Dataset Preview

Split (1)

train

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 6 new columns ({'Occupation', 'Designation', 'CustomerID', 'ProductPitched', 'MaritalStatus', 'ProdTaken'}) and 14 missing columns ({'Designation_Executive', 'Designation_Manager', 'Occupation_Salaried', 'Occupation_Large Business', 'Designation_VP', 'ProductPitched_Super Deluxe', 'Designation_Senior Manager', 'ProductPitched_Standard', 'MaritalStatus_Unmarried', 'ProductPitched_Deluxe', 'MaritalStatus_Married', 'Occupation_Small Business', 'ProductPitched_King', 'MaritalStatus_Single'}).

This happened while the csv dataset builder was generating data using

hf://datasets/vsakar/wellness-tourism-prediction/tourism.csv (at revision f41c52ea6443dc9ce365971b4fdd78b963236604)

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 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, 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
              {'Unnamed: 0': Value('int64'), 'Age': Value('float64'), 'TypeofContact': Value('int64'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), 'Gender': Value('int64'), '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'), 'Occupation_Large Business': Value('bool'), 'Occupation_Salaried': Value('bool'), 'Occupation_Small Business': Value('bool'), 'MaritalStatus_Married': Value('bool'), 'MaritalStatus_Single': Value('bool'), 'MaritalStatus_Unmarried': Value('bool'), 'Designation_Executive': Value('bool'), 'Designation_Manager': Value('bool'), 'Designation_Senior Manager': Value('bool'), 'Designation_VP': Value('bool'), 'ProductPitched_Deluxe': Value('bool'), 'ProductPitched_King': Value('bool'), 'ProductPitched_Standard': Value('bool'), 'ProductPitched_Super Deluxe': Value('bool')}
              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 1455, 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 1054, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, 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 1833, 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 6 new columns ({'Occupation', 'Designation', 'CustomerID', 'ProductPitched', 'MaritalStatus', 'ProdTaken'}) and 14 missing columns ({'Designation_Executive', 'Designation_Manager', 'Occupation_Salaried', 'Occupation_Large Business', 'Designation_VP', 'ProductPitched_Super Deluxe', 'Designation_Senior Manager', 'ProductPitched_Standard', 'MaritalStatus_Unmarried', 'ProductPitched_Deluxe', 'MaritalStatus_Married', 'Occupation_Small Business', 'ProductPitched_King', 'MaritalStatus_Single'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/vsakar/wellness-tourism-prediction/tourism.csv (at revision f41c52ea6443dc9ce365971b4fdd78b963236604)
              
              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.

Unnamed: 0 int64	Age float64	TypeofContact int64	CityTier int64	DurationOfPitch float64	Gender int64	NumberOfPersonVisiting int64	NumberOfFollowups float64	PreferredPropertyStar float64	NumberOfTrips float64	Passport int64	PitchSatisfactionScore int64	OwnCar int64	NumberOfChildrenVisiting float64	MonthlyIncome float64	Occupation_Large Business bool	Occupation_Salaried bool	Occupation_Small Business bool	MaritalStatus_Married bool	MaritalStatus_Single bool	MaritalStatus_Unmarried bool	Designation_Executive bool	Designation_Manager bool	Designation_Senior Manager bool	Designation_VP bool	ProductPitched_Deluxe bool	ProductPitched_King bool	ProductPitched_Standard bool	ProductPitched_Super Deluxe bool
1,214	44	1	0	8	1	3	1	3	2	1	4	1	0	22,879	false	true	false	true	false	false	false	false	true	false	false	false	true	false
3,829	35	1	2	20	2	3	4	3	3	0	1	1	2	27,306	false	false	true	true	false	false	false	false	true	false	false	false	true	false
2,622	47	1	2	7	1	4	4	5	3	0	2	1	2	29,131	false	false	true	true	false	false	false	false	true	false	false	false	true	false
1,543	32	1	0	6	2	3	3	4	2	0	3	1	0	21,220	false	true	false	true	false	false	false	true	false	false	true	false	false	false
3,144	59	1	0	9	2	3	4	3	6	0	2	1	2	21,157	true	false	false	false	true	false	true	false	false	false	false	false	false	false
907	44	1	2	11	2	2	3	4	1	0	5	1	1	33,213	false	false	true	false	false	false	false	false	false	true	false	true	false	false
1,426	32	1	0	35	1	2	4	4	2	0	3	1	0	17,837	false	true	false	false	true	false	true	false	false	false	false	false	false	false
4,269	27	1	2	7	2	3	4	3	3	0	5	0	2	23,974	false	true	false	true	false	false	false	true	false	false	true	false	false	false
261	38	0	2	8	2	2	4	3	4	0	5	1	1	20,249	false	true	false	false	false	false	false	true	false	false	true	false	false	false
4,223	32	1	0	12	2	3	4	3	2	1	4	1	1	23,499	true	false	false	true	false	false	true	false	false	false	false	false	false	false
243	40	1	0	30	2	3	3	3	2	0	3	1	1	18,319	true	false	false	true	false	false	false	true	false	false	true	false	false	false
3,533	38	1	0	20	2	3	4	3	3	0	1	0	1	22,963	false	false	true	true	false	false	false	true	false	false	true	false	false	false
228	35	0	2	6	0	3	3	3	2	0	5	1	0	23,789	false	false	true	false	false	true	false	false	true	false	false	false	true	false
1,110	35	1	0	8	1	3	3	5	2	1	1	1	1	17,074	false	true	false	true	false	false	true	false	false	false	false	false	false	false
4,350	34	1	0	17	2	3	6	3	2	0	5	0	1	22,086	false	false	true	true	false	false	true	false	false	false	false	false	false	false
3,870	33	1	0	36	1	3	5	4	3	0	3	1	1	21,515	false	true	false	false	false	true	true	false	false	false	false	false	false	false
87	51	1	0	15	2	3	3	3	4	0	3	1	0	17,075	false	true	false	false	false	false	true	false	false	false	false	false	false	false
1,365	29	0	2	30	2	2	1	5	2	0	3	1	1	16,091	true	false	false	false	true	false	true	false	false	false	false	false	false	false
378	34	0	2	25	2	3	2	3	1	1	2	1	2	20,304	false	false	true	false	true	false	false	true	false	false	true	false	false	false
2,522	38	1	0	14	2	2	4	3	6	0	2	0	1	32,342	false	false	true	false	true	false	false	false	true	false	false	false	true	false
209	46	1	0	6	2	3	3	5	1	0	2	0	0	24,396	false	false	true	true	false	false	false	false	true	false	false	false	true	false
510	54	1	1	25	2	2	3	4	3	0	3	1	0	25,725	false	false	true	false	false	false	false	false	true	false	false	false	true	false
2,022	56	1	0	15	2	2	3	3	1	0	4	0	0	26,103	false	false	true	true	false	false	false	false	false	false	false	false	false	true
385	30	0	0	10	2	2	3	3	19	1	4	1	1	17,285	true	false	false	false	true	false	true	false	false	false	false	false	false	false
1,386	26	1	0	6	2	3	3	5	1	0	5	1	2	17,867	false	false	true	false	true	false	true	false	false	false	false	false	false	false
2,060	33	1	0	13	2	2	3	3	1	0	4	1	0	26,691	false	false	true	true	false	false	false	false	true	false	false	false	true	false
1,946	24	1	0	23	2	3	4	4	2	0	3	1	1	17,127	false	true	false	true	false	false	true	false	false	false	false	false	false	false
3,768	30	1	0	36	2	4	6	3	2	0	5	1	3	25,062	false	true	false	true	false	false	false	true	false	false	true	false	false	false
1,253	33	0	2	8	1	3	3	4	1	0	1	0	0	20,147	false	false	true	false	true	false	false	true	false	false	true	false	false	false
2,230	53	0	2	8	1	2	4	4	3	0	1	1	0	22,525	false	false	true	true	false	false	false	false	true	false	false	false	true	false
3,514	29	0	2	14	2	3	4	5	2	0	3	1	2	23,576	false	true	false	false	false	true	false	true	false	false	true	false	false	false
1,372	39	1	0	15	2	2	3	5	2	0	4	1	0	20,151	false	false	true	true	false	false	false	true	false	false	true	false	false	false
4,366	46	1	2	9	2	4	4	4	2	0	5	1	3	23,483	false	true	false	true	false	false	false	true	false	false	true	false	false	false
2,466	35	1	0	14	1	3	4	4	2	0	3	1	1	30,672	false	true	false	false	true	false	false	false	true	false	false	false	true	false
4,073	35	0	2	9	1	4	4	3	8	0	5	0	1	20,909	false	false	true	true	false	false	true	false	false	false	false	false	false	false
4,596	33	0	0	7	1	4	5	4	8	0	3	0	3	21,010	false	true	false	true	false	false	true	false	false	false	false	false	false	false
2,373	29	0	0	16	1	2	4	3	2	0	4	1	0	21,623	false	true	false	false	false	true	true	false	false	false	false	false	false	false
1,916	41	0	2	16	2	2	3	3	1	0	1	0	1	21,230	false	true	false	false	true	false	false	true	false	false	true	false	false	false
3,268	43	1	0	36	2	3	6	3	6	0	3	1	1	22,950	false	false	true	false	false	true	false	true	false	false	true	false	false	false
4,329	35	0	2	13	1	3	6	3	2	0	4	0	2	21,029	false	false	true	true	false	false	true	false	false	false	false	false	false	false
1,685	41	1	2	12	1	3	3	3	4	1	1	0	0	28,591	false	true	false	false	true	false	false	false	true	false	false	false	true	false
694	33	1	0	6	1	2	4	3	1	0	4	0	0	21,949	false	true	false	false	false	true	false	true	false	false	true	false	false	false
837	40	0	0	15	0	2	3	3	1	0	4	0	0	28,499	false	false	true	false	false	true	false	false	true	false	false	false	true	false
1,852	26	0	0	9	2	3	3	5	1	0	3	0	1	18,102	true	false	false	false	true	false	true	false	false	false	false	false	false	false
1,712	41	1	0	25	2	2	3	5	3	0	1	0	0	18,072	false	true	false	true	false	false	false	true	false	false	true	false	false	false
222	37	0	0	17	2	2	3	3	2	1	3	0	1	27,185	false	true	false	true	false	false	false	false	true	false	false	false	true	false
2,145	31	1	2	13	2	2	4	3	4	0	4	1	1	17,329	false	true	false	true	false	false	true	false	false	false	false	false	false	false
4,867	45	1	2	8	2	3	6	4	8	0	3	0	2	21,040	false	true	false	false	true	false	false	true	false	false	true	false	false	false
514	33	0	0	9	2	3	3	5	2	1	5	1	2	18,348	false	true	false	false	true	false	true	false	false	false	false	false	false	false
2,795	33	1	0	9	1	4	4	4	3	0	4	0	1	21,048	false	false	true	false	false	false	true	false	false	false	false	false	false	false
1,074	33	1	0	14	2	3	3	3	3	1	3	0	2	21,388	false	true	false	false	false	true	false	true	false	false	true	false	false	false
402	30	1	2	18	1	2	3	3	1	0	2	1	0	21,577	true	false	false	false	false	true	false	true	false	false	true	false	false	false
547	42	0	0	25	2	2	2	3	7	1	3	1	1	17,759	false	false	true	true	false	false	true	false	false	false	false	false	false	false
1,899	46	1	0	8	2	2	3	3	7	0	5	1	0	32,861	false	true	false	true	false	false	false	false	false	false	false	false	false	true
4,656	51	1	0	16	2	4	4	3	6	0	5	1	3	21,058	false	true	false	true	false	false	true	false	false	false	false	false	false	false
1,880	30	1	0	8	1	2	5	3	3	0	1	1	0	21,091	false	true	false	false	true	false	false	true	false	false	true	false	false	false
2,742	37	0	0	25	2	3	3	3	6	0	5	0	1	22,366	false	true	false	false	false	false	true	false	false	false	false	false	false	false
1,323	28	0	1	6	2	2	3	3	2	0	4	0	1	17,706	false	true	false	true	false	false	true	false	false	false	false	false	false	false
1,357	42	1	0	12	2	2	3	5	1	0	3	1	0	28,348	false	false	true	true	false	false	false	false	true	false	false	false	true	false
617	44	1	0	10	2	2	3	4	1	0	2	1	0	20,933	false	false	true	false	true	false	false	true	false	false	true	false	false	false
3,637	39	0	0	9	1	3	5	4	3	0	1	1	1	21,118	false	false	true	false	true	false	true	false	false	false	false	false	false	false
253	42	1	0	23	1	2	2	5	4	1	2	0	0	21,545	false	true	false	false	false	true	false	true	false	false	true	false	false	false
2,223	39	0	0	28	0	2	3	5	2	1	5	1	1	25,880	false	false	true	false	false	true	false	false	true	false	false	false	true	false
944	28	0	0	6	1	2	5	3	1	0	3	1	0	21,674	false	true	false	false	false	false	false	true	false	false	true	false	false	false
2,079	43	1	0	20	2	3	3	5	7	0	5	1	1	32,159	false	true	false	true	false	false	false	false	false	false	false	false	false	true
3,372	45	1	0	22	1	4	4	3	3	0	3	0	2	26,656	false	false	true	false	false	false	false	false	true	false	false	false	true	false
4,382	53	1	0	13	2	4	4	5	5	1	4	1	2	24,255	true	false	false	true	false	false	false	true	false	false	true	false	false	false
4,062	42	1	0	16	2	4	4	5	4	0	1	0	1	20,916	false	true	false	true	false	false	true	false	false	false	false	false	false	false
9	36	1	0	33	2	3	3	3	7	0	3	1	0	20,237	false	false	true	false	false	false	false	true	false	false	true	false	false	false
3,259	22	1	0	7	1	4	5	4	3	1	5	0	3	20,748	true	false	false	false	true	false	true	false	false	false	false	false	false	false
2,664	37	1	0	12	2	4	4	4	2	0	2	0	3	24,592	false	true	false	false	false	true	false	true	false	false	true	false	false	false
3,501	30	0	2	20	0	3	4	4	7	0	3	0	2	24,443	true	false	false	false	false	true	false	true	false	false	true	false	false	false
3,967	36	0	0	18	2	4	5	5	4	1	5	1	3	28,562	false	false	true	true	false	false	false	false	true	false	false	false	true	false
186	40	1	0	10	1	2	3	3	2	0	5	0	1	34,033	false	false	true	false	false	false	false	false	false	true	false	true	false	false
136	51	0	0	14	2	2	5	3	3	0	2	0	1	25,650	false	true	false	false	false	true	false	false	true	false	false	false	true	false
3,835	39	1	2	7	2	3	5	5	6	0	3	0	2	21,536	false	true	false	false	false	true	true	false	false	false	false	false	false	false
390	43	1	0	18	2	2	4	4	2	0	3	0	1	29,336	false	true	false	true	false	false	false	false	false	false	false	false	false	true
40	35	1	0	10	2	3	3	3	2	0	4	0	0	16,951	false	true	false	true	false	false	true	false	false	false	false	false	false	false
2,695	40	0	0	9	1	4	4	3	2	0	2	1	2	29,616	true	false	false	false	true	false	false	false	true	false	false	false	true	false
3,753	27	1	2	17	2	3	4	3	3	0	1	0	1	23,362	false	false	true	false	false	true	false	true	false	false	true	false	false	false
762	26	0	0	8	2	2	3	5	7	1	5	1	0	17,042	false	true	false	false	false	false	true	false	false	false	false	false	false	false
119	43	0	2	32	2	3	3	3	2	1	2	0	0	31,959	false	true	false	false	false	false	false	false	false	false	false	false	false	true
3,339	32	1	0	18	2	4	4	5	3	1	2	0	3	25,511	false	false	true	false	false	false	false	true	false	false	true	false	false	false
2,560	35	1	0	12	1	3	5	5	4	0	2	0	1	30,309	false	false	true	false	true	false	false	false	true	false	false	false	true	false
4,135	34	1	0	11	1	3	5	4	8	0	4	0	2	21,300	false	false	true	true	false	false	true	false	false	false	false	false	false	false
1,016	31	1	0	14	1	2	4	4	2	0	4	0	1	16,261	false	true	false	false	true	false	true	false	false	false	false	false	false	false
4,748	35	1	2	16	1	4	4	3	3	0	1	0	1	24,392	false	true	false	true	false	false	false	true	false	false	true	false	false	false
4,865	42	0	2	16	2	3	6	3	2	0	5	1	2	24,829	false	true	false	true	false	false	false	false	false	false	false	false	false	true
2,030	34	1	0	14	1	2	3	5	4	0	5	1	1	20,121	false	true	false	true	false	false	false	true	false	false	true	false	false	false
2,680	34	1	0	9	1	3	4	5	2	0	3	1	1	21,385	false	true	false	false	false	false	true	false	false	false	false	false	false	false
22	34	1	0	13	0	2	3	4	1	0	3	1	0	26,994	false	true	false	false	false	true	false	false	true	false	false	false	true	false
2,643	39	1	0	36	2	3	4	3	5	0	2	0	2	24,939	true	false	false	false	false	false	false	true	false	false	true	false	false	false
3,965	29	1	0	12	2	3	4	3	3	1	1	0	1	22,119	true	false	false	false	false	true	true	false	false	false	false	false	false	false
1,288	35	0	0	8	2	2	3	3	3	0	3	0	1	20,762	false	false	true	true	false	false	false	true	false	false	true	false	false	false
293	26	1	2	10	2	2	4	3	2	1	2	1	1	20,828	false	false	true	false	true	false	false	true	false	false	true	false	false	false
2,562	37	1	0	10	1	3	4	3	7	0	2	1	1	21,513	false	true	false	true	false	false	true	false	false	false	false	false	false	false
3,734	35	0	0	16	2	4	4	5	6	0	3	0	2	24,024	false	true	false	true	false	false	false	true	false	false	true	false	false	false
4,727	40	0	0	9	2	3	4	3	2	0	3	1	1	30,847	false	true	false	true	false	false	false	false	false	false	false	false	false	true
363	33	1	2	11	1	2	3	3	2	1	2	1	0	17,851	false	false	true	false	true	false	true	false	false	false	false	false	false	false
642	38	1	2	15	2	3	4	4	1	0	4	0	0	17,899	false	false	true	false	false	false	true	false	false	false	false	false	false	false

End of preview.

No dataset card yet

Downloads last month: 12