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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 1 new columns ({'ProdTaken'}) and 28 missing columns ({'Gender_Male', 'Passport', 'MonthlyIncome', 'NumberOfFollowups', 'NumberOfTrips', 'Designation_VP', 'MaritalStatus_Single', 'ProductPitched_Deluxe', 'MaritalStatus_Married', 'Occupation_Small Business', 'PreferredPropertyStar', 'Designation_Executive', 'CityTier', 'Designation_Senior Manager', 'Occupation_Salaried', 'ProductPitched_Super Deluxe', 'MaritalStatus_Unmarried', 'Age', 'Designation_Manager', 'ProductPitched_King', 'ProductPitched_Standard', 'NumberOfPersonVisiting', 'DurationOfPitch', 'TypeofContact_Self Enquiry', 'PitchSatisfactionScore', 'OwnCar', 'NumberOfChildrenVisiting', 'Occupation_Large Business'}).

This happened while the csv dataset builder was generating data using

hf://datasets/subhradasgupta/tourism-data1/y_train.csv (at revision 07af4ba78c293fcc9fb1f2b9f73fef169531967f)

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
              ProdTaken: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 376
              to
              {'Age': Value('float64'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), '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'), 'TypeofContact_Self Enquiry': Value('bool'), 'Occupation_Large Business': Value('bool'), 'Occupation_Salaried': Value('bool'), 'Occupation_Small Business': Value('bool'), 'Gender_Male': Value('bool'), 'ProductPitched_Deluxe': Value('bool'), 'ProductPitched_King': Value('bool'), 'ProductPitched_Standard': Value('bool'), 'ProductPitched_Super Deluxe': 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')}
              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 1 new columns ({'ProdTaken'}) and 28 missing columns ({'Gender_Male', 'Passport', 'MonthlyIncome', 'NumberOfFollowups', 'NumberOfTrips', 'Designation_VP', 'MaritalStatus_Single', 'ProductPitched_Deluxe', 'MaritalStatus_Married', 'Occupation_Small Business', 'PreferredPropertyStar', 'Designation_Executive', 'CityTier', 'Designation_Senior Manager', 'Occupation_Salaried', 'ProductPitched_Super Deluxe', 'MaritalStatus_Unmarried', 'Age', 'Designation_Manager', 'ProductPitched_King', 'ProductPitched_Standard', 'NumberOfPersonVisiting', 'DurationOfPitch', 'TypeofContact_Self Enquiry', 'PitchSatisfactionScore', 'OwnCar', 'NumberOfChildrenVisiting', 'Occupation_Large Business'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/subhradasgupta/tourism-data1/y_train.csv (at revision 07af4ba78c293fcc9fb1f2b9f73fef169531967f)
              
              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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Age float64	CityTier int64	DurationOfPitch float64	NumberOfPersonVisiting int64	NumberOfFollowups float64	PreferredPropertyStar float64	NumberOfTrips float64	Passport int64	PitchSatisfactionScore int64	OwnCar int64	NumberOfChildrenVisiting float64	MonthlyIncome float64	TypeofContact_Self Enquiry bool	Occupation_Large Business bool	Occupation_Salaried bool	Occupation_Small Business bool	Gender_Male bool	ProductPitched_Deluxe bool	ProductPitched_King bool	ProductPitched_Standard bool	ProductPitched_Super Deluxe bool	MaritalStatus_Married bool	MaritalStatus_Single bool	MaritalStatus_Unmarried bool	Designation_Executive bool	Designation_Manager bool	Designation_Senior Manager bool	Designation_VP bool
36	1	12	2	1	3	4	0	1	0	0	18,038	true	false	true	false	false	false	false	false	false	true	false	false	true	false	false	false
36	1	7	4	5	5	3	0	1	0	3	21,128	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false	false
37	2	15	4	5	5	2	0	2	1	3	21,020	true	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false
54	3	14	4	5	3	2	1	1	0	2	29,612	true	false	false	true	false	false	false	true	false	true	false	false	false	false	true	false
40	1	7	4	4	3	2	0	3	1	1	20,910	true	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false
52	1	7	4	4	3	3	0	4	1	1	21,401	true	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false
50	3	18	3	3	3	5	0	5	0	0	24,208	false	false	false	true	false	false	false	true	false	false	false	true	false	false	true	false
49	1	7	3	5	5	2	0	4	0	2	34,080	true	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false
27	3	8	2	3	5	1	0	5	1	1	18,041	true	false	false	true	true	false	false	false	false	true	false	false	true	false	false	false
38	1	7	3	5	3	3	0	5	1	2	24,671	true	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
36	3	35	4	5	3	2	0	4	1	3	21,108	true	false	true	false	false	false	false	false	false	true	false	false	true	false	false	false
28	1	14	3	3	3	2	0	4	0	1	18,310	true	false	true	false	false	false	false	false	false	false	true	false	true	false	false	false
20	1	17	3	4	5	2	1	3	1	2	17,600	true	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false
25	3	32	2	3	3	2	0	4	0	0	21,180	true	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false
40	3	30	3	1	4	5	1	3	1	0	28,194	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false
30	1	32	4	5	4	7	0	3	0	3	21,224	true	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false
34	1	7	4	4	3	3	1	3	1	1	21,364	true	false	true	false	true	false	false	false	false	false	false	true	true	false	false	false
35	1	7	4	4	4	3	0	5	0	1	23,003	true	false	false	true	true	true	false	false	false	false	false	false	false	true	false	false
45	1	34	2	4	4	2	0	3	1	0	31,704	true	true	false	false	false	false	false	false	true	false	true	false	false	false	false	false
35	1	29	2	4	3	4	1	4	1	1	20,916	true	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false
41	1	15	3	3	3	3	0	4	1	2	34,419	false	false	false	true	true	false	true	false	false	true	false	false	false	false	false	true
36	1	28	3	4	3	3	1	3	0	1	22,692	true	false	true	false	false	true	false	false	false	true	false	false	false	true	false	false
30	1	13	3	4	3	3	0	3	0	1	21,809	true	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false
50	3	18	3	4	3	3	0	4	0	2	26,332	true	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false
53	3	12	2	3	3	3	1	5	0	0	17,306	true	false	false	true	true	true	false	false	false	false	false	false	false	true	false	false
50	1	8	3	3	3	3	1	2	1	1	34,237	true	false	false	true	true	false	true	false	false	false	false	false	false	false	false	true
37	1	34	4	4	3	8	0	3	1	1	23,915	true	false	false	true	true	true	false	false	false	false	false	false	false	true	false	false
32	3	27	3	4	3	3	0	2	1	1	25,610	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false
40	1	22	2	4	3	7	0	5	1	1	20,094	false	false	false	true	true	true	false	false	false	false	true	false	false	true	false	false
36	3	9	3	4	3	1	0	3	1	0	22,644	true	false	false	true	false	false	false	true	false	true	false	false	false	false	true	false
55	3	24	2	3	4	4	0	1	1	0	31,835	true	false	true	false	false	false	false	false	true	false	true	false	false	false	false	false
36	3	25	4	4	5	2	0	3	0	1	20,822	true	false	false	true	true	true	false	false	false	true	false	false	false	true	false	false
22	1	22	3	1	3	2	1	3	0	2	17,935	false	false	false	true	true	false	false	false	false	true	false	false	true	false	false	false
30	3	7	3	4	3	3	0	3	1	1	26,596	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false
29	3	20	3	4	4	7	0	3	1	1	22,545	false	true	false	false	false	true	false	false	false	false	false	true	false	true	false	false
30	1	8	2	3	3	1	0	4	1	0	17,564	false	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false
38	1	14	2	4	3	6	0	1	0	1	32,342	true	false	false	true	true	false	false	true	false	false	true	false	false	false	true	false
19	1	9	3	3	4	2	0	3	1	0	16,483	true	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false
50	3	15	3	5	4	8	0	4	1	2	31,798	true	true	false	false	true	false	false	false	true	true	false	false	false	false	false	false
30	1	31	4	5	3	3	1	3	1	3	21,117	false	false	true	false	true	false	false	false	false	false	true	false	true	false	false	false
31	1	16	3	5	3	3	0	4	0	2	20,884	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false	false
36	1	34	3	5	5	3	0	4	1	2	21,237	true	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false
33	1	11	3	4	3	2	0	1	0	0	17,911	true	false	true	false	false	false	false	false	false	true	false	false	true	false	false	false
37	3	14	3	2	5	4	0	1	1	1	23,317	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false
57	3	13	3	3	5	2	0	3	1	1	31,890	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false
30	3	15	2	3	3	2	0	5	0	0	16,081	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false	false
38	1	14	2	4	4	2	1	1	1	0	17,133	false	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false
55	1	15	3	4	3	2	0	2	1	1	22,458	true	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false
35	3	11	4	4	3	4	1	4	0	3	28,391	true	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
59	2	12	2	3	3	1	0	4	1	0	17,267	true	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false
29	1	8	3	3	4	1	0	4	0	0	17,703	true	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false
36	1	14	4	4	4	3	1	3	1	2	24,041	true	false	true	false	true	false	false	false	false	false	false	true	true	false	false	false
37	1	9	2	3	3	2	0	2	1	0	24,434	true	false	true	false	true	false	false	true	false	false	false	false	false	false	true	false
37	3	15	3	3	4	2	0	1	1	2	23,834	false	false	false	true	true	false	false	true	false	true	false	false	false	false	true	false
35	2	10	3	4	3	2	0	1	1	1	22,415	true	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
40	1	13	3	4	4	6	0	3	0	2	27,054	true	false	true	false	false	false	false	true	false	false	false	true	false	false	true	false
31	1	9	3	5	3	7	1	2	1	2	25,555	true	false	false	true	false	true	false	false	false	false	false	true	false	true	false	false
30	3	27	3	4	3	4	0	1	1	1	25,609	true	false	false	true	true	true	false	false	false	true	false	false	false	true	false	false
39	3	30	2	4	3	1	0	1	1	1	27,031	true	false	false	true	false	false	false	false	true	true	false	false	false	false	false	false
31	1	11	3	3	5	1	0	1	0	2	24,936	false	false	false	true	true	true	false	false	false	false	false	true	false	true	false	false
30	1	9	2	3	5	6	1	4	0	0	17,657	true	true	false	false	false	false	false	false	false	false	true	false	true	false	false	false
47	3	33	4	5	4	2	0	4	1	3	26,669	false	false	true	false	true	true	false	false	false	false	false	true	false	true	false	false
58	1	13	2	4	5	1	1	4	1	0	25,008	true	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false
31	1	12	3	4	5	7	0	5	1	2	21,882	true	true	false	false	false	false	false	false	false	true	false	false	true	false	false	false
25	1	16	3	4	5	4	1	3	0	2	21,850	false	false	false	true	true	false	false	false	false	false	false	true	true	false	false	false
45	1	8	3	4	3	1	0	4	1	0	31,792	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false
22	1	9	2	4	3	1	1	3	0	1	17,853	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
50	1	8	1	3	5	3	0	3	0	0	32,856	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	false
29	1	15	4	4	3	3	0	4	0	2	21,988	true	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false
43	1	15	3	4	3	6	0	1	1	2	22,646	true	false	true	false	true	false	false	false	false	false	false	true	true	false	false	false
30	3	9	4	4	3	3	0	2	1	2	23,327	false	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false
59	1	9	4	5	3	2	0	5	0	1	21,050	true	true	false	false	false	false	false	true	false	true	false	false	false	false	true	false
49	1	22	3	3	5	5	1	1	1	1	17,587	true	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false
35	1	6	3	3	5	5	0	2	0	1	17,194	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false
35	1	10	3	5	3	5	0	1	0	1	21,657	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
23	1	15	4	4	5	3	1	3	0	2	21,076	true	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false
59	3	6	2	4	3	1	0	5	1	1	20,473	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false
34	3	7	3	2	5	3	0	4	0	2	22,715	true	false	false	true	true	true	false	false	false	true	false	false	false	true	false	false
28	3	15	3	4	4	3	0	2	0	1	24,892	true	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false
37	1	15	2	3	3	2	1	2	0	0	17,326	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false
31	1	17	2	3	3	4	1	3	1	1	17,356	true	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false
48	1	26	2	3	3	5	0	1	1	0	17,259	true	false	false	true	true	false	false	false	false	true	false	false	true	false	false	false
55	3	19	2	1	3	7	1	3	1	1	28,695	true	false	false	true	true	false	false	false	true	false	true	false	false	false	false	false
29	1	34	3	3	3	5	0	5	1	0	17,514	true	false	true	false	false	false	false	false	false	true	false	false	true	false	false	false
28	1	12	3	4	4	3	1	3	0	2	21,156	true	false	true	false	false	false	false	false	false	false	true	false	true	false	false	false
29	1	13	3	5	3	3	1	4	1	1	21,381	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
37	1	8	3	4	3	7	1	3	0	0	17,090	true	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false
37	1	21	4	4	3	2	1	5	1	3	25,264	true	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false
48	3	16	3	6	3	2	0	5	1	2	31,614	false	false	true	false	true	false	false	false	true	true	false	false	false	false	false	false
27	1	23	3	4	4	4	1	2	1	2	21,051	true	false	false	true	true	false	false	false	false	true	false	false	true	false	false	false
40	1	8	2	4	3	1	1	3	0	0	17,342	true	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false
40	3	30	3	1	4	5	1	3	1	2	28,194	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false
30	1	30	3	3	3	2	0	2	0	1	17,693	true	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
54	1	13	3	4	3	4	0	5	1	1	21,128	true	false	true	false	true	true	false	false	false	false	true	false	false	true	false	false
50	3	34	2	4	5	5	1	5	0	0	25,854	true	false	false	true	true	false	false	true	false	true	false	false	false	false	true	false
29	3	16	2	4	4	2	1	5	0	1	23,268	true	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false
29	1	9	3	5	5	2	0	4	0	1	21,239	false	false	false	true	true	false	false	false	false	false	true	false	true	false	false	false
30	1	15	3	3	5	2	0	2	0	1	18,072	false	false	false	true	true	true	false	false	false	true	false	false	false	true	false	false
46	1	17	4	4	3	5	0	5	0	3	21,332	true	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
33	1	13	2	3	3	1	0	4	0	0	26,691	true	false	false	true	true	false	false	true	false	false	false	false	false	false	true	false

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