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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 ({'ProdTaken', 'Unnamed: 0', 'CustomerID'})
This happened while the csv dataset builder was generating data using
hf://datasets/deepacsr/tourism-package-prediction/tourism.csv (at revision 44c69ac3be4cabcdb4551f90d0313531efef8a05), [/tmp/hf-datasets-cache/medium/datasets/35272002677636-config-parquet-and-info-deepacsr-tourism-package--2ed5d9d6/hub/datasets--deepacsr--tourism-package-prediction/snapshots/44c69ac3be4cabcdb4551f90d0313531efef8a05/Xtest.csv (origin=hf://datasets/deepacsr/tourism-package-prediction@44c69ac3be4cabcdb4551f90d0313531efef8a05/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/35272002677636-config-parquet-and-info-deepacsr-tourism-package--2ed5d9d6/hub/datasets--deepacsr--tourism-package-prediction/snapshots/44c69ac3be4cabcdb4551f90d0313531efef8a05/Xtrain.csv (origin=hf://datasets/deepacsr/tourism-package-prediction@44c69ac3be4cabcdb4551f90d0313531efef8a05/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/35272002677636-config-parquet-and-info-deepacsr-tourism-package--2ed5d9d6/hub/datasets--deepacsr--tourism-package-prediction/snapshots/44c69ac3be4cabcdb4551f90d0313531efef8a05/tourism.csv (origin=hf://datasets/deepacsr/tourism-package-prediction@44c69ac3be4cabcdb4551f90d0313531efef8a05/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/35272002677636-config-parquet-and-info-deepacsr-tourism-package--2ed5d9d6/hub/datasets--deepacsr--tourism-package-prediction/snapshots/44c69ac3be4cabcdb4551f90d0313531efef8a05/ytest.csv (origin=hf://datasets/deepacsr/tourism-package-prediction@44c69ac3be4cabcdb4551f90d0313531efef8a05/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/35272002677636-config-parquet-and-info-deepacsr-tourism-package--2ed5d9d6/hub/datasets--deepacsr--tourism-package-prediction/snapshots/44c69ac3be4cabcdb4551f90d0313531efef8a05/ytrain.csv (origin=hf://datasets/deepacsr/tourism-package-prediction@44c69ac3be4cabcdb4551f90d0313531efef8a05/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: int64
TypeofContact: string
CityTier: int64
DurationOfPitch: int64
Occupation: string
Gender: string
NumberOfPersonVisiting: int64
NumberOfFollowups: int64
ProductPitched: string
PreferredPropertyStar: int64
MaritalStatus: string
NumberOfTrips: int64
Passport: int64
PitchSatisfactionScore: int64
OwnCar: int64
NumberOfChildrenVisiting: int64
Designation: string
MonthlyIncome: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2853
to
{'Age': Value('float64'), 'TypeofContact': Value('string'), 'CityTier': Value('float64'), 'DurationOfPitch': Value('float64'), 'Occupation': Value('string'), 'Gender': Value('string'), 'NumberOfPersonVisiting': Value('float64'), 'NumberOfFollowups': Value('float64'), 'ProductPitched': Value('string'), 'PreferredPropertyStar': Value('float64'), 'MaritalStatus': Value('string'), 'NumberOfTrips': Value('float64'), 'Passport': Value('float64'), 'PitchSatisfactionScore': Value('float64'), 'OwnCar': Value('float64'), 'NumberOfChildrenVisiting': Value('float64'), '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 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 ({'ProdTaken', 'Unnamed: 0', 'CustomerID'})
This happened while the csv dataset builder was generating data using
hf://datasets/deepacsr/tourism-package-prediction/tourism.csv (at revision 44c69ac3be4cabcdb4551f90d0313531efef8a05), [/tmp/hf-datasets-cache/medium/datasets/35272002677636-config-parquet-and-info-deepacsr-tourism-package--2ed5d9d6/hub/datasets--deepacsr--tourism-package-prediction/snapshots/44c69ac3be4cabcdb4551f90d0313531efef8a05/Xtest.csv (origin=hf://datasets/deepacsr/tourism-package-prediction@44c69ac3be4cabcdb4551f90d0313531efef8a05/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/35272002677636-config-parquet-and-info-deepacsr-tourism-package--2ed5d9d6/hub/datasets--deepacsr--tourism-package-prediction/snapshots/44c69ac3be4cabcdb4551f90d0313531efef8a05/Xtrain.csv (origin=hf://datasets/deepacsr/tourism-package-prediction@44c69ac3be4cabcdb4551f90d0313531efef8a05/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/35272002677636-config-parquet-and-info-deepacsr-tourism-package--2ed5d9d6/hub/datasets--deepacsr--tourism-package-prediction/snapshots/44c69ac3be4cabcdb4551f90d0313531efef8a05/tourism.csv (origin=hf://datasets/deepacsr/tourism-package-prediction@44c69ac3be4cabcdb4551f90d0313531efef8a05/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/35272002677636-config-parquet-and-info-deepacsr-tourism-package--2ed5d9d6/hub/datasets--deepacsr--tourism-package-prediction/snapshots/44c69ac3be4cabcdb4551f90d0313531efef8a05/ytest.csv (origin=hf://datasets/deepacsr/tourism-package-prediction@44c69ac3be4cabcdb4551f90d0313531efef8a05/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/35272002677636-config-parquet-and-info-deepacsr-tourism-package--2ed5d9d6/hub/datasets--deepacsr--tourism-package-prediction/snapshots/44c69ac3be4cabcdb4551f90d0313531efef8a05/ytrain.csv (origin=hf://datasets/deepacsr/tourism-package-prediction@44c69ac3be4cabcdb4551f90d0313531efef8a05/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 | TypeofContact string | CityTier float64 | DurationOfPitch float64 | Occupation string | Gender string | NumberOfPersonVisiting float64 | NumberOfFollowups float64 | ProductPitched string | PreferredPropertyStar float64 | MaritalStatus string | NumberOfTrips float64 | Passport float64 | PitchSatisfactionScore float64 | OwnCar float64 | NumberOfChildrenVisiting float64 | Designation string | MonthlyIncome float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
31 | Self Enquiry | 2 | 14 | Small Business | Female | 3 | 1 | Basic | 4 | Single | 1 | 0 | 1 | 0 | 2 | Executive | 17,109 |
30 | Self Enquiry | 3 | 33 | Small Business | Male | 3 | 3 | Deluxe | 3 | Married | 1 | 0 | 3 | 1 | 2 | Manager | 20,304 |
38 | Self Enquiry | 1 | 32 | Small Business | Female | 3 | 5 | Deluxe | 3 | Married | 2 | 0 | 3 | 0 | 2 | Manager | 24,409 |
43 | Company Invited | 1 | 27 | Small Business | Male | 3 | 3 | Basic | 3 | Married | 1 | 0 | 4 | 1 | 2 | Executive | 17,258 |
21 | Self Enquiry | 1 | 16 | Salaried | Female | 2 | 4 | Basic | 5 | Single | 2 | 0 | 3 | 1 | 0 | Executive | 16,416 |
36 | Self Enquiry | 3 | 14 | Salaried | Female | 3 | 4 | Deluxe | 3 | Married | 3 | 1 | 4 | 1 | 2 | Manager | 23,882 |
21 | Self Enquiry | 1 | 10 | Salaried | Male | 3 | 5 | Basic | 3 | Unmarried | 3 | 0 | 3 | 0 | 2 | Executive | 21,711 |
27 | Company Invited | 3 | 7 | Small Business | Male | 3 | 5 | Deluxe | 5 | Unmarried | 3 | 0 | 3 | 1 | 2 | Manager | 22,972 |
50 | Self Enquiry | 1 | 34 | Small Business | Male | 3 | 2 | Basic | 3 | Divorced | 2 | 1 | 2 | 1 | 2 | Executive | 18,221 |
26 | Self Enquiry | 1 | 14 | Small Business | Male | 4 | 5 | Basic | 3 | Divorced | 3 | 0 | 2 | 1 | 3 | Executive | 21,567 |
46 | Company Invited | 1 | 14 | Salaried | Male | 4 | 4 | Standard | 5 | Married | 3 | 0 | 1 | 1 | 1 | Senior Manager | 23,888 |
56 | Self Enquiry | 3 | 7 | Salaried | Male | 4 | 4 | Standard | 3 | Married | 5 | 0 | 1 | 0 | 3 | Senior Manager | 28,917 |
23 | Self Enquiry | 1 | 7 | Salaried | Male | 4 | 4 | Basic | 3 | Unmarried | 2 | 0 | 3 | 0 | 3 | Executive | 22,053 |
31 | Self Enquiry | 2 | 28 | Salaried | Male | 2 | 5 | Basic | 3 | Married | 2 | 0 | 1 | 0 | 1 | Executive | 24,852 |
33 | Self Enquiry | 1 | 12 | Salaried | Female | 3 | 2 | Basic | 3 | Married | 5 | 0 | 5 | 1 | 2 | Executive | 21,990 |
40 | Self Enquiry | 1 | 20 | Small Business | Male | 2 | 4 | King | 3 | Single | 2 | 0 | 3 | 1 | 1 | VP | 34,626 |
52 | Self Enquiry | 1 | 9 | Small Business | Male | 3 | 6 | Deluxe | 4 | Married | 7 | 0 | 1 | 0 | 2 | Manager | 24,160 |
26 | Self Enquiry | 1 | 12 | Salaried | Female | 2 | 4 | Basic | 4 | Married | 2 | 0 | 3 | 1 | 1 | Executive | 17,368 |
43 | Self Enquiry | 3 | 11 | Small Business | Male | 3 | 4 | Deluxe | 5 | Unmarried | 2 | 0 | 5 | 1 | 2 | Manager | 23,833 |
35 | Self Enquiry | 1 | 13 | Salaried | Male | 2 | 3 | Standard | 3 | Unmarried | 4 | 0 | 3 | 0 | 1 | Senior Manager | 25,221 |
32 | Self Enquiry | 1 | 11 | Salaried | Male | 2 | 4 | Deluxe | 3 | Unmarried | 1 | 0 | 1 | 0 | 1 | Manager | 24,679 |
46 | Self Enquiry | 3 | 6 | Salaried | Male | 2 | 1 | Super Deluxe | 5 | Single | 2 | 0 | 4 | 1 | 1 | AVP | 32,567 |
27 | Company Invited | 1 | 12 | Small Business | Female | 3 | 5 | Basic | 3 | Married | 3 | 1 | 1 | 0 | 2 | Executive | 21,044 |
32 | Self Enquiry | 3 | 16 | Small Business | Male | 2 | 3 | Deluxe | 3 | Single | 2 | 0 | 4 | 1 | 1 | Manager | 20,396 |
29 | Self Enquiry | 1 | 22 | Salaried | Male | 3 | 4 | Basic | 3 | Married | 3 | 0 | 4 | 0 | 1 | Executive | 20,885 |
45 | Company Invited | 3 | 8 | Large Business | Male | 4 | 4 | Basic | 3 | Married | 5 | 0 | 3 | 1 | 2 | Executive | 26,656 |
36 | Self Enquiry | 1 | 21 | Salaried | Male | 3 | 5 | Basic | 4 | Married | 3 | 1 | 5 | 1 | 2 | Executive | 22,421 |
43 | Self Enquiry | 1 | 15 | Salaried | Male | 3 | 4 | Basic | 3 | Unmarried | 6 | 0 | 1 | 1 | 2 | Executive | 22,646 |
42 | Self Enquiry | 3 | 13 | Salaried | Female | 4 | 4 | Standard | 3 | Single | 5 | 1 | 2 | 0 | 2 | Senior Manager | 32,269 |
41 | Self Enquiry | 3 | 33 | Small Business | Male | 4 | 4 | Deluxe | 5 | Married | 3 | 0 | 1 | 1 | 3 | Manager | 27,074 |
54 | Self Enquiry | 3 | 13 | Small Business | Male | 3 | 4 | Deluxe | 3 | Married | 4 | 1 | 5 | 0 | 2 | Manager | 20,984 |
30 | Company Invited | 3 | 28 | Salaried | Female | 3 | 3 | Standard | 5 | Married | 1 | 0 | 1 | 1 | 1 | Senior Manager | 23,412 |
33 | Self Enquiry | 1 | 31 | Small Business | Male | 2 | 4 | Basic | 4 | Single | 5 | 1 | 4 | 1 | 0 | Executive | 17,313 |
42 | Self Enquiry | 1 | 19 | Large Business | Female | 3 | 4 | King | 3 | Married | 3 | 0 | 4 | 1 | 2 | VP | 38,223 |
31 | Self Enquiry | 3 | 13 | Salaried | Male | 2 | 4 | Basic | 3 | Married | 4 | 0 | 4 | 1 | 1 | Executive | 17,329 |
27 | Self Enquiry | 1 | 13 | Salaried | Female | 4 | 4 | Basic | 3 | Married | 3 | 1 | 1 | 0 | 2 | Executive | 21,337 |
46 | Self Enquiry | 1 | 8 | Small Business | Male | 2 | 3 | King | 3 | Married | 1 | 1 | 1 | 1 | 1 | VP | 34,328 |
46 | Company Invited | 1 | 11 | Salaried | Male | 3 | 4 | Deluxe | 4 | Married | 3 | 0 | 4 | 1 | 1 | Manager | 23,125 |
58 | Company Invited | 1 | 6 | Salaried | Male | 2 | 5 | Deluxe | 3 | Married | 3 | 1 | 1 | 1 | 1 | Manager | 20,660 |
31 | Self Enquiry | 1 | 8 | Small Business | Male | 4 | 4 | Basic | 4 | Married | 2 | 1 | 4 | 1 | 3 | Executive | 22,257 |
38 | Self Enquiry | 1 | 31 | Salaried | Female | 2 | 4 | Standard | 4 | Divorced | 4 | 0 | 3 | 1 | 0 | Senior Manager | 27,061 |
35 | Self Enquiry | 3 | 23 | Salaried | Male | 3 | 3 | Deluxe | 5 | Divorced | 4 | 1 | 3 | 1 | 1 | Manager | 23,966 |
23 | Company Invited | 1 | 11 | Large Business | Male | 4 | 5 | Basic | 3 | Unmarried | 7 | 0 | 5 | 0 | 1 | Executive | 22,572 |
42 | Self Enquiry | 3 | 16 | Salaried | Male | 4 | 4 | Standard | 3 | Married | 5 | 1 | 2 | 0 | 2 | Senior Manager | 26,867 |
47 | Company Invited | 3 | 33 | Salaried | Female | 3 | 1 | Deluxe | 3 | Unmarried | 5 | 1 | 4 | 0 | 1 | Manager | 21,397 |
36 | Company Invited | 1 | 24 | Small Business | Female | 3 | 3 | Basic | 3 | Single | 2 | 0 | 3 | 0 | 1 | Executive | 17,153 |
46 | Company Invited | 3 | 32 | Salaried | Male | 3 | 4 | Deluxe | 4 | Unmarried | 1 | 0 | 4 | 1 | 2 | Manager | 22,991 |
26 | Self Enquiry | 1 | 7 | Salaried | Female | 4 | 4 | Deluxe | 3 | Married | 2 | 0 | 3 | 1 | 2 | Manager | 23,576 |
45 | Self Enquiry | 1 | 15 | Salaried | Male | 4 | 2 | Basic | 3 | Married | 4 | 1 | 3 | 1 | 1 | Executive | 21,496 |
31 | Company Invited | 1 | 12 | Salaried | Male | 4 | 5 | Basic | 3 | Married | 2 | 0 | 5 | 0 | 1 | Executive | 22,439 |
52 | Self Enquiry | 1 | 16 | Salaried | Male | 4 | 4 | Basic | 3 | Married | 5 | 0 | 3 | 1 | 1 | Executive | 20,753 |
43 | Self Enquiry | 3 | 7 | Salaried | Male | 3 | 4 | Deluxe | 3 | Divorced | 3 | 0 | 3 | 1 | 1 | Manager | 23,585 |
41 | Self Enquiry | 3 | 9 | Small Business | Female | 3 | 4 | Deluxe | 4 | Married | 2 | 0 | 1 | 0 | 1 | Manager | 24,393 |
56 | Company Invited | 1 | 9 | Salaried | Male | 4 | 4 | Standard | 4 | Divorced | 5 | 0 | 2 | 1 | 2 | Senior Manager | 29,654 |
41 | Self Enquiry | 3 | 17 | Large Business | Female | 3 | 5 | Deluxe | 4 | Married | 2 | 0 | 5 | 1 | 1 | Manager | 25,530 |
61 | Company Invited | 3 | 35 | Small Business | Female | 4 | 5 | Standard | 5 | Divorced | 6 | 0 | 2 | 1 | 1 | Senior Manager | 28,944 |
26 | Self Enquiry | 1 | 9 | Salaried | Male | 3 | 4 | Basic | 3 | Married | 8 | 1 | 5 | 0 | 1 | Executive | 22,655 |
32 | Self Enquiry | 3 | 20 | Small Business | Male | 4 | 5 | Deluxe | 5 | Married | 7 | 1 | 1 | 1 | 1 | Manager | 20,980 |
27 | Self Enquiry | 1 | 9 | Small Business | Male | 2 | 4 | Basic | 3 | Single | 1 | 0 | 2 | 0 | 0 | Executive | 17,045 |
32 | Self Enquiry | 3 | 13 | Small Business | Male | 4 | 3 | Deluxe | 3 | Married | 6 | 0 | 5 | 1 | 3 | Manager | 24,138 |
38 | Self Enquiry | 1 | 16 | Small Business | Female | 3 | 3 | Deluxe | 3 | Married | 4 | 0 | 3 | 0 | 1 | Manager | 24,824 |
39 | Company Invited | 1 | 8 | Large Business | Fe Male | 3 | 3 | Standard | 3 | Unmarried | 1 | 0 | 2 | 1 | 1 | Senior Manager | 25,938 |
34 | Company Invited | 1 | 21 | Small Business | Male | 3 | 3 | Super Deluxe | 5 | Married | 1 | 0 | 4 | 1 | 2 | AVP | 32,007 |
30 | Self Enquiry | 3 | 34 | Small Business | Female | 3 | 4 | Standard | 3 | Divorced | 3 | 0 | 3 | 1 | 2 | Senior Manager | 26,317 |
20 | Self Enquiry | 1 | 16 | Small Business | Male | 2 | 3 | Basic | 3 | Single | 2 | 1 | 5 | 0 | 0 | Executive | 16,009 |
27 | Company Invited | 3 | 14 | Salaried | Male | 2 | 3 | Standard | 3 | Unmarried | 2 | 0 | 1 | 1 | 0 | Senior Manager | 23,726 |
33 | Self Enquiry | 1 | 27 | Small Business | Male | 3 | 1 | Basic | 4 | Married | 2 | 0 | 1 | 1 | 1 | Executive | 17,028 |
33 | Self Enquiry | 1 | 13 | Small Business | Male | 2 | 3 | Standard | 3 | Divorced | 1 | 0 | 4 | 0 | 0 | Senior Manager | 26,691 |
27 | Self Enquiry | 3 | 11 | Small Business | Male | 3 | 5 | Deluxe | 3 | Unmarried | 3 | 1 | 1 | 1 | 1 | Manager | 24,506 |
19 | Company Invited | 3 | 10 | Small Business | Female | 4 | 4 | Basic | 3 | Single | 3 | 1 | 5 | 0 | 2 | Executive | 20,247 |
49 | Self Enquiry | 1 | 7 | Salaried | Male | 4 | 5 | Standard | 3 | Unmarried | 2 | 1 | 5 | 0 | 1 | Senior Manager | 24,059 |
26 | Self Enquiry | 1 | 31 | Salaried | Male | 2 | 5 | Basic | 3 | Single | 2 | 0 | 2 | 1 | 1 | Executive | 17,293 |
30 | Self Enquiry | 3 | 30 | Salaried | Female | 3 | 5 | Standard | 5 | Married | 3 | 0 | 3 | 1 | 2 | Senior Manager | 26,014 |
53 | Self Enquiry | 1 | 13 | Small Business | Female | 2 | 3 | King | 3 | Married | 4 | 0 | 1 | 1 | 1 | VP | 33,606 |
44 | Self Enquiry | 2 | 6 | Small Business | Male | 3 | 4 | Standard | 5 | Divorced | 1 | 0 | 4 | 1 | 0 | Senior Manager | 25,482 |
22 | Self Enquiry | 1 | 10 | Small Business | Male | 4 | 5 | Basic | 3 | Unmarried | 3 | 0 | 5 | 1 | 3 | Executive | 21,908 |
19 | Self Enquiry | 3 | 28 | Small Business | Male | 2 | 3 | Basic | 3 | Single | 2 | 1 | 2 | 0 | 1 | Executive | 16,675 |
39 | Company Invited | 3 | 9 | Salaried | Male | 3 | 5 | Deluxe | 3 | Divorced | 5 | 0 | 5 | 1 | 2 | Manager | 23,927 |
41 | Company Invited | 1 | 23 | Small Business | Male | 3 | 5 | Deluxe | 3 | Unmarried | 8 | 0 | 5 | 1 | 2 | Manager | 23,772 |
31 | Company Invited | 1 | 7 | Small Business | Female | 3 | 4 | Deluxe | 3 | Unmarried | 3 | 0 | 3 | 1 | 1 | Manager | 22,689 |
51 | Self Enquiry | 1 | 6 | Salaried | Male | 3 | 3 | Basic | 3 | Divorced | 2 | 0 | 4 | 1 | 0 | Executive | 17,723 |
26 | Self Enquiry | 3 | 12 | Small Business | Male | 3 | 5 | Deluxe | 3 | Unmarried | 3 | 1 | 3 | 1 | 1 | Manager | 24,422 |
31 | Company Invited | 1 | 9 | Small Business | Male | 3 | 3 | Standard | 3 | Married | 1 | 0 | 4 | 1 | 0 | Senior Manager | 28,675 |
41 | Company Invited | 3 | 35 | Salaried | Male | 3 | 6 | Standard | 5 | Married | 5 | 0 | 5 | 1 | 1 | Senior Manager | 29,610 |
27 | Self Enquiry | 1 | 23 | Salaried | Female | 2 | 3 | Basic | 3 | Single | 2 | 1 | 5 | 1 | 0 | Executive | 17,394 |
23 | Self Enquiry | 1 | 10 | Small Business | Female | 2 | 3 | Basic | 4 | Single | 2 | 0 | 3 | 1 | 1 | Executive | 18,295 |
30 | Self Enquiry | 3 | 14 | Salaried | Male | 3 | 3 | Standard | 3 | Married | 6 | 0 | 3 | 1 | 0 | Senior Manager | 22,264 |
53 | Self Enquiry | 1 | 9 | Small Business | Male | 2 | 3 | Super Deluxe | 3 | Divorced | 4 | 1 | 5 | 0 | 0 | AVP | 32,584 |
36 | Self Enquiry | 1 | 33 | Small Business | Male | 3 | 3 | Deluxe | 3 | Married | 7 | 0 | 3 | 0 | 0 | Manager | 20,237 |
34 | Self Enquiry | 3 | 14 | Large Business | Male | 3 | 1 | Deluxe | 3 | Married | 2 | 0 | 1 | 0 | 2 | Manager | 21,799 |
19 | Self Enquiry | 1 | 9 | Small Business | Female | 3 | 3 | Basic | 4 | Single | 2 | 0 | 3 | 1 | 2 | Executive | 16,483 |
28 | Self Enquiry | 3 | 11 | Small Business | Male | 2 | 3 | Deluxe | 5 | Unmarried | 1 | 0 | 1 | 1 | 0 | Manager | 23,463 |
41 | Self Enquiry | 3 | 12 | Salaried | Female | 3 | 3 | Standard | 3 | Single | 4 | 1 | 1 | 0 | 0 | Senior Manager | 28,591 |
35 | Company Invited | 1 | 10 | Salaried | Male | 2 | 4 | Basic | 3 | Divorced | 2 | 0 | 2 | 1 | 0 | Executive | 17,376 |
53 | Self Enquiry | 1 | 12 | Salaried | Male | 2 | 3 | Deluxe | 3 | Single | 3 | 0 | 5 | 1 | 1 | Manager | 17,450 |
43 | Company Invited | 1 | 9 | Salaried | Male | 3 | 4 | Standard | 3 | Married | 4 | 1 | 3 | 1 | 1 | Senior Manager | 28,802 |
55 | Self Enquiry | 3 | 6 | Salaried | Male | 3 | 3 | Standard | 3 | Married | 4 | 0 | 1 | 0 | 2 | Senior Manager | 25,239 |
40 | Self Enquiry | 3 | 14 | Salaried | Male | 4 | 4 | Basic | 3 | Married | 3 | 1 | 1 | 1 | 3 | Executive | 23,212 |
37 | Self Enquiry | 1 | 12 | Salaried | Male | 4 | 4 | Deluxe | 4 | Unmarried | 2 | 0 | 2 | 0 | 3 | Manager | 24,592 |
35 | Company Invited | 1 | 17 | Small Business | Male | 3 | 4 | Basic | 3 | Married | 3 | 1 | 3 | 1 | 2 | Executive | 22,493 |
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