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 7 new columns ({'TypeofContact', 'Designation', 'CustomerID', 'Occupation', 'MaritalStatus', 'ProductPitched', 'ProdTaken'}) and 15 missing columns ({'MaritalStatus_Unmarried', 'Occupation_Large Business', 'ProductPitched_Standard', 'ProductPitched_King', 'Occupation_Small Business', 'MaritalStatus_Single', 'TypeofContact_Self Enquiry', 'Occupation_Salaried', 'Designation_Manager', 'ProductPitched_Deluxe', 'Designation_VP', 'ProductPitched_Super Deluxe', 'Designation_Senior Manager', 'MaritalStatus_Married', 'Designation_Executive'}).

This happened while the csv dataset builder was generating data using

hf://datasets/bhumitps/MLops/tourism.csv (at revision f547f92eec4f89837ec1fe5228e9797b24f01c6f)

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'), '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'), 'Designation_Executive': Value('bool'), 'Designation_Manager': Value('bool'), 'Designation_Senior Manager': Value('bool'), 'Designation_VP': Value('bool'), 'MaritalStatus_Married': Value('bool'), 'MaritalStatus_Single': Value('bool'), 'MaritalStatus_Unmarried': Value('bool'), 'ProductPitched_Deluxe': Value('bool'), 'ProductPitched_King': Value('bool'), 'ProductPitched_Standard': Value('bool'), 'ProductPitched_Super Deluxe': Value('bool'), 'TypeofContact_Self Enquiry': 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 1339, 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 972, 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 7 new columns ({'TypeofContact', 'Designation', 'CustomerID', 'Occupation', 'MaritalStatus', 'ProductPitched', 'ProdTaken'}) and 15 missing columns ({'MaritalStatus_Unmarried', 'Occupation_Large Business', 'ProductPitched_Standard', 'ProductPitched_King', 'Occupation_Small Business', 'MaritalStatus_Single', 'TypeofContact_Self Enquiry', 'Occupation_Salaried', 'Designation_Manager', 'ProductPitched_Deluxe', 'Designation_VP', 'ProductPitched_Super Deluxe', 'Designation_Senior Manager', 'MaritalStatus_Married', 'Designation_Executive'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/bhumitps/MLops/tourism.csv (at revision f547f92eec4f89837ec1fe5228e9797b24f01c6f)
              
              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)

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Unnamed: 0 int64	Age float64	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	Designation_Executive bool	Designation_Manager bool	Designation_Senior Manager bool	Designation_VP bool	MaritalStatus_Married bool	MaritalStatus_Single bool	MaritalStatus_Unmarried bool	ProductPitched_Deluxe bool	ProductPitched_King bool	ProductPitched_Standard bool	ProductPitched_Super Deluxe bool	TypeofContact_Self Enquiry bool
2,273	34	1	9	2	2	4	3	4	0	1	0	0	17,979	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false
73	32	1	6	2	3	3	4	2	0	3	0	0	21,220	false	true	false	false	true	false	false	false	false	false	true	false	false	false	true
167	30	3	11	1	2	3	3	3	0	4	1	1	24,419	false	true	false	false	false	true	false	false	false	false	false	false	true	false	true
4,725	39	3	9	2	3	4	4	2	0	4	1	2	26,029	false	false	true	false	false	true	false	false	false	true	false	false	true	false	true
4,219	37	1	31	1	3	4	4	2	0	3	1	2	24,352	false	true	false	false	true	false	false	true	false	false	true	false	false	false	false
2,620	34	1	9	2	3	4	3	2	0	3	0	2	21,178	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true
3,015	27	1	7	1	4	6	3	5	0	4	1	3	23,042	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false
1,106	30	3	6	2	3	4	5	2	0	4	1	1	24,714	false	true	false	false	true	false	false	true	false	false	true	false	false	false	true
4,541	53	1	32	1	3	5	3	5	0	5	0	2	32,504	false	false	true	false	false	false	false	true	false	false	false	false	false	true	false
3,334	55	1	7	1	3	4	3	2	0	5	1	2	29,180	false	true	false	false	false	true	false	true	false	false	false	false	true	false	false
93	46	1	6	2	2	4	5	3	1	2	1	1	25,673	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false
4,519	39	1	19	2	2	5	5	4	0	5	1	1	24,966	false	true	false	false	true	false	false	true	false	false	true	false	false	false	false
112	54	2	32	1	1	2	3	3	1	3	1	0	32,328	false	true	false	false	false	false	false	false	true	false	false	false	false	true	false
1,287	42	1	19	2	3	1	5	6	0	4	1	0	20,538	false	false	true	false	true	false	false	true	false	false	true	false	false	false	true
3,583	33	1	12	1	3	2	3	5	0	5	1	2	21,990	false	true	false	true	false	false	false	true	false	false	false	false	false	false	true
1,551	35	1	6	2	1	4	3	2	0	4	1	0	17,859	false	false	true	true	false	false	false	false	true	false	false	false	false	false	true
1,284	39	1	16	2	3	3	3	1	0	3	1	0	28,464	false	false	true	false	false	true	false	false	false	true	false	false	true	false	true
3,232	29	1	17	1	3	4	3	5	0	4	1	2	22,338	false	true	false	false	true	false	false	false	false	true	true	false	false	false	true
2,887	23	1	11	2	3	5	3	7	0	5	1	1	22,572	true	false	false	true	false	false	false	false	false	true	false	false	false	false	false
294	37	1	15	2	2	3	3	2	1	2	0	0	17,326	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false
4,606	33	1	10	1	4	4	5	3	0	1	1	1	25,403	false	false	true	false	true	false	false	true	false	false	true	false	false	false	true
4,015	33	1	7	2	4	4	5	3	0	1	0	2	21,634	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true
4,115	50	1	25	2	4	4	3	3	1	1	0	1	25,482	false	true	false	false	true	false	false	true	false	false	true	false	false	false	false
2,067	42	1	6	1	2	4	3	1	1	3	0	0	21,062	false	true	false	false	true	false	false	true	false	false	true	false	false	false	true
4,801	43	1	33	1	3	4	5	5	1	3	0	1	31,869	false	false	true	false	false	true	false	true	false	false	false	false	true	false	false
1,383	36	1	15	2	3	1	4	2	0	5	1	0	17,810	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false
1,350	27	3	8	1	2	1	3	1	0	1	0	1	21,500	false	false	true	false	true	false	false	false	false	true	true	false	false	false	true
4,288	29	3	16	2	4	4	3	3	0	3	1	2	23,931	false	true	false	false	true	false	false	false	false	true	true	false	false	false	true
2,690	34	1	12	1	4	5	3	3	0	2	0	3	21,589	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true
3,576	41	3	21	1	3	4	5	3	0	3	0	2	23,317	false	true	false	false	true	false	false	true	false	false	true	false	false	false	true
4,866	32	3	20	2	4	5	5	7	1	1	1	1	20,980	false	false	true	false	true	false	false	true	false	false	true	false	false	false	true
1,947	50	2	9	2	3	3	4	2	0	1	1	2	33,200	false	false	true	false	false	false	true	true	false	false	false	true	false	false	false
1,940	24	3	30	2	2	3	3	1	0	4	1	1	17,400	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false
4,511	43	1	7	1	3	5	3	2	1	3	0	1	24,740	false	true	false	false	true	false	false	true	false	false	true	false	false	false	true
1,148	39	1	16	2	3	3	5	3	0	5	1	2	20,377	false	false	true	false	true	false	false	true	false	false	true	false	false	false	true
1,740	55	1	6	2	2	3	5	1	1	1	1	1	34,045	false	false	true	false	false	false	true	false	true	false	false	true	false	false	true
4,849	33	1	10	0	3	4	3	3	0	4	1	1	24,887	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false
3,699	34	3	23	0	4	4	5	4	1	5	0	1	27,242	false	true	false	false	false	true	false	false	false	true	false	false	true	false	true
4,027	25	1	25	2	3	4	3	2	0	4	0	1	21,452	false	true	false	true	false	false	false	true	false	false	false	false	false	false	true
1,613	30	1	24	1	3	3	3	2	0	1	1	2	17,632	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true
4,206	32	3	12	1	3	4	4	3	0	3	0	1	21,467	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false
2,757	34	1	12	1	4	4	4	8	0	3	1	3	30,556	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false
2,191	50	1	30	2	3	3	3	4	1	4	1	2	28,973	false	true	false	false	false	false	false	true	false	false	false	false	false	true	true
673	33	1	6	2	3	4	5	4	1	4	0	0	17,799	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true
3,264	36	3	18	2	3	4	3	3	0	5	0	1	23,646	false	false	true	false	true	false	false	true	false	false	true	false	false	false	false
2,645	50	1	25	2	4	4	3	3	1	2	0	2	25,482	false	true	false	false	true	false	false	true	false	false	true	false	false	false	false
3,648	49	3	14	1	4	4	3	4	1	4	1	2	21,333	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false
3,412	37	3	14	1	3	2	5	4	0	1	1	1	23,317	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false
143	30	1	24	1	3	3	3	2	0	2	1	0	17,632	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true
3,867	23	1	7	2	4	4	3	2	0	3	0	3	22,053	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true
1,651	34	1	33	1	3	3	4	3	0	3	0	0	17,311	false	false	true	true	false	false	false	false	true	false	false	false	false	false	true
4,005	52	3	28	2	4	4	3	2	1	5	0	3	24,119	false	false	true	false	true	false	false	false	false	true	true	false	false	false	true
3,192	27	3	36	2	4	6	5	2	0	3	0	1	23,647	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false
706	40	3	30	0	3	1	4	5	1	3	1	2	28,194	false	true	false	false	false	false	false	false	false	true	false	false	false	true	false
891	44	1	8	1	3	1	3	2	0	4	1	0	17,011	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true
4,676	27	1	9	2	3	4	5	8	1	5	0	1	20,720	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false
3,308	42	1	12	2	4	5	5	8	0	3	1	1	20,785	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false
2,939	28	3	9	2	3	4	5	2	0	5	0	2	21,719	false	false	true	true	false	false	false	true	false	false	false	false	false	false	true
4,588	59	1	12	1	3	5	4	4	1	5	1	2	29,230	true	false	false	false	false	true	false	true	false	false	false	false	true	false	true
3,430	40	3	28	2	3	5	3	5	1	1	0	2	24,798	false	true	false	false	true	false	false	false	false	false	true	false	false	false	true
3,767	29	2	7	2	3	4	3	3	0	4	0	2	21,384	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false
3,004	35	1	15	1	3	4	5	5	0	5	1	1	23,799	false	true	false	false	true	false	false	true	false	false	true	false	false	false	true
1,039	34	2	15	1	2	3	3	2	0	1	1	0	17,742	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true
1,018	36	1	10	2	2	4	3	2	0	5	1	1	20,810	false	true	false	false	true	false	false	false	true	false	true	false	false	false	true
147	41	1	16	2	3	4	5	5	0	2	1	0	32,181	false	true	false	false	false	false	false	true	false	false	false	false	false	true	false
1,563	46	1	6	2	2	4	5	3	1	1	1	1	25,673	false	false	true	false	false	true	false	true	false	false	false	false	true	false	false
2,904	27	3	36	2	3	4	3	7	0	5	1	1	22,984	false	false	true	false	true	false	false	true	false	false	true	false	false	false	true
4,732	32	3	27	2	4	2	3	2	0	5	1	1	21,469	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false
3,961	38	1	26	2	4	4	4	6	0	4	0	2	21,700	false	true	false	true	false	false	false	true	false	false	false	false	false	false	true
4,035	34	3	29	2	4	4	4	2	0	1	0	1	24,824	false	false	true	false	true	false	false	true	false	false	true	false	false	false	false
962	51	2	11	2	2	3	4	2	1	3	1	1	29,026	false	true	false	false	false	false	false	true	false	false	false	false	false	true	true
553	40	1	8	1	2	4	3	1	1	3	1	1	17,342	false	false	true	true	false	false	false	false	true	false	false	false	false	false	true
1,845	49	1	13	2	2	4	3	1	0	1	1	0	25,965	false	true	false	false	false	true	false	false	false	true	false	false	true	false	true
3,765	48	1	16	1	4	4	3	6	0	3	1	1	20,783	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true
1,724	29	3	26	2	2	3	3	3	0	1	1	0	21,931	false	false	true	false	true	false	false	true	false	false	true	false	false	false	true
4,384	25	3	31	2	3	4	3	2	0	4	1	2	21,078	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false
2,340	35	3	23	2	3	3	5	4	1	3	0	2	23,966	false	true	false	false	true	false	false	true	false	false	true	false	false	false	true
4,257	30	3	17	1	3	5	4	3	1	5	1	1	26,946	false	false	true	false	true	false	false	true	false	false	true	false	false	false	true
1,662	35	1	29	2	2	4	3	4	1	4	1	0	20,916	false	true	false	false	true	false	false	true	false	false	true	false	false	false	true
1,847	36	1	8	1	3	3	3	5	0	5	1	0	17,543	false	true	false	true	false	false	false	true	false	false	false	false	false	false	true
1,126	50	3	5	2	2	3	3	5	1	5	0	1	34,331	false	false	true	false	false	false	true	true	false	false	false	true	false	false	true
4,689	44	3	32	2	4	5	3	7	0	4	1	2	29,476	false	false	true	false	false	true	false	true	false	false	false	false	true	false	true
811	38	3	8	2	2	3	4	1	0	4	1	0	22,351	false	false	true	false	false	true	false	false	false	true	false	false	true	false	true
3,624	37	1	14	2	4	4	4	4	0	1	0	3	20,691	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true
2,754	32	2	9	2	4	5	5	5	0	3	0	2	25,088	false	true	false	false	true	false	false	false	false	false	true	false	false	false	true
2,890	42	3	17	2	3	4	3	2	0	2	0	2	24,908	false	true	false	false	true	false	false	false	false	true	true	false	false	false	false
523	50	1	34	2	3	2	3	2	1	2	1	2	18,221	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true
4,393	25	1	14	1	3	4	3	3	1	4	0	1	21,564	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false
853	19	1	15	2	2	3	5	2	0	3	0	0	17,552	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true
4,598	41	3	17	2	4	5	4	4	0	4	0	1	28,383	false	false	true	false	false	true	false	true	false	false	false	false	true	false	true
2,909	47	1	25	1	3	4	3	7	0	3	1	1	29,205	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false
3,123	32	3	27	1	3	4	3	3	0	2	1	1	25,610	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false
750	44	3	34	1	2	1	3	4	1	2	1	1	28,320	false	false	true	false	false	false	false	false	false	false	false	false	false	true	true
2,983	51	3	15	2	3	4	4	2	0	2	1	1	22,553	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true
2,325	37	1	7	1	2	4	3	2	0	1	0	0	21,474	false	true	false	false	true	false	false	true	false	false	true	false	false	false	true
3,552	36	1	7	2	4	5	5	3	0	1	0	3	21,128	false	false	true	true	false	false	false	false	true	false	false	false	false	false	true
2,780	30	1	15	2	4	6	5	3	1	3	1	2	20,797	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true
4,586	43	3	21	0	4	5	3	2	0	3	1	1	24,922	false	false	true	false	true	false	false	false	false	true	true	false	false	false	true
4,234	28	3	9	2	4	4	3	3	1	4	0	2	23,156	false	true	false	false	true	false	false	false	false	true	true	false	false	false	true
4,176	33	1	9	2	3	5	5	6	0	4	0	2	20,854	true	false	false	false	true	false	false	false	true	false	true	false	false	false	true

End of preview.