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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/p-kansal/mlops-tourism-project/tourism.csv (at revision c985950cd0eda16ae4f573c637de9171f3a7b34b), [/tmp/hf-datasets-cache/medium/datasets/51152758775703-config-parquet-and-info-p-kansal-mlops-tourism-pr-1afd16c8/hub/datasets--p-kansal--mlops-tourism-project/snapshots/c985950cd0eda16ae4f573c637de9171f3a7b34b/Xtest.csv (origin=hf://datasets/p-kansal/mlops-tourism-project@c985950cd0eda16ae4f573c637de9171f3a7b34b/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/51152758775703-config-parquet-and-info-p-kansal-mlops-tourism-pr-1afd16c8/hub/datasets--p-kansal--mlops-tourism-project/snapshots/c985950cd0eda16ae4f573c637de9171f3a7b34b/Xtrain.csv (origin=hf://datasets/p-kansal/mlops-tourism-project@c985950cd0eda16ae4f573c637de9171f3a7b34b/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/51152758775703-config-parquet-and-info-p-kansal-mlops-tourism-pr-1afd16c8/hub/datasets--p-kansal--mlops-tourism-project/snapshots/c985950cd0eda16ae4f573c637de9171f3a7b34b/tourism.csv (origin=hf://datasets/p-kansal/mlops-tourism-project@c985950cd0eda16ae4f573c637de9171f3a7b34b/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/51152758775703-config-parquet-and-info-p-kansal-mlops-tourism-pr-1afd16c8/hub/datasets--p-kansal--mlops-tourism-project/snapshots/c985950cd0eda16ae4f573c637de9171f3a7b34b/ytest.csv (origin=hf://datasets/p-kansal/mlops-tourism-project@c985950cd0eda16ae4f573c637de9171f3a7b34b/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/51152758775703-config-parquet-and-info-p-kansal-mlops-tourism-pr-1afd16c8/hub/datasets--p-kansal--mlops-tourism-project/snapshots/c985950cd0eda16ae4f573c637de9171f3a7b34b/ytrain.csv (origin=hf://datasets/p-kansal/mlops-tourism-project@c985950cd0eda16ae4f573c637de9171f3a7b34b/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
{'Age': Value('float64'), 'TypeofContact': Value('string'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), 'Occupation': Value('string'), 'Gender': Value('string'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'ProductPitched': Value('string'), 'PreferredPropertyStar': Value('float64'), 'MaritalStatus': Value('string'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'PitchSatisfactionScore': Value('int64'), 'OwnCar': Value('int64'), '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 ({'CustomerID', 'Unnamed: 0', 'ProdTaken'})
This happened while the csv dataset builder was generating data using
hf://datasets/p-kansal/mlops-tourism-project/tourism.csv (at revision c985950cd0eda16ae4f573c637de9171f3a7b34b), [/tmp/hf-datasets-cache/medium/datasets/51152758775703-config-parquet-and-info-p-kansal-mlops-tourism-pr-1afd16c8/hub/datasets--p-kansal--mlops-tourism-project/snapshots/c985950cd0eda16ae4f573c637de9171f3a7b34b/Xtest.csv (origin=hf://datasets/p-kansal/mlops-tourism-project@c985950cd0eda16ae4f573c637de9171f3a7b34b/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/51152758775703-config-parquet-and-info-p-kansal-mlops-tourism-pr-1afd16c8/hub/datasets--p-kansal--mlops-tourism-project/snapshots/c985950cd0eda16ae4f573c637de9171f3a7b34b/Xtrain.csv (origin=hf://datasets/p-kansal/mlops-tourism-project@c985950cd0eda16ae4f573c637de9171f3a7b34b/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/51152758775703-config-parquet-and-info-p-kansal-mlops-tourism-pr-1afd16c8/hub/datasets--p-kansal--mlops-tourism-project/snapshots/c985950cd0eda16ae4f573c637de9171f3a7b34b/tourism.csv (origin=hf://datasets/p-kansal/mlops-tourism-project@c985950cd0eda16ae4f573c637de9171f3a7b34b/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/51152758775703-config-parquet-and-info-p-kansal-mlops-tourism-pr-1afd16c8/hub/datasets--p-kansal--mlops-tourism-project/snapshots/c985950cd0eda16ae4f573c637de9171f3a7b34b/ytest.csv (origin=hf://datasets/p-kansal/mlops-tourism-project@c985950cd0eda16ae4f573c637de9171f3a7b34b/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/51152758775703-config-parquet-and-info-p-kansal-mlops-tourism-pr-1afd16c8/hub/datasets--p-kansal--mlops-tourism-project/snapshots/c985950cd0eda16ae4f573c637de9171f3a7b34b/ytrain.csv (origin=hf://datasets/p-kansal/mlops-tourism-project@c985950cd0eda16ae4f573c637de9171f3a7b34b/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
int64 | DurationOfPitch
float64 | Occupation
string | Gender
string | NumberOfPersonVisiting
int64 | NumberOfFollowups
float64 | ProductPitched
string | PreferredPropertyStar
float64 | MaritalStatus
string | NumberOfTrips
float64 | Passport
int64 | PitchSatisfactionScore
int64 | OwnCar
int64 | NumberOfChildrenVisiting
float64 | Designation
string | MonthlyIncome
float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
36
|
Self Enquiry
| 1
| 8
|
Small Business
|
Male
| 3
| 3
|
Basic
| 3
|
Single
| 5
| 0
| 5
| 1
| 0
|
Executive
| 17,519
|
21
|
Company Invited
| 1
| 13
|
Salaried
|
Female
| 4
| 5
|
Basic
| 3
|
Unmarried
| 3
| 1
| 1
| 0
| 1
|
Executive
| 21,604
|
44
|
Self Enquiry
| 1
| 13
|
Small Business
|
Female
| 2
| 3
|
King
| 3
|
Married
| 1
| 1
| 4
| 1
| 1
|
VP
| 34,049
|
29
|
Self Enquiry
| 3
| 16
|
Salaried
|
Male
| 3
| 3
|
Basic
| 3
|
Single
| 2
| 0
| 4
| 1
| 0
|
Executive
| 17,642
|
60
|
Company Invited
| 3
| 34
|
Small Business
|
Female
| 3
| 4
|
Standard
| 5
|
Married
| 5
| 0
| 1
| 1
| 0
|
Senior Manager
| 25,266
|
27
|
Self Enquiry
| 1
| 11
|
Salaried
|
Female
| 2
| 3
|
Basic
| 4
|
Single
| 2
| 1
| 3
| 0
| 1
|
Executive
| 17,478
|
44
|
Company Invited
| 1
| 16
|
Small Business
|
Male
| 4
| 4
|
Deluxe
| 3
|
Married
| 5
| 1
| 3
| 1
| 3
|
Manager
| 24,357
|
36
|
Company Invited
| 1
| 30
|
Salaried
|
Female
| 2
| 3
|
Deluxe
| 4
|
Married
| 1
| 0
| 3
| 1
| 1
|
Manager
| 20,674
|
46
|
Self Enquiry
| 3
| 9
|
Salaried
|
Female
| 2
| 3
|
Super Deluxe
| 4
|
Divorced
| 1
| 0
| 4
| 1
| 0
|
AVP
| 31,606
|
36
|
Company Invited
| 1
| 9
|
Salaried
|
Female
| 3
| 5
|
Basic
| 5
|
Unmarried
| 4
| 1
| 3
| 0
| 1
|
Executive
| 22,184
|
40
|
Self Enquiry
| 1
| 8
|
Small Business
|
Male
| 3
| 3
|
Basic
| 3
|
Married
| 3
| 0
| 1
| 0
| 0
|
Executive
| 17,345
|
53
|
Self Enquiry
| 3
| 12
|
Salaried
|
Male
| 4
| 4
|
Standard
| 4
|
Unmarried
| 2
| 1
| 4
| 1
| 2
|
Senior Manager
| 27,124
|
32
|
Self Enquiry
| 1
| 9
|
Small Business
|
Female
| 3
| 3
|
Deluxe
| 5
|
Divorced
| 2
| 0
| 2
| 1
| 1
|
Manager
| 21,725
|
47
|
Self Enquiry
| 3
| 8
|
Salaried
|
Female
| 3
| 4
|
Basic
| 3
|
Single
| 2
| 1
| 1
| 0
| 0
|
Executive
| 18,294
|
26
|
Self Enquiry
| 3
| 33
|
Small Business
|
Female
| 3
| 4
|
Deluxe
| 3
|
Unmarried
| 3
| 0
| 4
| 0
| 1
|
Manager
| 24,858
|
41
|
Self Enquiry
| 3
| 29
|
Salaried
|
Male
| 3
| 3
|
Standard
| 3
|
Unmarried
| 4
| 1
| 3
| 0
| 2
|
Senior Manager
| 22,082
|
32
|
Company Invited
| 3
| 33
|
Small Business
|
Male
| 4
| 4
|
Deluxe
| 3
|
Married
| 3
| 1
| 3
| 0
| 2
|
Manager
| 24,295
|
28
|
Self Enquiry
| 1
| 7
|
Small Business
|
Male
| 3
| 4
|
Basic
| 4
|
Married
| 3
| 0
| 3
| 0
| 1
|
Executive
| 22,494
|
30
|
Self Enquiry
| 3
| 6
|
Salaried
|
Male
| 3
| 4
|
Deluxe
| 5
|
Married
| 2
| 0
| 4
| 1
| 1
|
Manager
| 24,714
|
33
|
Self Enquiry
| 3
| 14
|
Salaried
|
Male
| 2
| 3
|
Deluxe
| 5
|
Married
| 3
| 0
| 5
| 1
| 0
|
Manager
| 21,392
|
34
|
Self Enquiry
| 3
| 12
|
Small Business
|
Female
| 3
| 4
|
Basic
| 3
|
Unmarried
| 3
| 1
| 2
| 1
| 1
|
Executive
| 21,529
|
22
|
Self Enquiry
| 3
| 12
|
Small Business
|
Male
| 3
| 4
|
Basic
| 5
|
Single
| 3
| 1
| 1
| 1
| 1
|
Executive
| 21,058
|
19
|
Self Enquiry
| 1
| 31
|
Salaried
|
Female
| 2
| 1
|
Basic
| 3
|
Single
| 2
| 1
| 5
| 1
| 0
|
Executive
| 17,994
|
56
|
Self Enquiry
| 1
| 30
|
Salaried
|
Male
| 3
| 3
|
Basic
| 3
|
Single
| 2
| 0
| 3
| 0
| 0
|
Executive
| 17,587
|
45
|
Self Enquiry
| 3
| 7
|
Small Business
|
Female
| 4
| 4
|
Deluxe
| 5
|
Married
| 7
| 1
| 2
| 0
| 1
|
Manager
| 24,132
|
34
|
Self Enquiry
| 2
| 15
|
Large Business
|
Female
| 2
| 3
|
Basic
| 3
|
Divorced
| 2
| 0
| 1
| 1
| 0
|
Executive
| 17,742
|
26
|
Self Enquiry
| 1
| 7
|
Salaried
|
Female
| 4
| 4
|
Deluxe
| 3
|
Divorced
| 2
| 0
| 3
| 1
| 2
|
Manager
| 23,576
|
39
|
Company Invited
| 1
| 36
|
Salaried
|
Female
| 3
| 4
|
Deluxe
| 3
|
Single
| 3
| 0
| 3
| 1
| 1
|
Manager
| 21,084
|
35
|
Self Enquiry
| 1
| 13
|
Small Business
|
Male
| 3
| 4
|
Basic
| 5
|
Unmarried
| 4
| 0
| 4
| 0
| 1
|
Executive
| 21,638
|
32
|
Self Enquiry
| 1
| 18
|
Salaried
|
Male
| 3
| 4
|
Basic
| 3
|
Divorced
| 5
| 1
| 3
| 1
| 2
|
Executive
| 21,034
|
46
|
Self Enquiry
| 1
| 14
|
Salaried
|
Male
| 3
| 4
|
Standard
| 5
|
Married
| 4
| 0
| 3
| 0
| 1
|
Senior Manager
| 28,402
|
40
|
Self Enquiry
| 3
| 15
|
Small Business
|
Male
| 2
| 3
|
Deluxe
| 5
|
Married
| 1
| 0
| 3
| 1
| 1
|
Manager
| 20,473
|
40
|
Self Enquiry
| 3
| 28
|
Small Business
|
Male
| 3
| 4
|
Deluxe
| 3
|
Married
| 3
| 1
| 3
| 1
| 0
|
Manager
| 21,380
|
34
|
Self Enquiry
| 1
| 17
|
Small Business
|
Male
| 3
| 6
|
Basic
| 3
|
Married
| 2
| 0
| 5
| 0
| 1
|
Executive
| 22,086
|
32
|
Company Invited
| 1
| 10
|
Salaried
|
Female
| 3
| 4
|
Basic
| 3
|
Unmarried
| 3
| 0
| 4
| 1
| 2
|
Executive
| 22,762
|
31
|
Self Enquiry
| 3
| 14
|
Small Business
|
Female
| 4
| 4
|
Deluxe
| 5
|
Married
| 2
| 0
| 3
| 1
| 1
|
Manager
| 23,457
|
41
|
Company Invited
| 3
| 12
|
Salaried
|
Male
| 3
| 5
|
Standard
| 5
|
Married
| 7
| 1
| 5
| 0
| 1
|
Senior Manager
| 29,153
|
29
|
Self Enquiry
| 1
| 26
|
Small Business
|
Male
| 4
| 5
|
Basic
| 5
|
Divorced
| 3
| 0
| 3
| 1
| 3
|
Executive
| 21,874
|
46
|
Self Enquiry
| 3
| 16
|
Small Business
|
Male
| 4
| 4
|
Standard
| 5
|
Married
| 6
| 1
| 1
| 0
| 1
|
Senior Manager
| 29,439
|
40
|
Company Invited
| 3
| 11
|
Salaried
|
Male
| 2
| 4
|
Standard
| 5
|
Married
| 6
| 1
| 5
| 0
| 0
|
Senior Manager
| 25,475
|
41
|
Company Invited
| 3
| 31
|
Salaried
|
Female
| 4
| 2
|
Super Deluxe
| 4
|
Single
| 6
| 1
| 3
| 1
| 3
|
AVP
| 31,872
|
39
|
Company Invited
| 2
| 17
|
Salaried
|
Male
| 4
| 4
|
Deluxe
| 3
|
Married
| 5
| 0
| 3
| 0
| 3
|
Manager
| 24,755
|
40
|
Company Invited
| 3
| 28
|
Salaried
|
Female
| 3
| 6
|
Deluxe
| 3
|
Married
| 8
| 0
| 5
| 0
| 1
|
Manager
| 24,414
|
48
|
Self Enquiry
| 1
| 10
|
Salaried
|
Male
| 3
| 4
|
Standard
| 3
|
Unmarried
| 1
| 0
| 5
| 1
| 1
|
Senior Manager
| 25,999
|
31
|
Self Enquiry
| 1
| 9
|
Small Business
|
Male
| 3
| 5
|
Basic
| 3
|
Unmarried
| 2
| 0
| 4
| 0
| 1
|
Executive
| 21,398
|
34
|
Self Enquiry
| 1
| 9
|
Salaried
|
Female
| 3
| 4
|
Basic
| 5
|
Married
| 2
| 0
| 3
| 1
| 2
|
Executive
| 21,385
|
30
|
Self Enquiry
| 1
| 7
|
Salaried
|
Female
| 4
| 4
|
Basic
| 3
|
Married
| 3
| 0
| 2
| 0
| 3
|
Executive
| 22,438
|
33
|
Self Enquiry
| 1
| 7
|
Salaried
|
Male
| 4
| 4
|
Basic
| 5
|
Unmarried
| 3
| 0
| 1
| 0
| 2
|
Executive
| 21,634
|
46
|
Self Enquiry
| 3
| 15
|
Small Business
|
Male
| 3
| 4
|
Standard
| 3
|
Unmarried
| 2
| 0
| 5
| 0
| 1
|
Senior Manager
| 24,619
|
49
|
Self Enquiry
| 3
| 9
|
Small Business
|
Female
| 3
| 4
|
Deluxe
| 3
|
Divorced
| 4
| 0
| 5
| 1
| 1
|
Manager
| 22,729
|
31
|
Self Enquiry
| 1
| 16
|
Small Business
|
Male
| 3
| 5
|
Basic
| 3
|
Single
| 3
| 0
| 4
| 0
| 2
|
Executive
| 20,884
|
51
|
Self Enquiry
| 1
| 9
|
Small Business
|
Female
| 3
| 3
|
Super Deluxe
| 4
|
Single
| 4
| 0
| 5
| 0
| 1
|
AVP
| 28,734
|
27
|
Company Invited
| 3
| 6
|
Small Business
|
Male
| 3
| 3
|
Deluxe
| 4
|
Divorced
| 1
| 1
| 2
| 0
| 2
|
Manager
| 21,349
|
29
|
Self Enquiry
| 1
| 13
|
Salaried
|
Male
| 2
| 3
|
Basic
| 5
|
Single
| 4
| 0
| 4
| 0
| 0
|
Executive
| 17,062
|
32
|
Self Enquiry
| 3
| 14
|
Large Business
|
Female
| 3
| 4
|
Deluxe
| 4
|
Married
| 2
| 1
| 1
| 1
| 2
|
Manager
| 20,228
|
45
|
Self Enquiry
| 1
| 16
|
Salaried
|
Male
| 3
| 3
|
Basic
| 3
|
Divorced
| 4
| 0
| 5
| 1
| 0
|
Executive
| 17,654
|
52
|
Self Enquiry
| 1
| 14
|
Salaried
|
Female
| 2
| 3
|
Basic
| 5
|
Divorced
| 1
| 0
| 1
| 1
| 1
|
Executive
| 17,950
|
28
|
Self Enquiry
| 1
| 7
|
Salaried
|
Female
| 4
| 4
|
Standard
| 5
|
Divorced
| 3
| 0
| 4
| 1
| 3
|
Senior Manager
| 26,090
|
30
|
Company Invited
| 1
| 7
|
Salaried
|
Male
| 4
| 6
|
Basic
| 3
|
Married
| 3
| 0
| 1
| 0
| 3
|
Executive
| 21,398
|
36
|
Company Invited
| 3
| 21
|
Small Business
|
Male
| 2
| 5
|
Deluxe
| 3
|
Married
| 2
| 0
| 1
| 1
| 0
|
Manager
| 20,406
|
27
|
Self Enquiry
| 3
| 12
|
Salaried
|
Female
| 2
| 5
|
Basic
| 3
|
Unmarried
| 2
| 0
| 1
| 0
| 1
|
Executive
| 21,644
|
42
|
Self Enquiry
| 3
| 13
|
Salaried
|
Female
| 4
| 4
|
Standard
| 3
|
Single
| 5
| 1
| 1
| 0
| 1
|
Senior Manager
| 32,269
|
36
|
Self Enquiry
| 1
| 18
|
Small Business
|
Female
| 2
| 4
|
Standard
| 3
|
Unmarried
| 1
| 0
| 2
| 1
| 0
|
Senior Manager
| 23,858
|
33
|
Self Enquiry
| 1
| 9
|
Large Business
|
Male
| 4
| 4
|
Basic
| 5
|
Single
| 3
| 0
| 1
| 1
| 2
|
Executive
| 21,117
|
45
|
Self Enquiry
| 1
| 31
|
Salaried
|
Female
| 3
| 4
|
Deluxe
| 3
|
Married
| 1
| 0
| 4
| 0
| 0
|
Manager
| 20,906
|
42
|
Self Enquiry
| 1
| 13
|
Small Business
|
Female
| 3
| 1
|
Deluxe
| 4
|
Divorced
| 7
| 1
| 1
| 0
| 0
|
Manager
| 17,372
|
57
|
Self Enquiry
| 3
| 18
|
Small Business
|
Female
| 3
| 5
|
Deluxe
| 5
|
Married
| 6
| 0
| 5
| 0
| 2
|
Manager
| 24,058
|
39
|
Company Invited
| 1
| 19
|
Salaried
|
Male
| 3
| 5
|
Deluxe
| 5
|
Married
| 4
| 0
| 5
| 1
| 1
|
Manager
| 24,966
|
40
|
Self Enquiry
| 2
| 9
|
Salaried
|
Female
| 3
| 5
|
Deluxe
| 3
|
Married
| 2
| 0
| 3
| 1
| 1
|
Manager
| 23,882
|
36
|
Self Enquiry
| 1
| 16
|
Salaried
|
Male
| 4
| 5
|
Deluxe
| 4
|
Unmarried
| 2
| 0
| 2
| 1
| 3
|
Manager
| 25,218
|
36
|
Self Enquiry
| 3
| 22
|
Small Business
|
Female
| 3
| 4
|
Deluxe
| 5
|
Divorced
| 5
| 0
| 5
| 1
| 0
|
Manager
| 20,647
|
19
|
Company Invited
| 1
| 15
|
Small Business
|
Male
| 4
| 4
|
Basic
| 3
|
Single
| 3
| 0
| 5
| 1
| 1
|
Executive
| 20,582
|
27
|
Company Invited
| 3
| 36
|
Small Business
|
Male
| 4
| 6
|
Deluxe
| 5
|
Unmarried
| 2
| 0
| 3
| 0
| 1
|
Manager
| 23,647
|
33
|
Self Enquiry
| 3
| 15
|
Small Business
|
Female
| 3
| 3
|
Deluxe
| 5
|
Unmarried
| 1
| 1
| 3
| 0
| 0
|
Manager
| 23,224
|
34
|
Company Invited
| 3
| 15
|
Salaried
|
Female
| 3
| 5
|
Basic
| 3
|
Single
| 2
| 0
| 2
| 1
| 2
|
Executive
| 21,020
|
50
|
Self Enquiry
| 1
| 7
|
Large Business
|
Female
| 3
| 5
|
Super Deluxe
| 3
|
Single
| 2
| 1
| 3
| 0
| 1
|
AVP
| 32,642
|
34
|
Company Invited
| 1
| 13
|
Salaried
|
Male
| 3
| 5
|
Deluxe
| 3
|
Single
| 2
| 1
| 5
| 1
| 0
|
Manager
| 21,074
|
42
|
Self Enquiry
| 1
| 19
|
Salaried
|
Male
| 3
| 4
|
Basic
| 3
|
Married
| 5
| 1
| 3
| 1
| 1
|
Executive
| 23,444
|
35
|
Self Enquiry
| 1
| 6
|
Small Business
|
Male
| 2
| 4
|
Basic
| 3
|
Married
| 7
| 0
| 1
| 1
| 1
|
Executive
| 17,258
|
31
|
Self Enquiry
| 3
| 7
|
Salaried
|
Male
| 4
| 5
|
Deluxe
| 5
|
Married
| 3
| 0
| 4
| 1
| 2
|
Manager
| 28,392
|
25
|
Self Enquiry
| 1
| 31
|
Small Business
|
Male
| 3
| 4
|
Basic
| 4
|
Unmarried
| 2
| 0
| 5
| 0
| 2
|
Executive
| 22,275
|
27
|
Company Invited
| 1
| 7
|
Salaried
|
Female
| 3
| 4
|
Deluxe
| 4
|
Married
| 3
| 0
| 5
| 0
| 2
|
Manager
| 25,075
|
45
|
Self Enquiry
| 1
| 16
|
Salaried
|
Male
| 4
| 4
|
Basic
| 5
|
Divorced
| 3
| 1
| 3
| 1
| 1
|
Executive
| 21,237
|
27
|
Self Enquiry
| 3
| 16
|
Small Business
|
Female
| 3
| 4
|
Deluxe
| 3
|
Divorced
| 2
| 1
| 3
| 1
| 2
|
Manager
| 20,769
|
35
|
Self Enquiry
| 1
| 10
|
Salaried
|
Male
| 3
| 3
|
Basic
| 3
|
Married
| 2
| 0
| 4
| 1
| 1
|
Executive
| 16,951
|
59
|
Self Enquiry
| 3
| 6
|
Large Business
|
Male
| 3
| 3
|
Standard
| 3
|
Divorced
| 4
| 1
| 2
| 0
| 1
|
Senior Manager
| 26,904
|
49
|
Self Enquiry
| 1
| 13
|
Salaried
|
Male
| 2
| 4
|
Standard
| 3
|
Unmarried
| 1
| 0
| 2
| 1
| 1
|
Senior Manager
| 25,965
|
28
|
Company Invited
| 3
| 6
|
Salaried
|
Male
| 2
| 4
|
Deluxe
| 3
|
Married
| 2
| 1
| 1
| 0
| 0
|
Manager
| 21,834
|
53
|
Self Enquiry
| 1
| 12
|
Salaried
|
Male
| 2
| 3
|
Deluxe
| 3
|
Single
| 3
| 0
| 5
| 1
| 1
|
Manager
| 17,450
|
29
|
Company Invited
| 3
| 11
|
Small Business
|
Male
| 3
| 4
|
Deluxe
| 3
|
Married
| 3
| 0
| 1
| 0
| 1
|
Manager
| 22,899
|
37
|
Self Enquiry
| 1
| 9
|
Salaried
|
Male
| 2
| 3
|
Standard
| 3
|
Divorced
| 2
| 0
| 2
| 1
| 0
|
Senior Manager
| 24,434
|
55
|
Self Enquiry
| 1
| 20
|
Small Business
|
Male
| 3
| 1
|
King
| 4
|
Single
| 1
| 0
| 1
| 1
| 1
|
VP
| 33,722
|
30
|
Self Enquiry
| 1
| 10
|
Small Business
|
Male
| 4
| 5
|
Standard
| 3
|
Married
| 3
| 0
| 1
| 0
| 3
|
Senior Manager
| 30,613
|
30
|
Company Invited
| 2
| 14
|
Salaried
|
Female
| 3
| 4
|
Basic
| 3
|
Married
| 7
| 0
| 5
| 1
| 2
|
Executive
| 17,180
|
44
|
Self Enquiry
| 1
| 9
|
Small Business
|
Female
| 4
| 3
|
Basic
| 3
|
Married
| 3
| 0
| 2
| 0
| 2
|
Executive
| 21,323
|
27
|
Self Enquiry
| 1
| 11
|
Large Business
|
Male
| 2
| 4
|
Standard
| 3
|
Married
| 2
| 1
| 3
| 0
| 0
|
Senior Manager
| 27,808
|
35
|
Company Invited
| 1
| 8
|
Small Business
|
Male
| 4
| 5
|
Deluxe
| 5
|
Unmarried
| 2
| 0
| 1
| 0
| 1
|
Manager
| 24,021
|
32
|
Self Enquiry
| 1
| 8
|
Small Business
|
Female
| 3
| 3
|
Basic
| 4
|
Divorced
| 2
| 0
| 4
| 0
| 0
|
Executive
| 17,370
|
46
|
Company Invited
| 3
| 13
|
Small Business
|
Female
| 3
| 5
|
Standard
| 3
|
Unmarried
| 8
| 0
| 4
| 1
| 1
|
Senior Manager
| 27,543
|
46
|
Self Enquiry
| 1
| 16
|
Salaried
|
Male
| 3
| 4
|
Deluxe
| 4
|
Married
| 2
| 0
| 4
| 1
| 1
|
Manager
| 21,026
|
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