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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 2 new columns ({'Unnamed: 0', 'ProdTaken'})
This happened while the csv dataset builder was generating data using
hf://datasets/Anikettony/Tourism-Package-Prediction/tourism.csv (at revision f077b180611dcff026407cd0930a3ca9ad148acc), [/tmp/hf-datasets-cache/medium/datasets/73780952346977-config-parquet-and-info-Anikettony-Tourism-Packag-e44fa427/hub/datasets--Anikettony--Tourism-Package-Prediction/snapshots/f077b180611dcff026407cd0930a3ca9ad148acc/Xtest.csv (origin=hf://datasets/Anikettony/Tourism-Package-Prediction@f077b180611dcff026407cd0930a3ca9ad148acc/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/73780952346977-config-parquet-and-info-Anikettony-Tourism-Packag-e44fa427/hub/datasets--Anikettony--Tourism-Package-Prediction/snapshots/f077b180611dcff026407cd0930a3ca9ad148acc/Xtrain.csv (origin=hf://datasets/Anikettony/Tourism-Package-Prediction@f077b180611dcff026407cd0930a3ca9ad148acc/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/73780952346977-config-parquet-and-info-Anikettony-Tourism-Packag-e44fa427/hub/datasets--Anikettony--Tourism-Package-Prediction/snapshots/f077b180611dcff026407cd0930a3ca9ad148acc/tourism.csv (origin=hf://datasets/Anikettony/Tourism-Package-Prediction@f077b180611dcff026407cd0930a3ca9ad148acc/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/73780952346977-config-parquet-and-info-Anikettony-Tourism-Packag-e44fa427/hub/datasets--Anikettony--Tourism-Package-Prediction/snapshots/f077b180611dcff026407cd0930a3ca9ad148acc/ytest.csv (origin=hf://datasets/Anikettony/Tourism-Package-Prediction@f077b180611dcff026407cd0930a3ca9ad148acc/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/73780952346977-config-parquet-and-info-Anikettony-Tourism-Packag-e44fa427/hub/datasets--Anikettony--Tourism-Package-Prediction/snapshots/f077b180611dcff026407cd0930a3ca9ad148acc/ytrain.csv (origin=hf://datasets/Anikettony/Tourism-Package-Prediction@f077b180611dcff026407cd0930a3ca9ad148acc/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 674, 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
{'CustomerID': Value('int64'), 'Age': Value('float64'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'PreferredPropertyStar': Value('float64'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'PitchSatisfactionScore': Value('int64'), 'OwnCar': Value('int64'), 'NumberOfChildrenVisiting': Value('float64'), 'MonthlyIncome': Value('float64'), 'TypeofContact': Value('string'), 'Occupation': Value('string'), 'Gender': Value('string'), 'ProductPitched': Value('string'), 'MaritalStatus': Value('string'), 'Designation': Value('string')}
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 2 new columns ({'Unnamed: 0', 'ProdTaken'})
This happened while the csv dataset builder was generating data using
hf://datasets/Anikettony/Tourism-Package-Prediction/tourism.csv (at revision f077b180611dcff026407cd0930a3ca9ad148acc), [/tmp/hf-datasets-cache/medium/datasets/73780952346977-config-parquet-and-info-Anikettony-Tourism-Packag-e44fa427/hub/datasets--Anikettony--Tourism-Package-Prediction/snapshots/f077b180611dcff026407cd0930a3ca9ad148acc/Xtest.csv (origin=hf://datasets/Anikettony/Tourism-Package-Prediction@f077b180611dcff026407cd0930a3ca9ad148acc/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/73780952346977-config-parquet-and-info-Anikettony-Tourism-Packag-e44fa427/hub/datasets--Anikettony--Tourism-Package-Prediction/snapshots/f077b180611dcff026407cd0930a3ca9ad148acc/Xtrain.csv (origin=hf://datasets/Anikettony/Tourism-Package-Prediction@f077b180611dcff026407cd0930a3ca9ad148acc/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/73780952346977-config-parquet-and-info-Anikettony-Tourism-Packag-e44fa427/hub/datasets--Anikettony--Tourism-Package-Prediction/snapshots/f077b180611dcff026407cd0930a3ca9ad148acc/tourism.csv (origin=hf://datasets/Anikettony/Tourism-Package-Prediction@f077b180611dcff026407cd0930a3ca9ad148acc/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/73780952346977-config-parquet-and-info-Anikettony-Tourism-Packag-e44fa427/hub/datasets--Anikettony--Tourism-Package-Prediction/snapshots/f077b180611dcff026407cd0930a3ca9ad148acc/ytest.csv (origin=hf://datasets/Anikettony/Tourism-Package-Prediction@f077b180611dcff026407cd0930a3ca9ad148acc/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/73780952346977-config-parquet-and-info-Anikettony-Tourism-Packag-e44fa427/hub/datasets--Anikettony--Tourism-Package-Prediction/snapshots/f077b180611dcff026407cd0930a3ca9ad148acc/ytrain.csv (origin=hf://datasets/Anikettony/Tourism-Package-Prediction@f077b180611dcff026407cd0930a3ca9ad148acc/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.
CustomerID int64 | Age float64 | CityTier int64 | DurationOfPitch float64 | NumberOfPersonVisiting int64 | NumberOfFollowups float64 | PreferredPropertyStar float64 | NumberOfTrips float64 | Passport int64 | PitchSatisfactionScore int64 | OwnCar int64 | NumberOfChildrenVisiting float64 | MonthlyIncome float64 | TypeofContact string | Occupation string | Gender string | ProductPitched string | MaritalStatus string | Designation string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
201,214 | 44 | 1 | 8 | 3 | 1 | 3 | 2 | 1 | 4 | 1 | 0 | 22,879 | Self Enquiry | Salaried | Female | Standard | Married | Senior Manager |
203,829 | 35 | 3 | 20 | 3 | 4 | 3 | 3 | 0 | 1 | 1 | 2 | 27,306 | Self Enquiry | Small Business | Male | Standard | Married | Senior Manager |
202,622 | 47 | 3 | 7 | 4 | 4 | 5 | 3 | 0 | 2 | 1 | 2 | 29,131 | Self Enquiry | Small Business | Female | Standard | Married | Senior Manager |
201,543 | 32 | 1 | 6 | 3 | 3 | 4 | 2 | 0 | 3 | 1 | 0 | 21,220 | Self Enquiry | Salaried | Male | Deluxe | Married | Manager |
203,144 | 59 | 1 | 9 | 3 | 4 | 3 | 6 | 0 | 2 | 1 | 2 | 21,157 | Self Enquiry | Large Business | Male | Basic | Single | Executive |
200,907 | 44 | 3 | 11 | 2 | 3 | 4 | 1 | 0 | 5 | 1 | 1 | 33,213 | Self Enquiry | Small Business | Male | King | Divorced | VP |
201,426 | 32 | 1 | 35 | 2 | 4 | 4 | 2 | 0 | 3 | 1 | 0 | 17,837 | Self Enquiry | Salaried | Female | Basic | Single | Executive |
204,269 | 27 | 3 | 7 | 3 | 4 | 3 | 3 | 0 | 5 | 0 | 2 | 23,974 | Self Enquiry | Salaried | Male | Deluxe | Married | Manager |
200,261 | 38 | 3 | 8 | 2 | 4 | 3 | 4 | 0 | 5 | 1 | 1 | 20,249 | Company Invited | Salaried | Male | Deluxe | Divorced | Manager |
204,223 | 32 | 1 | 12 | 3 | 4 | 3 | 2 | 1 | 4 | 1 | 1 | 23,499 | Self Enquiry | Large Business | Male | Basic | Married | Executive |
200,243 | 40 | 1 | 30 | 3 | 3 | 3 | 2 | 0 | 3 | 1 | 1 | 18,319 | Self Enquiry | Large Business | Male | Deluxe | Married | Manager |
203,533 | 38 | 1 | 20 | 3 | 4 | 3 | 3 | 0 | 1 | 0 | 1 | 22,963 | Self Enquiry | Small Business | Male | Deluxe | Married | Manager |
200,228 | 35 | 3 | 6 | 3 | 3 | 3 | 2 | 0 | 5 | 1 | 0 | 23,789 | Company Invited | Small Business | Fe Male | Standard | Unmarried | Senior Manager |
201,110 | 35 | 1 | 8 | 3 | 3 | 5 | 2 | 1 | 1 | 1 | 1 | 17,074 | Self Enquiry | Salaried | Female | Basic | Married | Executive |
204,350 | 34 | 1 | 17 | 3 | 6 | 3 | 2 | 0 | 5 | 0 | 1 | 22,086 | Self Enquiry | Small Business | Male | Basic | Married | Executive |
203,870 | 33 | 1 | 36 | 3 | 5 | 4 | 3 | 0 | 3 | 1 | 1 | 21,515 | Self Enquiry | Salaried | Female | Basic | Unmarried | Executive |
200,087 | 51 | 1 | 15 | 3 | 3 | 3 | 4 | 0 | 3 | 1 | 0 | 17,075 | Self Enquiry | Salaried | Male | Basic | Divorced | Executive |
201,365 | 29 | 3 | 30 | 2 | 1 | 5 | 2 | 0 | 3 | 1 | 1 | 16,091 | Company Invited | Large Business | Male | Basic | Single | Executive |
200,378 | 34 | 3 | 25 | 3 | 2 | 3 | 1 | 1 | 2 | 1 | 2 | 20,304 | Company Invited | Small Business | Male | Deluxe | Single | Manager |
202,522 | 38 | 1 | 14 | 2 | 4 | 3 | 6 | 0 | 2 | 0 | 1 | 32,342 | Self Enquiry | Small Business | Male | Standard | Single | Senior Manager |
200,209 | 46 | 1 | 6 | 3 | 3 | 5 | 1 | 0 | 2 | 0 | 0 | 24,396 | Self Enquiry | Small Business | Male | Standard | Married | Senior Manager |
200,510 | 54 | 2 | 25 | 2 | 3 | 4 | 3 | 0 | 3 | 1 | 0 | 25,725 | Self Enquiry | Small Business | Male | Standard | Divorced | Senior Manager |
202,022 | 56 | 1 | 15 | 2 | 3 | 3 | 1 | 0 | 4 | 0 | 0 | 26,103 | Self Enquiry | Small Business | Male | Super Deluxe | Married | AVP |
200,385 | 30 | 1 | 10 | 2 | 3 | 3 | 19 | 1 | 4 | 1 | 1 | 17,285 | Company Invited | Large Business | Male | Basic | Single | Executive |
201,386 | 26 | 1 | 6 | 3 | 3 | 5 | 1 | 0 | 5 | 1 | 2 | 17,867 | Self Enquiry | Small Business | Male | Basic | Single | Executive |
202,060 | 33 | 1 | 13 | 2 | 3 | 3 | 1 | 0 | 4 | 1 | 0 | 26,691 | Self Enquiry | Small Business | Male | Standard | Married | Senior Manager |
201,946 | 24 | 1 | 23 | 3 | 4 | 4 | 2 | 0 | 3 | 1 | 1 | 17,127 | Self Enquiry | Salaried | Male | Basic | Married | Executive |
203,768 | 30 | 1 | 36 | 4 | 6 | 3 | 2 | 0 | 5 | 1 | 3 | 25,062 | Self Enquiry | Salaried | Male | Deluxe | Married | Manager |
201,253 | 33 | 3 | 8 | 3 | 3 | 4 | 1 | 0 | 1 | 0 | 0 | 20,147 | Company Invited | Small Business | Female | Deluxe | Single | Manager |
202,230 | 53 | 3 | 8 | 2 | 4 | 4 | 3 | 0 | 1 | 1 | 0 | 22,525 | Company Invited | Small Business | Female | Standard | Married | Senior Manager |
203,514 | 29 | 3 | 14 | 3 | 4 | 5 | 2 | 0 | 3 | 1 | 2 | 23,576 | Company Invited | Salaried | Male | Deluxe | Unmarried | Manager |
201,372 | 39 | 1 | 15 | 2 | 3 | 5 | 2 | 0 | 4 | 1 | 0 | 20,151 | Self Enquiry | Small Business | Male | Deluxe | Married | Manager |
204,366 | 46 | 3 | 9 | 4 | 4 | 4 | 2 | 0 | 5 | 1 | 3 | 23,483 | Self Enquiry | Salaried | Male | Deluxe | Married | Manager |
202,466 | 35 | 1 | 14 | 3 | 4 | 4 | 2 | 0 | 3 | 1 | 1 | 30,672 | Self Enquiry | Salaried | Female | Standard | Single | Senior Manager |
204,073 | 35 | 3 | 9 | 4 | 4 | 3 | 8 | 0 | 5 | 0 | 1 | 20,909 | Company Invited | Small Business | Female | Basic | Married | Executive |
204,596 | 33 | 1 | 7 | 4 | 5 | 4 | 8 | 0 | 3 | 0 | 3 | 21,010 | Company Invited | Salaried | Female | Basic | Married | Executive |
202,373 | 29 | 1 | 16 | 2 | 4 | 3 | 2 | 0 | 4 | 1 | 0 | 21,623 | Company Invited | Salaried | Female | Basic | Unmarried | Executive |
201,916 | 41 | 3 | 16 | 2 | 3 | 3 | 1 | 0 | 1 | 0 | 1 | 21,230 | Company Invited | Salaried | Male | Deluxe | Single | Manager |
203,268 | 43 | 1 | 36 | 3 | 6 | 3 | 6 | 0 | 3 | 1 | 1 | 22,950 | Self Enquiry | Small Business | Male | Deluxe | Unmarried | Manager |
204,329 | 35 | 3 | 13 | 3 | 6 | 3 | 2 | 0 | 4 | 0 | 2 | 21,029 | Company Invited | Small Business | Female | Basic | Married | Executive |
201,685 | 41 | 3 | 12 | 3 | 3 | 3 | 4 | 1 | 1 | 0 | 0 | 28,591 | Self Enquiry | Salaried | Female | Standard | Single | Senior Manager |
200,694 | 33 | 1 | 6 | 2 | 4 | 3 | 1 | 0 | 4 | 0 | 0 | 21,949 | Self Enquiry | Salaried | Female | Deluxe | Unmarried | Manager |
200,837 | 40 | 1 | 15 | 2 | 3 | 3 | 1 | 0 | 4 | 0 | 0 | 28,499 | Company Invited | Small Business | Fe Male | Standard | Unmarried | Senior Manager |
201,852 | 26 | 1 | 9 | 3 | 3 | 5 | 1 | 0 | 3 | 0 | 1 | 18,102 | Company Invited | Large Business | Male | Basic | Single | Executive |
201,712 | 41 | 1 | 25 | 2 | 3 | 5 | 3 | 0 | 1 | 0 | 0 | 18,072 | Self Enquiry | Salaried | Male | Deluxe | Married | Manager |
200,222 | 37 | 1 | 17 | 2 | 3 | 3 | 2 | 1 | 3 | 0 | 1 | 27,185 | Company Invited | Salaried | Male | Standard | Married | Senior Manager |
202,145 | 31 | 3 | 13 | 2 | 4 | 3 | 4 | 0 | 4 | 1 | 1 | 17,329 | Self Enquiry | Salaried | Male | Basic | Married | Executive |
204,867 | 45 | 3 | 8 | 3 | 6 | 4 | 8 | 0 | 3 | 0 | 2 | 21,040 | Self Enquiry | Salaried | Male | Deluxe | Single | Manager |
200,514 | 33 | 1 | 9 | 3 | 3 | 5 | 2 | 1 | 5 | 1 | 2 | 18,348 | Company Invited | Salaried | Male | Basic | Single | Executive |
202,795 | 33 | 1 | 9 | 4 | 4 | 4 | 3 | 0 | 4 | 0 | 1 | 21,048 | Self Enquiry | Small Business | Female | Basic | Divorced | Executive |
201,074 | 33 | 1 | 14 | 3 | 3 | 3 | 3 | 1 | 3 | 0 | 2 | 21,388 | Self Enquiry | Salaried | Male | Deluxe | Unmarried | Manager |
200,402 | 30 | 3 | 18 | 2 | 3 | 3 | 1 | 0 | 2 | 1 | 0 | 21,577 | Self Enquiry | Large Business | Female | Deluxe | Unmarried | Manager |
200,547 | 42 | 1 | 25 | 2 | 2 | 3 | 7 | 1 | 3 | 1 | 1 | 17,759 | Company Invited | Small Business | Male | Basic | Married | Executive |
201,899 | 46 | 1 | 8 | 2 | 3 | 3 | 7 | 0 | 5 | 1 | 0 | 32,861 | Self Enquiry | Salaried | Male | Super Deluxe | Married | AVP |
204,656 | 51 | 1 | 16 | 4 | 4 | 3 | 6 | 0 | 5 | 1 | 3 | 21,058 | Self Enquiry | Salaried | Male | Basic | Married | Executive |
201,880 | 30 | 1 | 8 | 2 | 5 | 3 | 3 | 0 | 1 | 1 | 0 | 21,091 | Self Enquiry | Salaried | Female | Deluxe | Single | Manager |
202,742 | 37 | 1 | 25 | 3 | 3 | 3 | 6 | 0 | 5 | 0 | 1 | 22,366 | Company Invited | Salaried | Male | Basic | Divorced | Executive |
201,323 | 28 | 2 | 6 | 2 | 3 | 3 | 2 | 0 | 4 | 0 | 1 | 17,706 | Company Invited | Salaried | Male | Basic | Married | Executive |
201,357 | 42 | 1 | 12 | 2 | 3 | 5 | 1 | 0 | 3 | 1 | 0 | 28,348 | Self Enquiry | Small Business | Male | Standard | Married | Senior Manager |
200,617 | 44 | 1 | 10 | 2 | 3 | 4 | 1 | 0 | 2 | 1 | 0 | 20,933 | Self Enquiry | Small Business | Male | Deluxe | Single | Manager |
203,637 | 39 | 1 | 9 | 3 | 5 | 4 | 3 | 0 | 1 | 1 | 1 | 21,118 | Company Invited | Small Business | Female | Basic | Single | Executive |
200,253 | 42 | 1 | 23 | 2 | 2 | 5 | 4 | 1 | 2 | 0 | 0 | 21,545 | Self Enquiry | Salaried | Female | Deluxe | Unmarried | Manager |
202,223 | 39 | 1 | 28 | 2 | 3 | 5 | 2 | 1 | 5 | 1 | 1 | 25,880 | Company Invited | Small Business | Fe Male | Standard | Unmarried | Senior Manager |
200,944 | 28 | 1 | 6 | 2 | 5 | 3 | 1 | 0 | 3 | 1 | 0 | 21,674 | Company Invited | Salaried | Female | Deluxe | Divorced | Manager |
202,079 | 43 | 1 | 20 | 3 | 3 | 5 | 7 | 0 | 5 | 1 | 1 | 32,159 | Self Enquiry | Salaried | Male | Super Deluxe | Married | AVP |
203,372 | 45 | 1 | 22 | 4 | 4 | 3 | 3 | 0 | 3 | 0 | 2 | 26,656 | Self Enquiry | Small Business | Female | Standard | Divorced | Senior Manager |
204,382 | 53 | 1 | 13 | 4 | 4 | 5 | 5 | 1 | 4 | 1 | 2 | 24,255 | Self Enquiry | Large Business | Male | Deluxe | Married | Manager |
204,062 | 42 | 1 | 16 | 4 | 4 | 5 | 4 | 0 | 1 | 0 | 1 | 20,916 | Self Enquiry | Salaried | Male | Basic | Married | Executive |
200,009 | 36 | 1 | 33 | 3 | 3 | 3 | 7 | 0 | 3 | 1 | 0 | 20,237 | Self Enquiry | Small Business | Male | Deluxe | Divorced | Manager |
203,259 | 22 | 1 | 7 | 4 | 5 | 4 | 3 | 1 | 5 | 0 | 3 | 20,748 | Self Enquiry | Large Business | Female | Basic | Single | Executive |
202,664 | 37 | 1 | 12 | 4 | 4 | 4 | 2 | 0 | 2 | 0 | 3 | 24,592 | Self Enquiry | Salaried | Male | Deluxe | Unmarried | Manager |
203,501 | 30 | 3 | 20 | 3 | 4 | 4 | 7 | 0 | 3 | 0 | 2 | 24,443 | Company Invited | Large Business | Fe Male | Deluxe | Unmarried | Manager |
203,967 | 36 | 1 | 18 | 4 | 5 | 5 | 4 | 1 | 5 | 1 | 3 | 28,562 | Company Invited | Small Business | Male | Standard | Married | Senior Manager |
200,186 | 40 | 1 | 10 | 2 | 3 | 3 | 2 | 0 | 5 | 0 | 1 | 34,033 | Self Enquiry | Small Business | Female | King | Divorced | VP |
200,136 | 51 | 1 | 14 | 2 | 5 | 3 | 3 | 0 | 2 | 0 | 1 | 25,650 | Company Invited | Salaried | Male | Standard | Unmarried | Senior Manager |
203,835 | 39 | 3 | 7 | 3 | 5 | 5 | 6 | 0 | 3 | 0 | 2 | 21,536 | Self Enquiry | Salaried | Male | Basic | Unmarried | Executive |
200,390 | 43 | 1 | 18 | 2 | 4 | 4 | 2 | 0 | 3 | 0 | 1 | 29,336 | Self Enquiry | Salaried | Male | Super Deluxe | Married | AVP |
200,040 | 35 | 1 | 10 | 3 | 3 | 3 | 2 | 0 | 4 | 0 | 0 | 16,951 | Self Enquiry | Salaried | Male | Basic | Married | Executive |
202,695 | 40 | 1 | 9 | 4 | 4 | 3 | 2 | 0 | 2 | 1 | 2 | 29,616 | Company Invited | Large Business | Female | Standard | Single | Senior Manager |
203,753 | 27 | 3 | 17 | 3 | 4 | 3 | 3 | 0 | 1 | 0 | 1 | 23,362 | Self Enquiry | Small Business | Male | Deluxe | Unmarried | Manager |
200,762 | 26 | 1 | 8 | 2 | 3 | 5 | 7 | 1 | 5 | 1 | 0 | 17,042 | Company Invited | Salaried | Male | Basic | Divorced | Executive |
200,119 | 43 | 3 | 32 | 3 | 3 | 3 | 2 | 1 | 2 | 0 | 0 | 31,959 | Company Invited | Salaried | Male | Super Deluxe | Divorced | AVP |
203,339 | 32 | 1 | 18 | 4 | 4 | 5 | 3 | 1 | 2 | 0 | 3 | 25,511 | Self Enquiry | Small Business | Male | Deluxe | Divorced | Manager |
202,560 | 35 | 1 | 12 | 3 | 5 | 5 | 4 | 0 | 2 | 0 | 1 | 30,309 | Self Enquiry | Small Business | Female | Standard | Single | Senior Manager |
204,135 | 34 | 1 | 11 | 3 | 5 | 4 | 8 | 0 | 4 | 0 | 2 | 21,300 | Self Enquiry | Small Business | Female | Basic | Married | Executive |
201,016 | 31 | 1 | 14 | 2 | 4 | 4 | 2 | 0 | 4 | 0 | 1 | 16,261 | Self Enquiry | Salaried | Female | Basic | Single | Executive |
204,748 | 35 | 3 | 16 | 4 | 4 | 3 | 3 | 0 | 1 | 0 | 1 | 24,392 | Self Enquiry | Salaried | Female | Deluxe | Married | Manager |
204,865 | 42 | 3 | 16 | 3 | 6 | 3 | 2 | 0 | 5 | 1 | 2 | 24,829 | Company Invited | Salaried | Male | Super Deluxe | Married | AVP |
202,030 | 34 | 1 | 14 | 2 | 3 | 5 | 4 | 0 | 5 | 1 | 1 | 20,121 | Self Enquiry | Salaried | Female | Deluxe | Married | Manager |
202,680 | 34 | 1 | 9 | 3 | 4 | 5 | 2 | 0 | 3 | 1 | 1 | 21,385 | Self Enquiry | Salaried | Female | Basic | Divorced | Executive |
200,022 | 34 | 1 | 13 | 2 | 3 | 4 | 1 | 0 | 3 | 1 | 0 | 26,994 | Self Enquiry | Salaried | Fe Male | Standard | Unmarried | Senior Manager |
202,643 | 39 | 1 | 36 | 3 | 4 | 3 | 5 | 0 | 2 | 0 | 2 | 24,939 | Self Enquiry | Large Business | Male | Deluxe | Divorced | Manager |
203,965 | 29 | 1 | 12 | 3 | 4 | 3 | 3 | 1 | 1 | 0 | 1 | 22,119 | Self Enquiry | Large Business | Male | Basic | Unmarried | Executive |
201,288 | 35 | 1 | 8 | 2 | 3 | 3 | 3 | 0 | 3 | 0 | 1 | 20,762 | Company Invited | Small Business | Male | Deluxe | Married | Manager |
200,293 | 26 | 3 | 10 | 2 | 4 | 3 | 2 | 1 | 2 | 1 | 1 | 20,828 | Self Enquiry | Small Business | Male | Deluxe | Single | Manager |
202,562 | 37 | 1 | 10 | 3 | 4 | 3 | 7 | 0 | 2 | 1 | 1 | 21,513 | Self Enquiry | Salaried | Female | Basic | Married | Executive |
203,734 | 35 | 1 | 16 | 4 | 4 | 5 | 6 | 0 | 3 | 0 | 2 | 24,024 | Company Invited | Salaried | Male | Deluxe | Married | Manager |
204,727 | 40 | 1 | 9 | 3 | 4 | 3 | 2 | 0 | 3 | 1 | 1 | 30,847 | Company Invited | Salaried | Male | Super Deluxe | Married | AVP |
200,363 | 33 | 3 | 11 | 2 | 3 | 3 | 2 | 1 | 2 | 1 | 0 | 17,851 | Self Enquiry | Small Business | Female | Basic | Single | Executive |
200,642 | 38 | 3 | 15 | 3 | 4 | 4 | 1 | 0 | 4 | 0 | 0 | 17,899 | Self Enquiry | Small Business | Male | Basic | Divorced | Executive |
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