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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 ({'ProdTaken', 'CustomerID', 'Unnamed: 0'})

This happened while the csv dataset builder was generating data using

hf://datasets/SudeendraMG/tourism-package-purchase-prediction/tourism.csv (at revision 556d238d24bd989b9fa557c5b95ff36e0d55822d)

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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/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('int64'), 'NumberOfFollowups': Value('float64'), 'PreferredPropertyStar': Value('float64'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'OwnCar': Value('int64'), 'PitchSatisfactionScore': Value('int64'), 'NumberOfChildrenVisiting': Value('float64'), 'MonthlyIncome': Value('float64'), 'TypeofContact': Value('string'), 'Occupation': Value('string'), 'Gender': Value('string'), 'ProductPitched': Value('string'), 'MaritalStatus': Value('string'), 'Designation': 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 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/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 ({'ProdTaken', 'CustomerID', 'Unnamed: 0'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/SudeendraMG/tourism-package-purchase-prediction/tourism.csv (at revision 556d238d24bd989b9fa557c5b95ff36e0d55822d)
              
              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
CityTier
int64
DurationOfPitch
float64
NumberOfPersonVisiting
int64
NumberOfFollowups
float64
PreferredPropertyStar
float64
NumberOfTrips
float64
Passport
int64
OwnCar
int64
PitchSatisfactionScore
int64
NumberOfChildrenVisiting
float64
MonthlyIncome
float64
TypeofContact
string
Occupation
string
Gender
string
ProductPitched
string
MaritalStatus
string
Designation
string
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1
8
3
1
3
2
1
1
4
0
22,879
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Female
Standard
Married
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35
3
20
3
4
3
3
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1
1
2
27,306
Self Enquiry
Small Business
Male
Standard
Married
Senior Manager
47
3
7
4
4
5
3
0
1
2
2
29,131
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Small Business
Female
Standard
Married
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32
1
6
3
3
4
2
0
1
3
0
21,220
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Male
Deluxe
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Manager
59
1
9
3
4
3
6
0
1
2
2
21,157
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Large Business
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Basic
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Executive
44
3
11
2
3
4
1
0
1
5
1
33,213
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Small Business
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King
Divorced
VP
32
1
35
2
4
4
2
0
1
3
0
17,837
Self Enquiry
Salaried
Female
Basic
Single
Executive
27
3
7
3
4
3
3
0
0
5
2
23,974
Self Enquiry
Salaried
Male
Deluxe
Married
Manager
38
3
8
2
4
3
4
0
1
5
1
20,249
Company Invited
Salaried
Male
Deluxe
Divorced
Manager
32
1
12
3
4
3
2
1
1
4
1
23,499
Self Enquiry
Large Business
Male
Basic
Married
Executive
40
1
30
3
3
3
2
0
1
3
1
18,319
Self Enquiry
Large Business
Male
Deluxe
Married
Manager
38
1
20
3
4
3
3
0
0
1
1
22,963
Self Enquiry
Small Business
Male
Deluxe
Married
Manager
35
3
6
3
3
3
2
0
1
5
0
23,789
Company Invited
Small Business
Female
Standard
Unmarried
Senior Manager
35
1
8
3
3
5
2
1
1
1
1
17,074
Self Enquiry
Salaried
Female
Basic
Married
Executive
34
1
17
3
6
3
2
0
0
5
1
22,086
Self Enquiry
Small Business
Male
Basic
Married
Executive
33
1
36
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1
3
1
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Salaried
Female
Basic
Unmarried
Executive
51
1
15
3
3
3
4
0
1
3
0
17,075
Self Enquiry
Salaried
Male
Basic
Divorced
Executive
29
3
30
2
1
5
2
0
1
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1
16,091
Company Invited
Large Business
Male
Basic
Single
Executive
34
3
25
3
2
3
1
1
1
2
2
20,304
Company Invited
Small Business
Male
Deluxe
Single
Manager
38
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14
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3
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2
1
32,342
Self Enquiry
Small Business
Male
Standard
Single
Senior Manager
46
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2
0
24,396
Self Enquiry
Small Business
Male
Standard
Married
Senior Manager
54
2
25
2
3
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3
0
1
3
0
25,725
Self Enquiry
Small Business
Male
Standard
Divorced
Senior Manager
56
1
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3
1
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0
26,103
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Small Business
Male
Super Deluxe
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AVP
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3
19
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1
17,285
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5
1
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1
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2
17,867
Self Enquiry
Small Business
Male
Basic
Single
Executive
33
1
13
2
3
3
1
0
1
4
0
26,691
Self Enquiry
Small Business
Male
Standard
Married
Senior Manager
24
1
23
3
4
4
2
0
1
3
1
17,127
Self Enquiry
Salaried
Male
Basic
Married
Executive
30
1
36
4
6
3
2
0
1
5
3
25,062
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Salaried
Male
Deluxe
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Manager
33
3
8
3
3
4
1
0
0
1
0
20,147
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Small Business
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Single
Manager
53
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1
1
0
22,525
Company Invited
Small Business
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29
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Deluxe
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35
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Small Business
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Senior Manager
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Manager
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Manager
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Manager
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39
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28
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1
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1
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Small Business
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Standard
Unmarried
Senior Manager
28
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Salaried
Female
Deluxe
Divorced
Manager
43
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20
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3
5
7
0
1
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1
32,159
Self Enquiry
Salaried
Male
Super Deluxe
Married
AVP
45
1
22
4
4
3
3
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0
3
2
26,656
Self Enquiry
Small Business
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Standard
Divorced
Senior Manager
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13
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5
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1
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2
24,255
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Large Business
Male
Deluxe
Married
Manager
42
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4
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Salaried
Male
Basic
Married
Executive
36
1
33
3
3
3
7
0
1
3
0
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Small Business
Male
Deluxe
Divorced
Manager
22
1
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5
4
3
1
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5
3
20,748
Self Enquiry
Large Business
Female
Basic
Single
Executive
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Deluxe
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Small Business
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King
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VP
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3
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2
1
25,650
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Salaried
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Standard
Unmarried
Senior Manager
39
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5
6
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3
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21,536
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Salaried
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Basic
Unmarried
Executive
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2
4
4
2
0
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3
1
29,336
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Salaried
Male
Super Deluxe
Married
AVP
35
1
10
3
3
3
2
0
0
4
0
16,951
Self Enquiry
Salaried
Male
Basic
Married
Executive
40
1
9
4
4
3
2
0
1
2
2
29,616
Company Invited
Large Business
Female
Standard
Single
Senior Manager
27
3
17
3
4
3
3
0
0
1
1
23,362
Self Enquiry
Small Business
Male
Deluxe
Unmarried
Manager
26
1
8
2
3
5
7
1
1
5
0
17,042
Company Invited
Salaried
Male
Basic
Divorced
Executive
43
3
32
3
3
3
2
1
0
2
0
31,959
Company Invited
Salaried
Male
Super Deluxe
Divorced
AVP
32
1
18
4
4
5
3
1
0
2
3
25,511
Self Enquiry
Small Business
Male
Deluxe
Divorced
Manager
35
1
12
3
5
5
4
0
0
2
1
30,309
Self Enquiry
Small Business
Female
Standard
Single
Senior Manager
34
1
11
3
5
4
8
0
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4
2
21,300
Self Enquiry
Small Business
Female
Basic
Married
Executive
31
1
14
2
4
4
2
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0
4
1
16,261
Self Enquiry
Salaried
Female
Basic
Single
Executive
35
3
16
4
4
3
3
0
0
1
1
24,392
Self Enquiry
Salaried
Female
Deluxe
Married
Manager
42
3
16
3
6
3
2
0
1
5
2
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Company Invited
Salaried
Male
Super Deluxe
Married
AVP
34
1
14
2
3
5
4
0
1
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1
20,121
Self Enquiry
Salaried
Female
Deluxe
Married
Manager
34
1
9
3
4
5
2
0
1
3
1
21,385
Self Enquiry
Salaried
Female
Basic
Divorced
Executive
34
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13
2
3
4
1
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1
3
0
26,994
Self Enquiry
Salaried
Female
Standard
Unmarried
Senior Manager
39
1
36
3
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5
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2
2
24,939
Self Enquiry
Large Business
Male
Deluxe
Divorced
Manager
29
1
12
3
4
3
3
1
0
1
1
22,119
Self Enquiry
Large Business
Male
Basic
Unmarried
Executive
35
1
8
2
3
3
3
0
0
3
1
20,762
Company Invited
Small Business
Male
Deluxe
Married
Manager
26
3
10
2
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3
2
1
1
2
1
20,828
Self Enquiry
Small Business
Male
Deluxe
Single
Manager
37
1
10
3
4
3
7
0
1
2
1
21,513
Self Enquiry
Salaried
Female
Basic
Married
Executive
35
1
16
4
4
5
6
0
0
3
2
24,024
Company Invited
Salaried
Male
Deluxe
Married
Manager
40
1
9
3
4
3
2
0
1
3
1
30,847
Company Invited
Salaried
Male
Super Deluxe
Married
AVP
33
3
11
2
3
3
2
1
1
2
0
17,851
Self Enquiry
Small Business
Female
Basic
Single
Executive
38
3
15
3
4
4
1
0
0
4
0
17,899
Self Enquiry
Small Business
Male
Basic
Divorced
Executive
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