Single
|
Manager
|
Deluxe
| 0
| 0
|
53
| 3
| 2
| 4
| 3
| 0
| 22,525
| 1
| 4
| 8
|
Company Invited
|
Small Business
|
Female
|
Married
|
Senior Manager
|
Standard
| 0
| 1
|
29
| 3
| 3
| 5
| 2
| 2
| 23,576
| 3
| 4
| 14
|
Company Invited
|
Salaried
|
Male
|
Unmarried
|
Manager
|
Deluxe
| 0
| 1
|
39
| 1
| 2
| 5
| 2
| 0
| 20,151
| 4
| 3
| 15
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Manager
|
Deluxe
| 0
| 1
|
46
| 3
| 4
| 4
| 2
| 3
| 23,483
| 5
| 4
| 9
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Manager
|
Deluxe
| 0
| 1
|
35
| 1
| 3
| 4
| 2
| 1
| 30,672
| 3
| 4
| 14
|
Self Enquiry
|
Salaried
|
Female
|
Single
|
Senior Manager
|
Standard
| 0
| 1
|
35
| 3
| 4
| 3
| 8
| 1
| 20,909
| 5
| 4
| 9
|
Company Invited
|
Small Business
|
Female
|
Married
|
Executive
|
Basic
| 0
| 0
|
33
| 1
| 4
| 4
| 8
| 3
| 21,010
| 3
| 5
| 7
|
Company Invited
|
Salaried
|
Female
|
Married
|
Executive
|
Basic
| 0
| 0
|
29
| 1
| 2
| 3
| 2
| 0
| 21,623
| 4
| 4
| 16
|
Company Invited
|
Salaried
|
Female
|
Unmarried
|
Executive
|
Basic
| 0
| 1
|
41
| 3
| 2
| 3
| 1
| 1
| 21,230
| 1
| 3
| 16
|
Company Invited
|
Salaried
|
Male
|
Single
|
Manager
|
Deluxe
| 0
| 0
|
43
| 1
| 3
| 3
| 6
| 1
| 22,950
| 3
| 6
| 36
|
Self Enquiry
|
Small Business
|
Male
|
Unmarried
|
Manager
|
Deluxe
| 0
| 1
|
35
| 3
| 3
| 3
| 2
| 2
| 21,029
| 4
| 6
| 13
|
Company Invited
|
Small Business
|
Female
|
Married
|
Executive
|
Basic
| 0
| 0
|
41
| 3
| 3
| 3
| 4
| 0
| 28,591
| 1
| 3
| 12
|
Self Enquiry
|
Salaried
|
Female
|
Single
|
Senior Manager
|
Standard
| 1
| 0
|
33
| 1
| 2
| 3
| 1
| 0
| 21,949
| 4
| 4
| 6
|
Self Enquiry
|
Salaried
|
Female
|
Unmarried
|
Manager
|
Deluxe
| 0
| 0
|
40
| 1
| 2
| 3
| 1
| 0
| 28,499
| 4
| 3
| 15
|
Company Invited
|
Small Business
|
Fe Male
|
Unmarried
|
Senior Manager
|
Standard
| 0
| 0
|
26
| 1
| 3
| 5
| 1
| 1
| 18,102
| 3
| 3
| 9
|
Company Invited
|
Large Business
|
Male
|
Single
|
Executive
|
Basic
| 0
| 0
|
41
| 1
| 2
| 5
| 3
| 0
| 18,072
| 1
| 3
| 25
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Manager
|
Deluxe
| 0
| 0
|
37
| 1
| 2
| 3
| 2
| 1
| 27,185
| 3
| 3
| 17
|
Company Invited
|
Salaried
|
Male
|
Married
|
Senior Manager
|
Standard
| 1
| 0
|
31
| 3
| 2
| 3
| 4
| 1
| 17,329
| 4
| 4
| 13
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Executive
|
Basic
| 0
| 1
|
45
| 3
| 3
| 4
| 8
| 2
| 21,040
| 3
| 6
| 8
|
Self Enquiry
|
Salaried
|
Male
|
Single
|
Manager
|
Deluxe
| 0
| 0
|
33
| 1
| 3
| 5
| 2
| 2
| 18,348
| 5
| 3
| 9
|
Company Invited
|
Salaried
|
Male
|
Single
|
Executive
|
Basic
| 1
| 1
|
33
| 1
| 4
| 4
| 3
| 1
| 21,048
| 4
| 4
| 9
|
Self Enquiry
|
Small Business
|
Female
|
Divorced
|
Executive
|
Basic
| 0
| 0
|
33
| 1
| 3
| 3
| 3
| 2
| 21,388
| 3
| 3
| 14
|
Self Enquiry
|
Salaried
|
Male
|
Unmarried
|
Manager
|
Deluxe
| 1
| 0
|
30
| 3
| 2
| 3
| 1
| 0
| 21,577
| 2
| 3
| 18
|
Self Enquiry
|
Large Business
|
Female
|
Unmarried
|
Manager
|
Deluxe
| 0
| 1
|
42
| 1
| 2
| 3
| 7
| 1
| 17,759
| 3
| 2
| 25
|
Company Invited
|
Small Business
|
Male
|
Married
|
Executive
|
Basic
| 1
| 1
|
46
| 1
| 2
| 3
| 7
| 0
| 32,861
| 5
| 3
| 8
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
AVP
|
Super Deluxe
| 0
| 1
|
51
| 1
| 4
| 3
| 6
| 3
| 21,058
| 5
| 4
| 16
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Executive
|
Basic
| 0
| 1
|
30
| 1
| 2
| 3
| 3
| 0
| 21,091
| 1
| 5
| 8
|
Self Enquiry
|
Salaried
|
Female
|
Single
|
Manager
|
Deluxe
| 0
| 1
|
37
| 1
| 3
| 3
| 6
| 1
| 22,366
| 5
| 3
| 25
|
Company Invited
|
Salaried
|
Male
|
Divorced
|
Executive
|
Basic
| 0
| 0
|
28
| 2
| 2
| 3
| 2
| 1
| 17,706
| 4
| 3
| 6
|
Company Invited
|
Salaried
|
Male
|
Married
|
Executive
|
Basic
| 0
| 0
|
42
| 1
| 2
| 5
| 1
| 0
| 28,348
| 3
| 3
| 12
|
Self Enquiry
|
Small Business
|
Male
|
Married
|
Senior Manager
|
Standard
| 0
| 1
|
44
| 1
| 2
| 4
| 1
| 0
| 20,933
| 2
| 3
| 10
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Manager
|
Deluxe
| 0
| 1
|
39
| 1
| 3
| 4
| 3
| 1
| 21,118
| 1
| 5
| 9
|
Company Invited
|
Small Business
|
Female
|
Single
|
Executive
|
Basic
| 0
| 1
|
42
| 1
| 2
| 5
| 4
| 0
| 21,545
| 2
| 2
| 23
|
Self Enquiry
|
Salaried
|
Female
|
Unmarried
|
Manager
|
Deluxe
| 1
| 0
|
39
| 1
| 2
| 5
| 2
| 1
| 25,880
| 5
| 3
| 28
|
Company Invited
|
Small Business
|
Fe Male
|
Unmarried
|
Senior Manager
|
Standard
| 1
| 1
|
28
| 1
| 2
| 3
| 1
| 0
| 21,674
| 3
| 5
| 6
|
Company Invited
|
Salaried
|
Female
|
Divorced
|
Manager
|
Deluxe
| 0
| 1
|
43
| 1
| 3
| 5
| 7
| 1
| 32,159
| 5
| 3
| 20
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
AVP
|
Super Deluxe
| 0
| 1
|
45
| 1
| 4
| 3
| 3
| 2
| 26,656
| 3
| 4
| 22
|
Self Enquiry
|
Small Business
|
Female
|
Divorced
|
Senior Manager
|
Standard
| 0
| 0
|
53
| 1
| 4
| 5
| 5
| 2
| 24,255
| 4
| 4
| 13
|
Self Enquiry
|
Large Business
|
Male
|
Married
|
Manager
|
Deluxe
| 1
| 1
|
42
| 1
| 4
| 5
| 4
| 1
| 20,916
| 1
| 4
| 16
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Executive
|
Basic
| 0
| 0
|
36
| 1
| 3
| 3
| 7
| 0
| 20,237
| 3
| 3
| 33
|
Self Enquiry
|
Small Business
|
Male
|
Divorced
|
Manager
|
Deluxe
| 0
| 1
|
22
| 1
| 4
| 4
| 3
| 3
| 20,748
| 5
| 5
| 7
|
Self Enquiry
|
Large Business
|
Female
|
Single
|
Executive
|
Basic
| 1
| 0
|
37
| 1
| 4
| 4
| 2
| 3
| 24,592
| 2
| 4
| 12
|
Self Enquiry
|
Salaried
|
Male
|
Unmarried
|
Manager
|
Deluxe
| 0
| 0
|
30
| 3
| 3
| 4
| 7
| 2
| 24,443
| 3
| 4
| 20
|
Company Invited
|
Large Business
|
Fe Male
|
Unmarried
|
Manager
|
Deluxe
| 0
| 0
|
36
| 1
| 4
| 5
| 4
| 3
| 28,562
| 5
| 5
| 18
|
Company Invited
|
Small Business
|
Male
|
Married
|
Senior Manager
|
Standard
| 1
| 1
|
40
| 1
| 2
| 3
| 2
| 1
| 34,033
| 5
| 3
| 10
|
Self Enquiry
|
Small Business
|
Female
|
Divorced
|
VP
|
King
| 0
| 0
|
51
| 1
| 2
| 3
| 3
| 1
| 25,650
| 2
| 5
| 14
|
Company Invited
|
Salaried
|
Male
|
Unmarried
|
Senior Manager
|
Standard
| 0
| 0
|
39
| 3
| 3
| 5
| 6
| 2
| 21,536
| 3
| 5
| 7
|
Self Enquiry
|
Salaried
|
Male
|
Unmarried
|
Executive
|
Basic
| 0
| 0
|
43
| 1
| 2
| 4
| 2
| 1
| 29,336
| 3
| 4
| 18
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
AVP
|
Super Deluxe
| 0
| 0
|
35
| 1
| 3
| 3
| 2
| 0
| 16,951
| 4
| 3
| 10
|
Self Enquiry
|
Salaried
|
Male
|
Married
|
Executive
|
Basic
| 0
| 0
|
40
| 1
| 4
| 3
| 2
| 2
| 29,616
| 2
| 4
| 9
|
Company Invited
|
Large Business
|
Female
|
Single
|
Senior Manager
|
Standard
| 0
| 1
|
27
| 3
| 3
| 3
| 3
| 1
| 23,362
| 1
| 4
| 17
|
Self Enquiry
|
Small Business
|
Male
|
Unmarried
|
Manager
|
Deluxe
| 0
| 0
|
26
| 1
| 2
| 5
| 7
| 0
| 17,042
| 5
| 3
| 8
|
Company Invited
|
Salaried
|
Male
|
Divorced
|
Executive
|
Basic
| 1
| 1
|
43
| 3
| 3
| 3
| 2
| 0
| 31,959
| 2
| 3
| 32
|
Company Invited
|
Salaried
|
Male
|
Divorced
|
AVP
|
Super Deluxe
| 1
| 0
|
32
| 1
| 4
| 5
| 3
| 3
| 25,511
| 2
| 4
| 18
|
Self Enquiry
|
Small Business
|
Male
|
Divorced
|
Manager
|
Deluxe
| 1
| 0
|
35
| 1
| 3
| 5
| 4
| 1
| 30,309
| 2
| 5
| 12
|
Self Enquiry
|
Small Business
|
Female
|
Single
|
Senior Manager
|
Standard
| 0
| 0
|
34
| 1
| 3
| 4
| 8
| 2
| 21,300
| 4
| 5
| 11
|
Self Enquiry
|
Small Business
|
Female
|
Married
|
Executive
|
Basic
| 0
| 0
|
31
| 1
| 2
| 4
| 2
| 1
| 16,261
| 4
| 4
| 14
|
Self Enquiry
|
Salaried
|
Female
|
Single
|
Executive
|
Basic
| 0
| 0
|
35
| 3
| 4
| 3
| 3
| 1
| 24,392
| 1
| 4
| 16
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Manager
|
Deluxe
| 0
| 0
|
42
| 3
| 3
| 3
| 2
| 2
| 24,829
| 5
| 6
| 16
|
Company Invited
|
Salaried
|
Male
|
Married
|
AVP
|
Super Deluxe
| 0
| 1
|
34
| 1
| 2
| 5
| 4
| 1
| 20,121
| 5
| 3
| 14
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Manager
|
Deluxe
| 0
| 1
|
34
| 1
| 3
| 5
| 2
| 1
| 21,385
| 3
| 4
| 9
|
Self Enquiry
|
Salaried
|
Female
|
Divorced
|
Executive
|
Basic
| 0
| 1
|
34
| 1
| 2
| 4
| 1
| 0
| 26,994
| 3
| 3
| 13
|
Self Enquiry
|
Salaried
|
Fe Male
|
Unmarried
|
Senior Manager
|
Standard
| 0
| 1
|
39
| 1
| 3
| 3
| 5
| 2
| 24,939
| 2
| 4
| 36
|
Self Enquiry
|
Large Business
|
Male
|
Divorced
|
Manager
|
Deluxe
| 0
| 0
|
29
| 1
| 3
| 3
| 3
| 1
| 22,119
| 1
| 4
| 12
|
Self Enquiry
|
Large Business
|
Male
|
Unmarried
|
Executive
|
Basic
| 1
| 0
|
35
| 1
| 2
| 3
| 3
| 1
| 20,762
| 3
| 3
| 8
|
Company Invited
|
Small Business
|
Male
|
Married
|
Manager
|
Deluxe
| 0
| 0
|
26
| 3
| 2
| 3
| 2
| 1
| 20,828
| 2
| 4
| 10
|
Self Enquiry
|
Small Business
|
Male
|
Single
|
Manager
|
Deluxe
| 1
| 1
|
37
| 1
| 3
| 3
| 7
| 1
| 21,513
| 2
| 4
| 10
|
Self Enquiry
|
Salaried
|
Female
|
Married
|
Executive
|
Basic
| 0
| 1
|
35
| 1
| 4
| 5
| 6
| 2
| 24,024
| 3
| 4
| 16
|
Company Invited
|
Salaried
|
Male
|
Married
|
Manager
|
Deluxe
| 0
| 0
|
40
| 1
| 3
| 3
| 2
| 1
| 30,847
| 3
| 4
| 9
|
Company Invited
|
Salaried
|
Male
|
Married
|
AVP
|
Super Deluxe
| 0
| 1
|
33
| 3
| 2
| 3
| 2
| 0
| 17,851
| 2
| 3
| 11
|
Self Enquiry
|
Small Business
|
Female
|
Single
|
Executive
|
Basic
| 1
| 1
|
38
| 3
| 3
| 4
| 1
| 0
| 17,899
| 4
| 4
| 15
|
Self Enquiry
|
Small Business
|
Male
|
Divorced
|
Executive
|
Basic
| 0
| 0
|
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