Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 1 new columns ({'ProdTaken'}) and 18 missing columns ({'NumberOfPersonVisiting', 'Age', 'TypeofContact', 'Designation', 'Passport', 'NumberOfFollowups', 'Gender', 'CityTier', 'MonthlyIncome', 'PitchSatisfactionScore', 'ProductPitched', 'Occupation', 'NumberOfChildrenVisiting', 'DurationOfPitch', 'OwnCar', 'PreferredPropertyStar', 'NumberOfTrips', 'MaritalStatus'}).
This happened while the csv dataset builder was generating data using
hf://datasets/neeraj-jain/turism-package-prediction/data_splits/ytest.csv (at revision 654812a2e1f4d7d3b0246d34fe0264dfae71cf9b), [/tmp/hf-datasets-cache/medium/datasets/29711451757307-config-parquet-and-info-neeraj-jain-turism-packag-ab540bd1/hub/datasets--neeraj-jain--turism-package-prediction/snapshots/654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/Xtest.csv (origin=hf://datasets/neeraj-jain/turism-package-prediction@654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/29711451757307-config-parquet-and-info-neeraj-jain-turism-packag-ab540bd1/hub/datasets--neeraj-jain--turism-package-prediction/snapshots/654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/Xtrain.csv (origin=hf://datasets/neeraj-jain/turism-package-prediction@654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/29711451757307-config-parquet-and-info-neeraj-jain-turism-packag-ab540bd1/hub/datasets--neeraj-jain--turism-package-prediction/snapshots/654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/ytest.csv (origin=hf://datasets/neeraj-jain/turism-package-prediction@654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/29711451757307-config-parquet-and-info-neeraj-jain-turism-packag-ab540bd1/hub/datasets--neeraj-jain--turism-package-prediction/snapshots/654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/ytrain.csv (origin=hf://datasets/neeraj-jain/turism-package-prediction@654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/ytrain.csv), /tmp/hf-datasets-cache/medium/datasets/29711451757307-config-parquet-and-info-neeraj-jain-turism-packag-ab540bd1/hub/datasets--neeraj-jain--turism-package-prediction/snapshots/654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/tourism.csv (origin=hf://datasets/neeraj-jain/turism-package-prediction@654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/tourism.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
ProdTaken: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 376
to
{'Age': Value('float64'), 'CityTier': Value('int64'), 'NumberOfPersonVisiting': Value('int64'), 'PreferredPropertyStar': Value('float64'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'OwnCar': Value('int64'), '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')}
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 1 new columns ({'ProdTaken'}) and 18 missing columns ({'NumberOfPersonVisiting', 'Age', 'TypeofContact', 'Designation', 'Passport', 'NumberOfFollowups', 'Gender', 'CityTier', 'MonthlyIncome', 'PitchSatisfactionScore', 'ProductPitched', 'Occupation', 'NumberOfChildrenVisiting', 'DurationOfPitch', 'OwnCar', 'PreferredPropertyStar', 'NumberOfTrips', 'MaritalStatus'}).
This happened while the csv dataset builder was generating data using
hf://datasets/neeraj-jain/turism-package-prediction/data_splits/ytest.csv (at revision 654812a2e1f4d7d3b0246d34fe0264dfae71cf9b), [/tmp/hf-datasets-cache/medium/datasets/29711451757307-config-parquet-and-info-neeraj-jain-turism-packag-ab540bd1/hub/datasets--neeraj-jain--turism-package-prediction/snapshots/654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/Xtest.csv (origin=hf://datasets/neeraj-jain/turism-package-prediction@654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/29711451757307-config-parquet-and-info-neeraj-jain-turism-packag-ab540bd1/hub/datasets--neeraj-jain--turism-package-prediction/snapshots/654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/Xtrain.csv (origin=hf://datasets/neeraj-jain/turism-package-prediction@654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/29711451757307-config-parquet-and-info-neeraj-jain-turism-packag-ab540bd1/hub/datasets--neeraj-jain--turism-package-prediction/snapshots/654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/ytest.csv (origin=hf://datasets/neeraj-jain/turism-package-prediction@654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/29711451757307-config-parquet-and-info-neeraj-jain-turism-packag-ab540bd1/hub/datasets--neeraj-jain--turism-package-prediction/snapshots/654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/ytrain.csv (origin=hf://datasets/neeraj-jain/turism-package-prediction@654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/data_splits/ytrain.csv), /tmp/hf-datasets-cache/medium/datasets/29711451757307-config-parquet-and-info-neeraj-jain-turism-packag-ab540bd1/hub/datasets--neeraj-jain--turism-package-prediction/snapshots/654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/tourism.csv (origin=hf://datasets/neeraj-jain/turism-package-prediction@654812a2e1f4d7d3b0246d34fe0264dfae71cf9b/tourism.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 | Passport int64 | OwnCar int64 | NumberOfChildrenVisiting float64 | MonthlyIncome float64 | PitchSatisfactionScore int64 | NumberOfFollowups float64 | DurationOfPitch float64 | TypeofContact string | Occupation string | Gender string | MaritalStatus string | Designation string | ProductPitched string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34 | 1 | 2 | 3 | 4 | 0 | 0 | 0 | 17,979 | 1 | 4 | 9 | Company Invited | Salaried | Male | Married | Executive | Basic |
32 | 1 | 3 | 4 | 2 | 0 | 0 | 0 | 21,220 | 3 | 3 | 6 | Self Enquiry | Salaried | Male | Divorced | Manager | Deluxe |
30 | 3 | 2 | 3 | 3 | 0 | 1 | 1 | 24,419 | 4 | 3 | 11 | Self Enquiry | Salaried | Female | Divorced | Senior Manager | Standard |
39 | 3 | 3 | 4 | 2 | 0 | 1 | 2 | 26,029 | 4 | 4 | 9 | Self Enquiry | Small Business | Male | Unmarried | Senior Manager | Standard |
37 | 1 | 3 | 4 | 2 | 0 | 1 | 2 | 24,352 | 3 | 4 | 31 | Company Invited | Salaried | Female | Married | Manager | Deluxe |
34 | 1 | 3 | 3 | 2 | 0 | 0 | 2 | 21,178 | 3 | 4 | 9 | Self Enquiry | Salaried | Male | Single | Executive | Basic |
27 | 1 | 4 | 3 | 5 | 0 | 1 | 3 | 23,042 | 4 | 6 | 7 | Company Invited | Salaried | Female | Married | Executive | Basic |
30 | 3 | 3 | 5 | 2 | 0 | 1 | 1 | 24,714 | 4 | 4 | 6 | Self Enquiry | Salaried | Male | Married | Manager | Deluxe |
53 | 1 | 3 | 3 | 5 | 0 | 0 | 2 | 32,504 | 5 | 5 | 32 | Company Invited | Small Business | Female | Married | AVP | Super Deluxe |
55 | 1 | 3 | 3 | 2 | 0 | 1 | 2 | 29,180 | 5 | 4 | 7 | Company Invited | Salaried | Female | Married | Senior Manager | Standard |
46 | 1 | 2 | 5 | 3 | 1 | 1 | 1 | 25,673 | 2 | 4 | 6 | Company Invited | Small Business | Male | Divorced | Senior Manager | Standard |
39 | 1 | 2 | 5 | 4 | 0 | 1 | 1 | 24,966 | 5 | 5 | 19 | Company Invited | Salaried | Male | Married | Manager | Deluxe |
54 | 2 | 1 | 3 | 3 | 1 | 1 | 0 | 32,328 | 3 | 2 | 32 | Company Invited | Salaried | Female | Single | AVP | Super Deluxe |
42 | 1 | 3 | 5 | 6 | 0 | 1 | 0 | 20,538 | 4 | 1 | 19 | Self Enquiry | Small Business | Male | Married | Manager | Deluxe |
33 | 1 | 3 | 3 | 5 | 0 | 1 | 2 | 21,990 | 5 | 2 | 12 | Self Enquiry | Salaried | Female | Married | Executive | Basic |
35 | 1 | 1 | 3 | 2 | 0 | 1 | 0 | 17,859 | 4 | 4 | 6 | Self Enquiry | Small Business | Male | Single | Executive | Basic |
39 | 1 | 3 | 3 | 1 | 0 | 1 | 0 | 28,464 | 3 | 3 | 16 | Self Enquiry | Small Business | Male | Unmarried | Senior Manager | Standard |
29 | 1 | 3 | 3 | 5 | 0 | 1 | 2 | 22,338 | 4 | 4 | 17 | Self Enquiry | Salaried | Female | Unmarried | Manager | Deluxe |
23 | 1 | 3 | 3 | 7 | 0 | 1 | 1 | 22,572 | 5 | 5 | 11 | Company Invited | Large Business | Male | Unmarried | Executive | Basic |
37 | 1 | 2 | 3 | 2 | 1 | 0 | 0 | 17,326 | 2 | 3 | 15 | Company Invited | Small Business | Male | Divorced | Executive | Basic |
33 | 1 | 4 | 5 | 3 | 0 | 1 | 1 | 25,403 | 1 | 4 | 10 | Self Enquiry | Small Business | Female | Married | Manager | Deluxe |
33 | 1 | 4 | 5 | 3 | 0 | 0 | 2 | 21,634 | 1 | 4 | 7 | Self Enquiry | Salaried | Male | Unmarried | Executive | Basic |
50 | 1 | 4 | 3 | 3 | 1 | 0 | 1 | 25,482 | 1 | 4 | 25 | Company Invited | Salaried | Male | Married | Manager | Deluxe |
42 | 1 | 2 | 3 | 1 | 1 | 0 | 0 | 21,062 | 3 | 4 | 6 | Self Enquiry | Salaried | Female | Married | Manager | Deluxe |
43 | 1 | 3 | 5 | 5 | 1 | 0 | 1 | 31,869 | 3 | 4 | 33 | Company Invited | Small Business | Female | Married | Senior Manager | Standard |
36 | 1 | 3 | 4 | 2 | 0 | 1 | 0 | 17,810 | 5 | 1 | 15 | Company Invited | Salaried | Male | Married | Executive | Basic |
27 | 3 | 2 | 3 | 1 | 0 | 0 | 1 | 21,500 | 1 | 1 | 8 | Self Enquiry | Small Business | Female | Unmarried | Manager | Deluxe |
29 | 3 | 4 | 3 | 3 | 0 | 1 | 2 | 23,931 | 3 | 4 | 16 | Self Enquiry | Salaried | Male | Unmarried | Manager | Deluxe |
34 | 1 | 4 | 3 | 3 | 0 | 0 | 3 | 21,589 | 2 | 5 | 12 | Self Enquiry | Salaried | Female | Divorced | Executive | Basic |
41 | 3 | 3 | 5 | 3 | 0 | 0 | 2 | 23,317 | 3 | 4 | 21 | Self Enquiry | Salaried | Female | Married | Manager | Deluxe |
32 | 3 | 4 | 5 | 7 | 1 | 1 | 1 | 20,980 | 1 | 5 | 20 | Self Enquiry | Small Business | Male | Married | Manager | Deluxe |
50 | 2 | 3 | 4 | 2 | 0 | 1 | 2 | 33,200 | 1 | 3 | 9 | Company Invited | Small Business | Male | Married | VP | King |
24 | 3 | 2 | 3 | 1 | 0 | 1 | 1 | 17,400 | 4 | 3 | 30 | Company Invited | Small Business | Male | Married | Executive | Basic |
43 | 1 | 3 | 3 | 2 | 1 | 0 | 1 | 24,740 | 3 | 5 | 7 | Self Enquiry | Salaried | Female | Married | Manager | Deluxe |
39 | 1 | 3 | 5 | 3 | 0 | 1 | 2 | 20,377 | 5 | 3 | 16 | Self Enquiry | Small Business | Male | Married | Manager | Deluxe |
55 | 1 | 2 | 5 | 1 | 1 | 1 | 1 | 34,045 | 1 | 3 | 6 | Self Enquiry | Small Business | Male | Single | VP | King |
33 | 1 | 3 | 3 | 3 | 0 | 1 | 1 | 24,887 | 4 | 4 | 10 | Company Invited | Salaried | Fe Male | Unmarried | Executive | Basic |
34 | 3 | 4 | 5 | 4 | 1 | 0 | 1 | 27,242 | 5 | 4 | 23 | Self Enquiry | Salaried | Fe Male | Unmarried | Senior Manager | Standard |
25 | 1 | 3 | 3 | 2 | 0 | 0 | 1 | 21,452 | 4 | 4 | 25 | Self Enquiry | Salaried | Male | Married | Executive | Basic |
30 | 1 | 3 | 3 | 2 | 0 | 1 | 2 | 17,632 | 1 | 3 | 24 | Self Enquiry | Salaried | Female | Single | Executive | Basic |
32 | 3 | 3 | 4 | 3 | 0 | 0 | 1 | 21,467 | 3 | 4 | 12 | Company Invited | Small Business | Female | Married | Executive | Basic |
34 | 1 | 4 | 4 | 8 | 0 | 1 | 3 | 30,556 | 3 | 4 | 12 | Company Invited | Salaried | Female | Divorced | Senior Manager | Standard |
50 | 1 | 3 | 3 | 4 | 1 | 1 | 2 | 28,973 | 4 | 3 | 30 | Self Enquiry | Salaried | Male | Married | AVP | Super Deluxe |
33 | 1 | 3 | 5 | 4 | 1 | 0 | 0 | 17,799 | 4 | 4 | 6 | Self Enquiry | Salaried | Male | Single | Executive | Basic |
36 | 3 | 3 | 3 | 3 | 0 | 0 | 1 | 23,646 | 5 | 4 | 18 | Company Invited | Small Business | Male | Married | Manager | Deluxe |
50 | 1 | 4 | 3 | 3 | 1 | 0 | 2 | 25,482 | 2 | 4 | 25 | Company Invited | Salaried | Male | Married | Manager | Deluxe |
49 | 3 | 4 | 3 | 4 | 1 | 1 | 2 | 21,333 | 4 | 4 | 14 | Company Invited | Small Business | Female | Married | Executive | Basic |
37 | 3 | 3 | 5 | 4 | 0 | 1 | 1 | 23,317 | 1 | 2 | 14 | Company Invited | Small Business | Female | Divorced | Manager | Deluxe |
30 | 1 | 3 | 3 | 2 | 0 | 1 | 0 | 17,632 | 2 | 3 | 24 | Self Enquiry | Salaried | Female | Single | Executive | Basic |
23 | 1 | 4 | 3 | 2 | 0 | 0 | 3 | 22,053 | 3 | 4 | 7 | Self Enquiry | Salaried | Male | Unmarried | Executive | Basic |
34 | 1 | 3 | 4 | 3 | 0 | 0 | 0 | 17,311 | 3 | 3 | 33 | Self Enquiry | Small Business | Female | Single | Executive | Basic |
52 | 3 | 4 | 3 | 2 | 1 | 0 | 3 | 24,119 | 5 | 4 | 28 | Self Enquiry | Small Business | Male | Unmarried | Manager | Deluxe |
27 | 3 | 4 | 5 | 2 | 0 | 0 | 1 | 23,647 | 3 | 6 | 36 | Company Invited | Small Business | Male | Unmarried | Manager | Deluxe |
40 | 3 | 3 | 4 | 5 | 1 | 1 | 2 | 28,194 | 3 | 1 | 30 | Company Invited | Salaried | Fe Male | Unmarried | AVP | Super Deluxe |
44 | 1 | 3 | 3 | 2 | 0 | 1 | 0 | 17,011 | 4 | 1 | 8 | Self Enquiry | Salaried | Female | Divorced | Executive | Basic |
27 | 1 | 3 | 5 | 8 | 1 | 0 | 1 | 20,720 | 5 | 4 | 9 | Company Invited | Salaried | Male | Married | Executive | Basic |
42 | 1 | 4 | 5 | 8 | 0 | 1 | 1 | 20,785 | 3 | 5 | 12 | Company Invited | Salaried | Male | Married | Executive | Basic |
28 | 3 | 3 | 5 | 2 | 0 | 0 | 2 | 21,719 | 5 | 4 | 9 | Self Enquiry | Small Business | Male | Married | Executive | Basic |
59 | 1 | 3 | 4 | 4 | 1 | 1 | 2 | 29,230 | 5 | 5 | 12 | Self Enquiry | Large Business | Female | Married | Senior Manager | Standard |
40 | 3 | 3 | 3 | 5 | 1 | 0 | 2 | 24,798 | 1 | 5 | 28 | Self Enquiry | Salaried | Male | Divorced | Manager | Deluxe |
29 | 2 | 3 | 3 | 3 | 0 | 0 | 2 | 21,384 | 4 | 4 | 7 | Company Invited | Salaried | Male | Married | Executive | Basic |
35 | 1 | 3 | 5 | 5 | 0 | 1 | 1 | 23,799 | 5 | 4 | 15 | Self Enquiry | Salaried | Female | Married | Manager | Deluxe |
34 | 2 | 2 | 3 | 2 | 0 | 1 | 0 | 17,742 | 1 | 3 | 15 | Self Enquiry | Large Business | Female | Divorced | Executive | Basic |
36 | 1 | 2 | 3 | 2 | 0 | 1 | 1 | 20,810 | 5 | 4 | 10 | Self Enquiry | Salaried | Male | Single | Manager | Deluxe |
41 | 1 | 3 | 5 | 5 | 0 | 1 | 0 | 32,181 | 2 | 4 | 16 | Company Invited | Salaried | Male | Married | AVP | Super Deluxe |
46 | 1 | 2 | 5 | 3 | 1 | 1 | 1 | 25,673 | 1 | 4 | 6 | Company Invited | Small Business | Male | Married | Senior Manager | Standard |
27 | 3 | 3 | 3 | 7 | 0 | 1 | 1 | 22,984 | 5 | 4 | 36 | Self Enquiry | Small Business | Male | Married | Manager | Deluxe |
32 | 3 | 4 | 3 | 2 | 0 | 1 | 1 | 21,469 | 5 | 2 | 27 | Company Invited | Salaried | Male | Married | Executive | Basic |
38 | 1 | 4 | 4 | 6 | 0 | 0 | 2 | 21,700 | 4 | 4 | 26 | Self Enquiry | Salaried | Male | Married | Executive | Basic |
34 | 3 | 4 | 4 | 2 | 0 | 0 | 1 | 24,824 | 1 | 4 | 29 | Company Invited | Small Business | Male | Married | Manager | Deluxe |
51 | 2 | 2 | 4 | 2 | 1 | 1 | 1 | 29,026 | 3 | 3 | 11 | Self Enquiry | Salaried | Male | Married | AVP | Super Deluxe |
40 | 1 | 2 | 3 | 1 | 1 | 1 | 1 | 17,342 | 3 | 4 | 8 | Self Enquiry | Small Business | Female | Single | Executive | Basic |
49 | 1 | 2 | 3 | 1 | 0 | 1 | 0 | 25,965 | 1 | 4 | 13 | Self Enquiry | Salaried | Male | Unmarried | Senior Manager | Standard |
48 | 1 | 4 | 3 | 6 | 0 | 1 | 1 | 20,783 | 3 | 4 | 16 | Self Enquiry | Salaried | Female | Single | Executive | Basic |
29 | 3 | 2 | 3 | 3 | 0 | 1 | 0 | 21,931 | 1 | 3 | 26 | Self Enquiry | Small Business | Male | Married | Manager | Deluxe |
25 | 3 | 3 | 3 | 2 | 0 | 1 | 2 | 21,078 | 4 | 4 | 31 | Company Invited | Small Business | Male | Married | Executive | Basic |
35 | 3 | 3 | 5 | 4 | 1 | 0 | 2 | 23,966 | 3 | 3 | 23 | Self Enquiry | Salaried | Male | Married | Manager | Deluxe |
30 | 3 | 3 | 4 | 3 | 1 | 1 | 1 | 26,946 | 5 | 5 | 17 | Self Enquiry | Small Business | Female | Married | Manager | Deluxe |
35 | 1 | 2 | 3 | 4 | 1 | 1 | 0 | 20,916 | 4 | 4 | 29 | Self Enquiry | Salaried | Male | Married | Manager | Deluxe |
36 | 1 | 3 | 3 | 5 | 0 | 1 | 0 | 17,543 | 5 | 3 | 8 | Self Enquiry | Salaried | Female | Married | Executive | Basic |
50 | 3 | 2 | 3 | 5 | 1 | 0 | 1 | 34,331 | 5 | 3 | 5 | Self Enquiry | Small Business | Male | Married | VP | King |
44 | 3 | 4 | 3 | 7 | 0 | 1 | 2 | 29,476 | 4 | 5 | 32 | Self Enquiry | Small Business | Male | Married | Senior Manager | Standard |
38 | 3 | 2 | 4 | 1 | 0 | 1 | 0 | 22,351 | 4 | 3 | 8 | Self Enquiry | Small Business | Male | Unmarried | Senior Manager | Standard |
37 | 1 | 4 | 4 | 4 | 0 | 0 | 3 | 20,691 | 1 | 4 | 14 | Self Enquiry | Salaried | Male | Single | Executive | Basic |
32 | 2 | 4 | 5 | 5 | 0 | 0 | 2 | 25,088 | 3 | 5 | 9 | Self Enquiry | Salaried | Male | Divorced | Manager | Deluxe |
42 | 3 | 3 | 3 | 2 | 0 | 0 | 2 | 24,908 | 2 | 4 | 17 | Company Invited | Salaried | Male | Unmarried | Manager | Deluxe |
50 | 1 | 3 | 3 | 2 | 1 | 1 | 2 | 18,221 | 2 | 2 | 34 | Self Enquiry | Small Business | Male | Divorced | Executive | Basic |
25 | 1 | 3 | 3 | 3 | 1 | 0 | 1 | 21,564 | 4 | 4 | 14 | Company Invited | Salaried | Female | Married | Executive | Basic |
19 | 1 | 2 | 5 | 2 | 0 | 0 | 0 | 17,552 | 3 | 3 | 15 | Self Enquiry | Salaried | Male | Single | Executive | Basic |
41 | 3 | 4 | 4 | 4 | 0 | 0 | 1 | 28,383 | 4 | 5 | 17 | Self Enquiry | Small Business | Male | Married | Senior Manager | Standard |
47 | 1 | 3 | 3 | 7 | 0 | 1 | 1 | 29,205 | 3 | 4 | 25 | Company Invited | Small Business | Female | Divorced | Senior Manager | Standard |
32 | 3 | 3 | 3 | 3 | 0 | 1 | 1 | 25,610 | 2 | 4 | 27 | Company Invited | Small Business | Female | Divorced | Manager | Deluxe |
44 | 3 | 2 | 3 | 4 | 1 | 1 | 1 | 28,320 | 2 | 1 | 34 | Self Enquiry | Small Business | Female | Divorced | AVP | Super Deluxe |
51 | 3 | 3 | 4 | 2 | 0 | 1 | 1 | 22,553 | 2 | 4 | 15 | Self Enquiry | Small Business | Male | Divorced | Executive | Basic |
37 | 1 | 2 | 3 | 2 | 0 | 0 | 0 | 21,474 | 1 | 4 | 7 | Self Enquiry | Salaried | Female | Married | Manager | Deluxe |
36 | 1 | 4 | 5 | 3 | 0 | 0 | 3 | 21,128 | 1 | 5 | 7 | Self Enquiry | Small Business | Male | Single | Executive | Basic |
30 | 1 | 4 | 5 | 3 | 1 | 1 | 2 | 20,797 | 3 | 6 | 15 | Self Enquiry | Salaried | Male | Divorced | Executive | Basic |
43 | 3 | 4 | 3 | 2 | 0 | 1 | 1 | 24,922 | 3 | 5 | 21 | Self Enquiry | Small Business | Fe Male | Unmarried | Manager | Deluxe |
28 | 3 | 4 | 3 | 3 | 1 | 0 | 2 | 23,156 | 4 | 4 | 9 | Self Enquiry | Salaried | Male | Unmarried | Manager | Deluxe |
33 | 1 | 3 | 5 | 6 | 0 | 0 | 2 | 20,854 | 4 | 5 | 9 | Self Enquiry | Large Business | Male | Single | Manager | Deluxe |
End of preview.
No dataset card yet
- Downloads last month
- 3