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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 8 new columns ({'PitchSatisfactionScore', 'ProdTaken', 'NumberOfPersonVisiting', 'CustomerID', 'Unnamed: 0', 'NumberOfFollowups', 'DurationOfPitch', 'NumberOfChildrenVisiting'}) and 1 missing columns ({'TotalVisiting'}).

This happened while the csv dataset builder was generating data using

hf://datasets/mkrish2025/Tourism-Customer-Prediction/tourism.csv (at revision 871a443339237f510529846586dc425d960e9b4b), [/tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/Xtest.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/Xtrain.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/tourism.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/ytest.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/ytrain.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/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 1887, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, 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
              {'Age': Value('float64'), 'TypeofContact': Value('string'), 'CityTier': Value('int64'), 'Occupation': Value('string'), 'Gender': Value('string'), 'ProductPitched': Value('string'), 'PreferredPropertyStar': Value('float64'), 'MaritalStatus': Value('string'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'OwnCar': Value('int64'), 'Designation': Value('string'), 'MonthlyIncome': Value('float64'), 'TotalVisiting': 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 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 8 new columns ({'PitchSatisfactionScore', 'ProdTaken', 'NumberOfPersonVisiting', 'CustomerID', 'Unnamed: 0', 'NumberOfFollowups', 'DurationOfPitch', 'NumberOfChildrenVisiting'}) and 1 missing columns ({'TotalVisiting'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/mkrish2025/Tourism-Customer-Prediction/tourism.csv (at revision 871a443339237f510529846586dc425d960e9b4b), [/tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/Xtest.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/Xtrain.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/tourism.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/ytest.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/44259392424790-config-parquet-and-info-mkrish2025-Tourism-Custom-be52aa3f/hub/datasets--mkrish2025--Tourism-Customer-Prediction/snapshots/871a443339237f510529846586dc425d960e9b4b/ytrain.csv (origin=hf://datasets/mkrish2025/Tourism-Customer-Prediction@871a443339237f510529846586dc425d960e9b4b/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 string	CityTier int64	Occupation string	Gender string	ProductPitched string	PreferredPropertyStar float64	MaritalStatus string	NumberOfTrips float64	Passport int64	OwnCar int64	Designation string	MonthlyIncome float64	TotalVisiting float64
34	Company Invited	1	Salaried	Male	Basic	3	Married	4	0	0	Executive	17,979	2
32	Self Enquiry	1	Salaried	Male	Deluxe	4	Divorced	2	0	0	Manager	21,220	3
30	Self Enquiry	3	Salaried	Female	Standard	3	Divorced	3	0	1	Senior Manager	24,419	3
39	Self Enquiry	3	Small Business	Male	Standard	4	Unmarried	2	0	1	Senior Manager	26,029	5
37	Company Invited	1	Salaried	Female	Deluxe	4	Married	2	0	1	Manager	24,352	5
34	Self Enquiry	1	Salaried	Male	Basic	3	Single	2	0	0	Executive	21,178	5
27	Company Invited	1	Salaried	Female	Basic	3	Married	5	0	1	Executive	23,042	7
30	Self Enquiry	3	Salaried	Male	Deluxe	5	Married	2	0	1	Manager	24,714	4
53	Company Invited	1	Small Business	Female	Super Deluxe	3	Married	5	0	0	AVP	32,504	5
55	Company Invited	1	Salaried	Female	Standard	3	Married	2	0	1	Senior Manager	29,180	5
46	Company Invited	1	Small Business	Male	Standard	5	Divorced	3	1	1	Senior Manager	25,673	3
39	Company Invited	1	Salaried	Male	Deluxe	5	Married	4	0	1	Manager	24,966	3
54	Company Invited	2	Salaried	Female	Super Deluxe	3	Single	3	1	1	AVP	32,328	1
42	Self Enquiry	1	Small Business	Male	Deluxe	5	Married	6	0	1	Manager	20,538	3
33	Self Enquiry	1	Salaried	Female	Basic	3	Married	5	0	1	Executive	21,990	5
35	Self Enquiry	1	Small Business	Male	Basic	3	Single	2	0	1	Executive	17,859	1
39	Self Enquiry	1	Small Business	Male	Standard	3	Unmarried	1	0	1	Senior Manager	28,464	3
29	Self Enquiry	1	Salaried	Female	Deluxe	3	Unmarried	5	0	1	Manager	22,338	5
23	Company Invited	1	Large Business	Male	Basic	3	Unmarried	7	0	1	Executive	22,572	4
37	Company Invited	1	Small Business	Male	Basic	3	Divorced	2	1	0	Executive	17,326	2
33	Self Enquiry	1	Small Business	Female	Deluxe	5	Married	3	0	1	Manager	25,403	5
33	Self Enquiry	1	Salaried	Male	Basic	5	Unmarried	3	0	0	Executive	21,634	6
50	Company Invited	1	Salaried	Male	Deluxe	3	Married	3	1	0	Manager	25,482	5
42	Self Enquiry	1	Salaried	Female	Deluxe	3	Married	1	1	0	Manager	21,062	2
43	Company Invited	1	Small Business	Female	Standard	5	Married	5	1	0	Senior Manager	31,869	4
36	Company Invited	1	Salaried	Male	Basic	4	Married	2	0	1	Executive	17,810	3
27	Self Enquiry	3	Small Business	Female	Deluxe	3	Unmarried	1	0	0	Manager	21,500	3
29	Self Enquiry	3	Salaried	Male	Deluxe	3	Unmarried	3	0	1	Manager	23,931	6
34	Self Enquiry	1	Salaried	Female	Basic	3	Divorced	3	0	0	Executive	21,589	7
41	Self Enquiry	3	Salaried	Female	Deluxe	5	Married	3	0	0	Manager	23,317	5
32	Self Enquiry	3	Small Business	Male	Deluxe	5	Married	7	1	1	Manager	20,980	5
50	Company Invited	2	Small Business	Male	King	4	Married	2	0	1	VP	33,200	5
24	Company Invited	3	Small Business	Male	Basic	3	Married	1	0	1	Executive	17,400	3
43	Self Enquiry	1	Salaried	Female	Deluxe	3	Married	2	1	0	Manager	24,740	4
39	Self Enquiry	1	Small Business	Male	Deluxe	5	Married	3	0	1	Manager	20,377	5
55	Self Enquiry	1	Small Business	Male	King	5	Single	1	1	1	VP	34,045	3
33	Company Invited	1	Salaried	Female	Basic	3	Unmarried	3	0	1	Executive	24,887	4
34	Self Enquiry	3	Salaried	Female	Standard	5	Unmarried	4	1	0	Senior Manager	27,242	5
25	Self Enquiry	1	Salaried	Male	Basic	3	Married	2	0	0	Executive	21,452	4
30	Self Enquiry	1	Salaried	Female	Basic	3	Single	2	0	1	Executive	17,632	5
32	Company Invited	3	Small Business	Female	Basic	4	Married	3	0	0	Executive	21,467	4
34	Company Invited	1	Salaried	Female	Standard	4	Divorced	8	0	1	Senior Manager	30,556	7
50	Self Enquiry	1	Salaried	Male	Super Deluxe	3	Married	4	1	1	AVP	28,973	5
33	Self Enquiry	1	Salaried	Male	Basic	5	Single	4	1	0	Executive	17,799	3
36	Company Invited	3	Small Business	Male	Deluxe	3	Married	3	0	0	Manager	23,646	4
50	Company Invited	1	Salaried	Male	Deluxe	3	Married	3	1	0	Manager	25,482	6
49	Company Invited	3	Small Business	Female	Basic	3	Married	4	1	1	Executive	21,333	6
37	Company Invited	3	Small Business	Female	Deluxe	5	Divorced	4	0	1	Manager	23,317	4
30	Self Enquiry	1	Salaried	Female	Basic	3	Single	2	0	1	Executive	17,632	3
23	Self Enquiry	1	Salaried	Male	Basic	3	Unmarried	2	0	0	Executive	22,053	7
34	Self Enquiry	1	Small Business	Female	Basic	4	Single	3	0	0	Executive	17,311	3
52	Self Enquiry	3	Small Business	Male	Deluxe	3	Unmarried	2	1	0	Manager	24,119	7
27	Company Invited	3	Small Business	Male	Deluxe	5	Unmarried	2	0	0	Manager	23,647	5
40	Company Invited	3	Salaried	Female	Super Deluxe	4	Unmarried	5	1	1	AVP	28,194	5
44	Self Enquiry	1	Salaried	Female	Basic	3	Divorced	2	0	1	Executive	17,011	3
27	Company Invited	1	Salaried	Male	Basic	5	Married	8	1	0	Executive	20,720	4
42	Company Invited	1	Salaried	Male	Basic	5	Married	8	0	1	Executive	20,785	5
28	Self Enquiry	3	Small Business	Male	Basic	5	Married	2	0	0	Executive	21,719	5
59	Self Enquiry	1	Large Business	Female	Standard	4	Married	4	1	1	Senior Manager	29,230	5
40	Self Enquiry	3	Salaried	Male	Deluxe	3	Divorced	5	1	0	Manager	24,798	5
29	Company Invited	2	Salaried	Male	Basic	3	Married	3	0	0	Executive	21,384	5
35	Self Enquiry	1	Salaried	Female	Deluxe	5	Married	5	0	1	Manager	23,799	4
34	Self Enquiry	2	Large Business	Female	Basic	3	Divorced	2	0	1	Executive	17,742	2
36	Self Enquiry	1	Salaried	Male	Deluxe	3	Single	2	0	1	Manager	20,810	3
41	Company Invited	1	Salaried	Male	Super Deluxe	5	Married	5	0	1	AVP	32,181	3
46	Company Invited	1	Small Business	Male	Standard	5	Married	3	1	1	Senior Manager	25,673	3
27	Self Enquiry	3	Small Business	Male	Deluxe	3	Married	7	0	1	Manager	22,984	4
32	Company Invited	3	Salaried	Male	Basic	3	Married	2	0	1	Executive	21,469	5
38	Self Enquiry	1	Salaried	Male	Basic	4	Married	6	0	0	Executive	21,700	6
34	Company Invited	3	Small Business	Male	Deluxe	4	Married	2	0	0	Manager	24,824	5
51	Self Enquiry	2	Salaried	Male	Super Deluxe	4	Married	2	1	1	AVP	29,026	3
40	Self Enquiry	1	Small Business	Female	Basic	3	Single	1	1	1	Executive	17,342	3
49	Self Enquiry	1	Salaried	Male	Standard	3	Unmarried	1	0	1	Senior Manager	25,965	2
48	Self Enquiry	1	Salaried	Female	Basic	3	Single	6	0	1	Executive	20,783	5
29	Self Enquiry	3	Small Business	Male	Deluxe	3	Married	3	0	1	Manager	21,931	2
25	Company Invited	3	Small Business	Male	Basic	3	Married	2	0	1	Executive	21,078	5
35	Self Enquiry	3	Salaried	Male	Deluxe	5	Married	4	1	0	Manager	23,966	5
30	Self Enquiry	3	Small Business	Female	Deluxe	4	Married	3	1	1	Manager	26,946	4
35	Self Enquiry	1	Salaried	Male	Deluxe	3	Married	4	1	1	Manager	20,916	2
36	Self Enquiry	1	Salaried	Female	Basic	3	Married	5	0	1	Executive	17,543	3
50	Self Enquiry	3	Small Business	Male	King	3	Married	5	1	0	VP	34,331	3
44	Self Enquiry	3	Small Business	Male	Standard	3	Married	7	0	1	Senior Manager	29,476	6
38	Self Enquiry	3	Small Business	Male	Standard	4	Unmarried	1	0	1	Senior Manager	22,351	2
37	Self Enquiry	1	Salaried	Male	Basic	4	Single	4	0	0	Executive	20,691	7
32	Self Enquiry	2	Salaried	Male	Deluxe	5	Divorced	5	0	0	Manager	25,088	6
42	Company Invited	3	Salaried	Male	Deluxe	3	Unmarried	2	0	0	Manager	24,908	5
50	Self Enquiry	1	Small Business	Male	Basic	3	Divorced	2	1	1	Executive	18,221	5
25	Company Invited	1	Salaried	Female	Basic	3	Married	3	1	0	Executive	21,564	4
19	Self Enquiry	1	Salaried	Male	Basic	5	Single	2	0	0	Executive	17,552	2
41	Self Enquiry	3	Small Business	Male	Standard	4	Married	4	0	0	Senior Manager	28,383	5
47	Company Invited	1	Small Business	Female	Standard	3	Divorced	7	0	1	Senior Manager	29,205	4
32	Company Invited	3	Small Business	Female	Deluxe	3	Divorced	3	0	1	Manager	25,610	4
44	Self Enquiry	3	Small Business	Female	Super Deluxe	3	Divorced	4	1	1	AVP	28,320	3
51	Self Enquiry	3	Small Business	Male	Basic	4	Divorced	2	0	1	Executive	22,553	4
37	Self Enquiry	1	Salaried	Female	Deluxe	3	Married	2	0	0	Manager	21,474	2
36	Self Enquiry	1	Small Business	Male	Basic	5	Single	3	0	0	Executive	21,128	7
30	Self Enquiry	1	Salaried	Male	Basic	5	Divorced	3	1	1	Executive	20,797	6
43	Self Enquiry	3	Small Business	Female	Deluxe	3	Unmarried	2	0	1	Manager	24,922	5
28	Self Enquiry	3	Salaried	Male	Deluxe	3	Unmarried	3	1	0	Manager	23,156	6
33	Self Enquiry	1	Large Business	Male	Deluxe	5	Single	6	0	0	Manager	20,854	5

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