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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 3 new columns ({'Unnamed: 0', 'CustomerID', 'ProdTaken'})

This happened while the csv dataset builder was generating data using

hf://datasets/Abhilashu/tourism-project/tourism.csv (at revision 76dd86a80c2bc748cfedbdef5ccdeb0b3c07b7f3)

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
              {'Age': Value('float64'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), 'NumberOfPersonVisiting': Value('float64'), 'NumberOfFollowups': Value('float64'), 'PreferredPropertyStar': Value('float64'), 'NumberOfTrips': Value('float64'), 'Passport': Value('float64'), 'PitchSatisfactionScore': Value('float64'), 'OwnCar': Value('float64'), 'NumberOfChildrenVisiting': Value('float64'), 'MonthlyIncome': 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 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 3 new columns ({'Unnamed: 0', 'CustomerID', 'ProdTaken'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Abhilashu/tourism-project/tourism.csv (at revision 76dd86a80c2bc748cfedbdef5ccdeb0b3c07b7f3)
              
              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
float64
NumberOfFollowups
float64
PreferredPropertyStar
float64
NumberOfTrips
float64
Passport
float64
PitchSatisfactionScore
float64
OwnCar
float64
NumberOfChildrenVisiting
float64
MonthlyIncome
float64
TypeofContact
string
Occupation
string
Gender
string
MaritalStatus
string
Designation
string
ProductPitched
string
34
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2
4
3
4
0
1
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0
17,979
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32
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6
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3
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3
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21,220
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Manager
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30
3
11
2
3
3
3
0
4
1
1
24,419
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Salaried
Female
Divorced
Senior Manager
Standard
39
3
9
3
4
4
2
0
4
1
2
26,029
Self Enquiry
Small Business
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Single
Senior Manager
Standard
37
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31
3
4
4
2
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3
1
2
24,352
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Manager
Deluxe
34
1
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4
3
2
0
3
0
2
21,178
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Male
Single
Executive
Basic
27
1
7
4
6
3
5
0
4
1
3
23,042
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Salaried
Female
Married
Executive
Basic
30
3
6
3
4
5
2
0
4
1
1
24,714
Self Enquiry
Salaried
Male
Married
Manager
Deluxe
53
1
32
3
5
3
5
0
5
0
2
32,504
Company Invited
Small Business
Female
Married
AVP
Super Deluxe
55
1
7
3
4
3
2
0
5
1
2
29,180
Company Invited
Salaried
Female
Married
Senior Manager
Standard
46
1
6
2
4
5
3
1
2
1
1
25,673
Company Invited
Small Business
Male
Divorced
Senior Manager
Standard
39
1
19
2
5
5
4
0
5
1
1
24,966
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Salaried
Male
Married
Manager
Deluxe
54
2
32
1
2
3
3
1
3
1
0
32,328
Company Invited
Salaried
Female
Single
AVP
Super Deluxe
42
1
19
3
1
5
6
0
4
1
0
20,538
Self Enquiry
Small Business
Male
Married
Manager
Deluxe
33
1
12
3
2
3
5
0
5
1
2
21,990
Self Enquiry
Salaried
Female
Married
Executive
Basic
35
1
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1
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1
0
17,859
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Small Business
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Single
Executive
Basic
39
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3
3
1
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3
1
0
28,464
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Small Business
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Single
Senior Manager
Standard
29
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1
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22,338
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Salaried
Female
Single
Manager
Deluxe
23
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11
3
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3
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0
5
1
1
22,572
Company Invited
Large Business
Male
Single
Executive
Basic
37
1
15
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3
3
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1
2
0
0
17,326
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Small Business
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33
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1
1
1
25,403
Self Enquiry
Small Business
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Married
Manager
Deluxe
33
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21,634
Self Enquiry
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Single
Executive
Basic
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1
25,482
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21,062
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Married
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Deluxe
43
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33
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5
1
3
0
1
31,869
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Small Business
Female
Married
Senior Manager
Standard
36
1
15
3
1
4
2
0
5
1
0
17,810
Company Invited
Salaried
Male
Married
Executive
Basic
27
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1
3
1
0
1
0
1
21,500
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Small Business
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Single
Manager
Deluxe
29
3
16
4
4
3
3
0
3
1
2
23,931
Self Enquiry
Salaried
Male
Single
Manager
Deluxe
34
1
12
4
5
3
3
0
2
0
3
21,589
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Female
Divorced
Executive
Basic
41
3
21
3
4
5
3
0
3
0
2
23,317
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Salaried
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Married
Manager
Deluxe
32
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20
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1
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1
1
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Deluxe
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Senior Manager
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1
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Male
Married
Executive
Basic
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Single
Executive
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AVP
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Single
Executive
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1
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Small Business
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1
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0
2
25,482
Company Invited
Salaried
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1
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1
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Married
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Deluxe
34
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Large Business
Female
Divorced
Executive
Basic
36
1
10
2
4
3
2
0
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1
1
20,810
Self Enquiry
Salaried
Male
Single
Manager
Deluxe
41
1
16
3
4
5
5
0
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1
0
32,181
Company Invited
Salaried
Male
Married
AVP
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46
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1
25,673
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Male
Married
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Married
AVP
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Single
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Single
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Single
Executive
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Deluxe
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4
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1
2
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Married
Executive
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23
3
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1
3
0
2
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Salaried
Male
Married
Manager
Deluxe
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3
17
3
5
4
3
1
5
1
1
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Self Enquiry
Small Business
Female
Married
Manager
Deluxe
35
1
29
2
4
3
4
1
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1
0
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Self Enquiry
Salaried
Male
Married
Manager
Deluxe
36
1
8
3
3
3
5
0
5
1
0
17,543
Self Enquiry
Salaried
Female
Married
Executive
Basic
50
3
5
2
3
3
5
1
5
0
1
34,331
Self Enquiry
Small Business
Male
Married
VP
King
44
3
32
4
5
3
7
0
4
1
2
29,476
Self Enquiry
Small Business
Male
Married
Senior Manager
Standard
38
3
8
2
3
4
1
0
4
1
0
22,351
Self Enquiry
Small Business
Male
Single
Senior Manager
Standard
37
1
14
4
4
4
4
0
1
0
3
20,691
Self Enquiry
Salaried
Male
Single
Executive
Basic
32
2
9
4
5
5
5
0
3
0
2
25,088
Self Enquiry
Salaried
Male
Divorced
Manager
Deluxe
42
3
17
3
4
3
2
0
2
0
2
24,908
Company Invited
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Male
Single
Manager
Deluxe
50
1
34
3
2
3
2
1
2
1
2
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Self Enquiry
Small Business
Male
Divorced
Executive
Basic
25
1
14
3
4
3
3
1
4
0
1
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Salaried
Female
Married
Executive
Basic
19
1
15
2
3
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2
0
3
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Self Enquiry
Salaried
Male
Single
Executive
Basic
41
3
17
4
5
4
4
0
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0
1
28,383
Self Enquiry
Small Business
Male
Married
Senior Manager
Standard
47
1
25
3
4
3
7
0
3
1
1
29,205
Company Invited
Small Business
Female
Divorced
Senior Manager
Standard
32
3
27
3
4
3
3
0
2
1
1
25,610
Company Invited
Small Business
Female
Divorced
Manager
Deluxe
44
3
34
2
1
3
4
1
2
1
1
28,320
Self Enquiry
Small Business
Female
Divorced
AVP
Super Deluxe
51
3
15
3
4
4
2
0
2
1
1
22,553
Self Enquiry
Small Business
Male
Divorced
Executive
Basic
37
1
7
2
4
3
2
0
1
0
0
21,474
Self Enquiry
Salaried
Female
Married
Manager
Deluxe
36
1
7
4
5
5
3
0
1
0
3
21,128
Self Enquiry
Small Business
Male
Single
Executive
Basic
30
1
15
4
6
5
3
1
3
1
2
20,797
Self Enquiry
Salaried
Male
Divorced
Executive
Basic
43
3
21
4
5
3
2
0
3
1
1
24,922
Self Enquiry
Small Business
Female
Single
Manager
Deluxe
28
3
9
4
4
3
3
1
4
0
2
23,156
Self Enquiry
Salaried
Male
Single
Manager
Deluxe
33
1
9
3
5
5
6
0
4
0
2
20,854
Self Enquiry
Large Business
Male
Single
Manager
Deluxe
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