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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 ({'Unnamed: 0', 'CustomerID', 'ProdTaken'})
This happened while the csv dataset builder was generating data using
hf://datasets/vyasmax9/tourism-predict-app/tourism.csv (at revision ca596e3889f1eb415333479193f35dd79a481f3c), [/tmp/hf-datasets-cache/medium/datasets/86287230893097-config-parquet-and-info-vyasmax9-tourism-predict--7d61b280/hub/datasets--vyasmax9--tourism-predict-app/snapshots/ca596e3889f1eb415333479193f35dd79a481f3c/Xtest.csv (origin=hf://datasets/vyasmax9/tourism-predict-app@ca596e3889f1eb415333479193f35dd79a481f3c/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/86287230893097-config-parquet-and-info-vyasmax9-tourism-predict--7d61b280/hub/datasets--vyasmax9--tourism-predict-app/snapshots/ca596e3889f1eb415333479193f35dd79a481f3c/Xtrain.csv (origin=hf://datasets/vyasmax9/tourism-predict-app@ca596e3889f1eb415333479193f35dd79a481f3c/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/86287230893097-config-parquet-and-info-vyasmax9-tourism-predict--7d61b280/hub/datasets--vyasmax9--tourism-predict-app/snapshots/ca596e3889f1eb415333479193f35dd79a481f3c/tourism.csv (origin=hf://datasets/vyasmax9/tourism-predict-app@ca596e3889f1eb415333479193f35dd79a481f3c/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/86287230893097-config-parquet-and-info-vyasmax9-tourism-predict--7d61b280/hub/datasets--vyasmax9--tourism-predict-app/snapshots/ca596e3889f1eb415333479193f35dd79a481f3c/ytest.csv (origin=hf://datasets/vyasmax9/tourism-predict-app@ca596e3889f1eb415333479193f35dd79a481f3c/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/86287230893097-config-parquet-and-info-vyasmax9-tourism-predict--7d61b280/hub/datasets--vyasmax9--tourism-predict-app/snapshots/ca596e3889f1eb415333479193f35dd79a481f3c/ytrain.csv (origin=hf://datasets/vyasmax9/tourism-predict-app@ca596e3889f1eb415333479193f35dd79a481f3c/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 1800, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, 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('int64'), '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 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, 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 1802, 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 ({'Unnamed: 0', 'CustomerID', 'ProdTaken'})
This happened while the csv dataset builder was generating data using
hf://datasets/vyasmax9/tourism-predict-app/tourism.csv (at revision ca596e3889f1eb415333479193f35dd79a481f3c), [/tmp/hf-datasets-cache/medium/datasets/86287230893097-config-parquet-and-info-vyasmax9-tourism-predict--7d61b280/hub/datasets--vyasmax9--tourism-predict-app/snapshots/ca596e3889f1eb415333479193f35dd79a481f3c/Xtest.csv (origin=hf://datasets/vyasmax9/tourism-predict-app@ca596e3889f1eb415333479193f35dd79a481f3c/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/86287230893097-config-parquet-and-info-vyasmax9-tourism-predict--7d61b280/hub/datasets--vyasmax9--tourism-predict-app/snapshots/ca596e3889f1eb415333479193f35dd79a481f3c/Xtrain.csv (origin=hf://datasets/vyasmax9/tourism-predict-app@ca596e3889f1eb415333479193f35dd79a481f3c/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/86287230893097-config-parquet-and-info-vyasmax9-tourism-predict--7d61b280/hub/datasets--vyasmax9--tourism-predict-app/snapshots/ca596e3889f1eb415333479193f35dd79a481f3c/tourism.csv (origin=hf://datasets/vyasmax9/tourism-predict-app@ca596e3889f1eb415333479193f35dd79a481f3c/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/86287230893097-config-parquet-and-info-vyasmax9-tourism-predict--7d61b280/hub/datasets--vyasmax9--tourism-predict-app/snapshots/ca596e3889f1eb415333479193f35dd79a481f3c/ytest.csv (origin=hf://datasets/vyasmax9/tourism-predict-app@ca596e3889f1eb415333479193f35dd79a481f3c/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/86287230893097-config-parquet-and-info-vyasmax9-tourism-predict--7d61b280/hub/datasets--vyasmax9--tourism-predict-app/snapshots/ca596e3889f1eb415333479193f35dd79a481f3c/ytrain.csv (origin=hf://datasets/vyasmax9/tourism-predict-app@ca596e3889f1eb415333479193f35dd79a481f3c/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 int64 | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
44 | 1 | 1 | 8 | Salaried | Female | 3 | 1 | Standard | 3 | Married | 2 | 1 | 4 | 1 | 0 | Senior Manager | 22,879 |
35 | 1 | 3 | 20 | Small Business | Male | 3 | 4 | Standard | 3 | Married | 3 | 0 | 1 | 1 | 2 | Senior Manager | 27,306 |
47 | 1 | 3 | 7 | Small Business | Female | 4 | 4 | Standard | 5 | Married | 3 | 0 | 2 | 1 | 2 | Senior Manager | 29,131 |
32 | 1 | 1 | 6 | Salaried | Male | 3 | 3 | Deluxe | 4 | Married | 2 | 0 | 3 | 1 | 0 | Manager | 21,220 |
59 | 1 | 1 | 9 | Large Business | Male | 3 | 4 | Basic | 3 | Single | 6 | 0 | 2 | 1 | 2 | Executive | 21,157 |
44 | 1 | 3 | 11 | Small Business | Male | 2 | 3 | King | 4 | Divorced | 1 | 0 | 5 | 1 | 1 | VP | 33,213 |
32 | 1 | 1 | 35 | Salaried | Female | 2 | 4 | Basic | 4 | Single | 2 | 0 | 3 | 1 | 0 | Executive | 17,837 |
27 | 1 | 3 | 7 | Salaried | Male | 3 | 4 | Deluxe | 3 | Married | 3 | 0 | 5 | 0 | 2 | Manager | 23,974 |
38 | 0 | 3 | 8 | Salaried | Male | 2 | 4 | Deluxe | 3 | Divorced | 4 | 0 | 5 | 1 | 1 | Manager | 20,249 |
32 | 1 | 1 | 12 | Large Business | Male | 3 | 4 | Basic | 3 | Married | 2 | 1 | 4 | 1 | 1 | Executive | 23,499 |
40 | 1 | 1 | 30 | Large Business | Male | 3 | 3 | Deluxe | 3 | Married | 2 | 0 | 3 | 1 | 1 | Manager | 18,319 |
38 | 1 | 1 | 20 | Small Business | Male | 3 | 4 | Deluxe | 3 | Married | 3 | 0 | 1 | 0 | 1 | Manager | 22,963 |
35 | 0 | 3 | 6 | Small Business | Fe Male | 3 | 3 | Standard | 3 | Unmarried | 2 | 0 | 5 | 1 | 0 | Senior Manager | 23,789 |
35 | 1 | 1 | 8 | Salaried | Female | 3 | 3 | Basic | 5 | Married | 2 | 1 | 1 | 1 | 1 | Executive | 17,074 |
34 | 1 | 1 | 17 | Small Business | Male | 3 | 6 | Basic | 3 | Married | 2 | 0 | 5 | 0 | 1 | Executive | 22,086 |
33 | 1 | 1 | 36 | Salaried | Female | 3 | 5 | Basic | 4 | Unmarried | 3 | 0 | 3 | 1 | 1 | Executive | 21,515 |
51 | 1 | 1 | 15 | Salaried | Male | 3 | 3 | Basic | 3 | Divorced | 4 | 0 | 3 | 1 | 0 | Executive | 17,075 |
29 | 0 | 3 | 30 | Large Business | Male | 2 | 1 | Basic | 5 | Single | 2 | 0 | 3 | 1 | 1 | Executive | 16,091 |
34 | 0 | 3 | 25 | Small Business | Male | 3 | 2 | Deluxe | 3 | Single | 1 | 1 | 2 | 1 | 2 | Manager | 20,304 |
38 | 1 | 1 | 14 | Small Business | Male | 2 | 4 | Standard | 3 | Single | 6 | 0 | 2 | 0 | 1 | Senior Manager | 32,342 |
46 | 1 | 1 | 6 | Small Business | Male | 3 | 3 | Standard | 5 | Married | 1 | 0 | 2 | 0 | 0 | Senior Manager | 24,396 |
54 | 1 | 2 | 25 | Small Business | Male | 2 | 3 | Standard | 4 | Divorced | 3 | 0 | 3 | 1 | 0 | Senior Manager | 25,725 |
56 | 1 | 1 | 15 | Small Business | Male | 2 | 3 | Super Deluxe | 3 | Married | 1 | 0 | 4 | 0 | 0 | AVP | 26,103 |
30 | 0 | 1 | 10 | Large Business | Male | 2 | 3 | Basic | 3 | Single | 19 | 1 | 4 | 1 | 1 | Executive | 17,285 |
26 | 1 | 1 | 6 | Small Business | Male | 3 | 3 | Basic | 5 | Single | 1 | 0 | 5 | 1 | 2 | Executive | 17,867 |
33 | 1 | 1 | 13 | Small Business | Male | 2 | 3 | Standard | 3 | Married | 1 | 0 | 4 | 1 | 0 | Senior Manager | 26,691 |
24 | 1 | 1 | 23 | Salaried | Male | 3 | 4 | Basic | 4 | Married | 2 | 0 | 3 | 1 | 1 | Executive | 17,127 |
30 | 1 | 1 | 36 | Salaried | Male | 4 | 6 | Deluxe | 3 | Married | 2 | 0 | 5 | 1 | 3 | Manager | 25,062 |
33 | 0 | 3 | 8 | Small Business | Female | 3 | 3 | Deluxe | 4 | Single | 1 | 0 | 1 | 0 | 0 | Manager | 20,147 |
53 | 0 | 3 | 8 | Small Business | Female | 2 | 4 | Standard | 4 | Married | 3 | 0 | 1 | 1 | 0 | Senior Manager | 22,525 |
29 | 0 | 3 | 14 | Salaried | Male | 3 | 4 | Deluxe | 5 | Unmarried | 2 | 0 | 3 | 1 | 2 | Manager | 23,576 |
39 | 1 | 1 | 15 | Small Business | Male | 2 | 3 | Deluxe | 5 | Married | 2 | 0 | 4 | 1 | 0 | Manager | 20,151 |
46 | 1 | 3 | 9 | Salaried | Male | 4 | 4 | Deluxe | 4 | Married | 2 | 0 | 5 | 1 | 3 | Manager | 23,483 |
35 | 1 | 1 | 14 | Salaried | Female | 3 | 4 | Standard | 4 | Single | 2 | 0 | 3 | 1 | 1 | Senior Manager | 30,672 |
35 | 0 | 3 | 9 | Small Business | Female | 4 | 4 | Basic | 3 | Married | 8 | 0 | 5 | 0 | 1 | Executive | 20,909 |
33 | 0 | 1 | 7 | Salaried | Female | 4 | 5 | Basic | 4 | Married | 8 | 0 | 3 | 0 | 3 | Executive | 21,010 |
29 | 0 | 1 | 16 | Salaried | Female | 2 | 4 | Basic | 3 | Unmarried | 2 | 0 | 4 | 1 | 0 | Executive | 21,623 |
41 | 0 | 3 | 16 | Salaried | Male | 2 | 3 | Deluxe | 3 | Single | 1 | 0 | 1 | 0 | 1 | Manager | 21,230 |
43 | 1 | 1 | 36 | Small Business | Male | 3 | 6 | Deluxe | 3 | Unmarried | 6 | 0 | 3 | 1 | 1 | Manager | 22,950 |
35 | 0 | 3 | 13 | Small Business | Female | 3 | 6 | Basic | 3 | Married | 2 | 0 | 4 | 0 | 2 | Executive | 21,029 |
41 | 1 | 3 | 12 | Salaried | Female | 3 | 3 | Standard | 3 | Single | 4 | 1 | 1 | 0 | 0 | Senior Manager | 28,591 |
33 | 1 | 1 | 6 | Salaried | Female | 2 | 4 | Deluxe | 3 | Unmarried | 1 | 0 | 4 | 0 | 0 | Manager | 21,949 |
40 | 0 | 1 | 15 | Small Business | Fe Male | 2 | 3 | Standard | 3 | Unmarried | 1 | 0 | 4 | 0 | 0 | Senior Manager | 28,499 |
26 | 0 | 1 | 9 | Large Business | Male | 3 | 3 | Basic | 5 | Single | 1 | 0 | 3 | 0 | 1 | Executive | 18,102 |
41 | 1 | 1 | 25 | Salaried | Male | 2 | 3 | Deluxe | 5 | Married | 3 | 0 | 1 | 0 | 0 | Manager | 18,072 |
37 | 0 | 1 | 17 | Salaried | Male | 2 | 3 | Standard | 3 | Married | 2 | 1 | 3 | 0 | 1 | Senior Manager | 27,185 |
31 | 1 | 3 | 13 | Salaried | Male | 2 | 4 | Basic | 3 | Married | 4 | 0 | 4 | 1 | 1 | Executive | 17,329 |
45 | 1 | 3 | 8 | Salaried | Male | 3 | 6 | Deluxe | 4 | Single | 8 | 0 | 3 | 0 | 2 | Manager | 21,040 |
33 | 0 | 1 | 9 | Salaried | Male | 3 | 3 | Basic | 5 | Single | 2 | 1 | 5 | 1 | 2 | Executive | 18,348 |
33 | 1 | 1 | 9 | Small Business | Female | 4 | 4 | Basic | 4 | Divorced | 3 | 0 | 4 | 0 | 1 | Executive | 21,048 |
33 | 1 | 1 | 14 | Salaried | Male | 3 | 3 | Deluxe | 3 | Unmarried | 3 | 1 | 3 | 0 | 2 | Manager | 21,388 |
30 | 1 | 3 | 18 | Large Business | Female | 2 | 3 | Deluxe | 3 | Unmarried | 1 | 0 | 2 | 1 | 0 | Manager | 21,577 |
42 | 0 | 1 | 25 | Small Business | Male | 2 | 2 | Basic | 3 | Married | 7 | 1 | 3 | 1 | 1 | Executive | 17,759 |
46 | 1 | 1 | 8 | Salaried | Male | 2 | 3 | Super Deluxe | 3 | Married | 7 | 0 | 5 | 1 | 0 | AVP | 32,861 |
51 | 1 | 1 | 16 | Salaried | Male | 4 | 4 | Basic | 3 | Married | 6 | 0 | 5 | 1 | 3 | Executive | 21,058 |
30 | 1 | 1 | 8 | Salaried | Female | 2 | 5 | Deluxe | 3 | Single | 3 | 0 | 1 | 1 | 0 | Manager | 21,091 |
37 | 0 | 1 | 25 | Salaried | Male | 3 | 3 | Basic | 3 | Divorced | 6 | 0 | 5 | 0 | 1 | Executive | 22,366 |
28 | 0 | 2 | 6 | Salaried | Male | 2 | 3 | Basic | 3 | Married | 2 | 0 | 4 | 0 | 1 | Executive | 17,706 |
42 | 1 | 1 | 12 | Small Business | Male | 2 | 3 | Standard | 5 | Married | 1 | 0 | 3 | 1 | 0 | Senior Manager | 28,348 |
44 | 1 | 1 | 10 | Small Business | Male | 2 | 3 | Deluxe | 4 | Single | 1 | 0 | 2 | 1 | 0 | Manager | 20,933 |
39 | 0 | 1 | 9 | Small Business | Female | 3 | 5 | Basic | 4 | Single | 3 | 0 | 1 | 1 | 1 | Executive | 21,118 |
42 | 1 | 1 | 23 | Salaried | Female | 2 | 2 | Deluxe | 5 | Unmarried | 4 | 1 | 2 | 0 | 0 | Manager | 21,545 |
39 | 0 | 1 | 28 | Small Business | Fe Male | 2 | 3 | Standard | 5 | Unmarried | 2 | 1 | 5 | 1 | 1 | Senior Manager | 25,880 |
28 | 0 | 1 | 6 | Salaried | Female | 2 | 5 | Deluxe | 3 | Divorced | 1 | 0 | 3 | 1 | 0 | Manager | 21,674 |
43 | 1 | 1 | 20 | Salaried | Male | 3 | 3 | Super Deluxe | 5 | Married | 7 | 0 | 5 | 1 | 1 | AVP | 32,159 |
45 | 1 | 1 | 22 | Small Business | Female | 4 | 4 | Standard | 3 | Divorced | 3 | 0 | 3 | 0 | 2 | Senior Manager | 26,656 |
53 | 1 | 1 | 13 | Large Business | Male | 4 | 4 | Deluxe | 5 | Married | 5 | 1 | 4 | 1 | 2 | Manager | 24,255 |
42 | 1 | 1 | 16 | Salaried | Male | 4 | 4 | Basic | 5 | Married | 4 | 0 | 1 | 0 | 1 | Executive | 20,916 |
36 | 1 | 1 | 33 | Small Business | Male | 3 | 3 | Deluxe | 3 | Divorced | 7 | 0 | 3 | 1 | 0 | Manager | 20,237 |
22 | 1 | 1 | 7 | Large Business | Female | 4 | 5 | Basic | 4 | Single | 3 | 1 | 5 | 0 | 3 | Executive | 20,748 |
37 | 1 | 1 | 12 | Salaried | Male | 4 | 4 | Deluxe | 4 | Unmarried | 2 | 0 | 2 | 0 | 3 | Manager | 24,592 |
30 | 0 | 3 | 20 | Large Business | Fe Male | 3 | 4 | Deluxe | 4 | Unmarried | 7 | 0 | 3 | 0 | 2 | Manager | 24,443 |
36 | 0 | 1 | 18 | Small Business | Male | 4 | 5 | Standard | 5 | Married | 4 | 1 | 5 | 1 | 3 | Senior Manager | 28,562 |
40 | 1 | 1 | 10 | Small Business | Female | 2 | 3 | King | 3 | Divorced | 2 | 0 | 5 | 0 | 1 | VP | 34,033 |
51 | 0 | 1 | 14 | Salaried | Male | 2 | 5 | Standard | 3 | Unmarried | 3 | 0 | 2 | 0 | 1 | Senior Manager | 25,650 |
39 | 1 | 3 | 7 | Salaried | Male | 3 | 5 | Basic | 5 | Unmarried | 6 | 0 | 3 | 0 | 2 | Executive | 21,536 |
43 | 1 | 1 | 18 | Salaried | Male | 2 | 4 | Super Deluxe | 4 | Married | 2 | 0 | 3 | 0 | 1 | AVP | 29,336 |
35 | 1 | 1 | 10 | Salaried | Male | 3 | 3 | Basic | 3 | Married | 2 | 0 | 4 | 0 | 0 | Executive | 16,951 |
40 | 0 | 1 | 9 | Large Business | Female | 4 | 4 | Standard | 3 | Single | 2 | 0 | 2 | 1 | 2 | Senior Manager | 29,616 |
27 | 1 | 3 | 17 | Small Business | Male | 3 | 4 | Deluxe | 3 | Unmarried | 3 | 0 | 1 | 0 | 1 | Manager | 23,362 |
26 | 0 | 1 | 8 | Salaried | Male | 2 | 3 | Basic | 5 | Divorced | 7 | 1 | 5 | 1 | 0 | Executive | 17,042 |
43 | 0 | 3 | 32 | Salaried | Male | 3 | 3 | Super Deluxe | 3 | Divorced | 2 | 1 | 2 | 0 | 0 | AVP | 31,959 |
32 | 1 | 1 | 18 | Small Business | Male | 4 | 4 | Deluxe | 5 | Divorced | 3 | 1 | 2 | 0 | 3 | Manager | 25,511 |
35 | 1 | 1 | 12 | Small Business | Female | 3 | 5 | Standard | 5 | Single | 4 | 0 | 2 | 0 | 1 | Senior Manager | 30,309 |
34 | 1 | 1 | 11 | Small Business | Female | 3 | 5 | Basic | 4 | Married | 8 | 0 | 4 | 0 | 2 | Executive | 21,300 |
31 | 1 | 1 | 14 | Salaried | Female | 2 | 4 | Basic | 4 | Single | 2 | 0 | 4 | 0 | 1 | Executive | 16,261 |
35 | 1 | 3 | 16 | Salaried | Female | 4 | 4 | Deluxe | 3 | Married | 3 | 0 | 1 | 0 | 1 | Manager | 24,392 |
42 | 0 | 3 | 16 | Salaried | Male | 3 | 6 | Super Deluxe | 3 | Married | 2 | 0 | 5 | 1 | 2 | AVP | 24,829 |
34 | 1 | 1 | 14 | Salaried | Female | 2 | 3 | Deluxe | 5 | Married | 4 | 0 | 5 | 1 | 1 | Manager | 20,121 |
34 | 1 | 1 | 9 | Salaried | Female | 3 | 4 | Basic | 5 | Divorced | 2 | 0 | 3 | 1 | 1 | Executive | 21,385 |
34 | 1 | 1 | 13 | Salaried | Fe Male | 2 | 3 | Standard | 4 | Unmarried | 1 | 0 | 3 | 1 | 0 | Senior Manager | 26,994 |
39 | 1 | 1 | 36 | Large Business | Male | 3 | 4 | Deluxe | 3 | Divorced | 5 | 0 | 2 | 0 | 2 | Manager | 24,939 |
29 | 1 | 1 | 12 | Large Business | Male | 3 | 4 | Basic | 3 | Unmarried | 3 | 1 | 1 | 0 | 1 | Executive | 22,119 |
35 | 0 | 1 | 8 | Small Business | Male | 2 | 3 | Deluxe | 3 | Married | 3 | 0 | 3 | 0 | 1 | Manager | 20,762 |
26 | 1 | 3 | 10 | Small Business | Male | 2 | 4 | Deluxe | 3 | Single | 2 | 1 | 2 | 1 | 1 | Manager | 20,828 |
37 | 1 | 1 | 10 | Salaried | Female | 3 | 4 | Basic | 3 | Married | 7 | 0 | 2 | 1 | 1 | Executive | 21,513 |
35 | 0 | 1 | 16 | Salaried | Male | 4 | 4 | Deluxe | 5 | Married | 6 | 0 | 3 | 0 | 2 | Manager | 24,024 |
40 | 0 | 1 | 9 | Salaried | Male | 3 | 4 | Super Deluxe | 3 | Married | 2 | 0 | 3 | 1 | 1 | AVP | 30,847 |
33 | 1 | 3 | 11 | Small Business | Female | 2 | 3 | Basic | 3 | Single | 2 | 1 | 2 | 1 | 0 | Executive | 17,851 |
38 | 1 | 3 | 15 | Small Business | Male | 3 | 4 | Basic | 4 | Divorced | 1 | 0 | 4 | 0 | 0 | Executive | 17,899 |
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