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

This happened while the csv dataset builder was generating data using

hf://datasets/moushmim/tourism-package-pred-data/tourism.csv (at revision 5ac0141d639140653d4adf402389d2412a4e753f), [/tmp/hf-datasets-cache/medium/datasets/56679553825333-config-parquet-and-info-moushmim-tourism-package--068f52b6/hub/datasets--moushmim--tourism-package-pred-data/snapshots/5ac0141d639140653d4adf402389d2412a4e753f/Xtest.csv (origin=hf://datasets/moushmim/tourism-package-pred-data@5ac0141d639140653d4adf402389d2412a4e753f/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/56679553825333-config-parquet-and-info-moushmim-tourism-package--068f52b6/hub/datasets--moushmim--tourism-package-pred-data/snapshots/5ac0141d639140653d4adf402389d2412a4e753f/Xtrain.csv (origin=hf://datasets/moushmim/tourism-package-pred-data@5ac0141d639140653d4adf402389d2412a4e753f/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/56679553825333-config-parquet-and-info-moushmim-tourism-package--068f52b6/hub/datasets--moushmim--tourism-package-pred-data/snapshots/5ac0141d639140653d4adf402389d2412a4e753f/tourism.csv (origin=hf://datasets/moushmim/tourism-package-pred-data@5ac0141d639140653d4adf402389d2412a4e753f/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/56679553825333-config-parquet-and-info-moushmim-tourism-package--068f52b6/hub/datasets--moushmim--tourism-package-pred-data/snapshots/5ac0141d639140653d4adf402389d2412a4e753f/ytest.csv (origin=hf://datasets/moushmim/tourism-package-pred-data@5ac0141d639140653d4adf402389d2412a4e753f/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/56679553825333-config-parquet-and-info-moushmim-tourism-package--068f52b6/hub/datasets--moushmim--tourism-package-pred-data/snapshots/5ac0141d639140653d4adf402389d2412a4e753f/ytrain.csv (origin=hf://datasets/moushmim/tourism-package-pred-data@5ac0141d639140653d4adf402389d2412a4e753f/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 674, 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('string'), 'DurationOfPitch': Value('float64'), 'Occupation': Value('string'), 'Gender': Value('string'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'ProductPitched': Value('string'), 'PreferredPropertyStar': Value('string'), 'MaritalStatus': Value('string'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'PitchSatisfactionScore': Value('int64'), 'OwnCar': Value('int64'), 'NumberOfChildrenVisiting': Value('float64'), 'Designation': Value('string'), 'MonthlyIncome': 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 3 new columns ({'CustomerID', 'ProdTaken', 'Unnamed: 0'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/moushmim/tourism-package-pred-data/tourism.csv (at revision 5ac0141d639140653d4adf402389d2412a4e753f), [/tmp/hf-datasets-cache/medium/datasets/56679553825333-config-parquet-and-info-moushmim-tourism-package--068f52b6/hub/datasets--moushmim--tourism-package-pred-data/snapshots/5ac0141d639140653d4adf402389d2412a4e753f/Xtest.csv (origin=hf://datasets/moushmim/tourism-package-pred-data@5ac0141d639140653d4adf402389d2412a4e753f/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/56679553825333-config-parquet-and-info-moushmim-tourism-package--068f52b6/hub/datasets--moushmim--tourism-package-pred-data/snapshots/5ac0141d639140653d4adf402389d2412a4e753f/Xtrain.csv (origin=hf://datasets/moushmim/tourism-package-pred-data@5ac0141d639140653d4adf402389d2412a4e753f/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/56679553825333-config-parquet-and-info-moushmim-tourism-package--068f52b6/hub/datasets--moushmim--tourism-package-pred-data/snapshots/5ac0141d639140653d4adf402389d2412a4e753f/tourism.csv (origin=hf://datasets/moushmim/tourism-package-pred-data@5ac0141d639140653d4adf402389d2412a4e753f/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/56679553825333-config-parquet-and-info-moushmim-tourism-package--068f52b6/hub/datasets--moushmim--tourism-package-pred-data/snapshots/5ac0141d639140653d4adf402389d2412a4e753f/ytest.csv (origin=hf://datasets/moushmim/tourism-package-pred-data@5ac0141d639140653d4adf402389d2412a4e753f/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/56679553825333-config-parquet-and-info-moushmim-tourism-package--068f52b6/hub/datasets--moushmim--tourism-package-pred-data/snapshots/5ac0141d639140653d4adf402389d2412a4e753f/ytrain.csv (origin=hf://datasets/moushmim/tourism-package-pred-data@5ac0141d639140653d4adf402389d2412a4e753f/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
string
DurationOfPitch
float64
Occupation
string
Gender
string
NumberOfPersonVisiting
int64
NumberOfFollowups
float64
ProductPitched
string
PreferredPropertyStar
string
MaritalStatus
string
NumberOfTrips
float64
Passport
int64
PitchSatisfactionScore
int64
OwnCar
int64
NumberOfChildrenVisiting
float64
Designation
string
MonthlyIncome
float64
44
Self Enquiry
Tier 1
8
Salaried
Female
3
1
Standard
3 Star
Married
2
1
4
1
0
Senior Manager
22,879
35
Self Enquiry
Tier 3
20
Small Business
Male
3
4
Standard
3 Star
Married
3
0
1
1
2
Senior Manager
27,306
47
Self Enquiry
Tier 3
7
Small Business
Female
4
4
Standard
5 Star
Married
3
0
2
1
2
Senior Manager
29,131
32
Self Enquiry
Tier 1
6
Salaried
Male
3
3
Deluxe
4 Star
Married
2
0
3
1
0
Manager
21,220
59
Self Enquiry
Tier 1
9
Large Business
Male
3
4
Basic
3 Star
Single
6
0
2
1
2
Executive
21,157
44
Self Enquiry
Tier 3
11
Small Business
Male
2
3
King
4 Star
Divorced
1
0
5
1
1
VP
33,213
32
Self Enquiry
Tier 1
35
Salaried
Female
2
4
Basic
4 Star
Single
2
0
3
1
0
Executive
17,837
27
Self Enquiry
Tier 3
7
Salaried
Male
3
4
Deluxe
3 Star
Married
3
0
5
0
2
Manager
23,974
38
Company Invited
Tier 3
8
Salaried
Male
2
4
Deluxe
3 Star
Divorced
4
0
5
1
1
Manager
20,249
32
Self Enquiry
Tier 1
12
Large Business
Male
3
4
Basic
3 Star
Married
2
1
4
1
1
Executive
23,499
40
Self Enquiry
Tier 1
30
Large Business
Male
3
3
Deluxe
3 Star
Married
2
0
3
1
1
Manager
18,319
38
Self Enquiry
Tier 1
20
Small Business
Male
3
4
Deluxe
3 Star
Married
3
0
1
0
1
Manager
22,963
35
Company Invited
Tier 3
6
Small Business
Female
3
3
Standard
3 Star
Unmarried
2
0
5
1
0
Senior Manager
23,789
35
Self Enquiry
Tier 1
8
Salaried
Female
3
3
Basic
5 Star
Married
2
1
1
1
1
Executive
17,074
34
Self Enquiry
Tier 1
17
Small Business
Male
3
6
Basic
3 Star
Married
2
0
5
0
1
Executive
22,086
33
Self Enquiry
Tier 1
36
Salaried
Female
3
5
Basic
4 Star
Unmarried
3
0
3
1
1
Executive
21,515
51
Self Enquiry
Tier 1
15
Salaried
Male
3
3
Basic
3 Star
Divorced
4
0
3
1
0
Executive
17,075
29
Company Invited
Tier 3
30
Large Business
Male
2
1
Basic
5 Star
Single
2
0
3
1
1
Executive
16,091
34
Company Invited
Tier 3
25
Small Business
Male
3
2
Deluxe
3 Star
Single
1
1
2
1
2
Manager
20,304
38
Self Enquiry
Tier 1
14
Small Business
Male
2
4
Standard
3 Star
Single
6
0
2
0
1
Senior Manager
32,342
46
Self Enquiry
Tier 1
6
Small Business
Male
3
3
Standard
5 Star
Married
1
0
2
0
0
Senior Manager
24,396
54
Self Enquiry
Tier 2
25
Small Business
Male
2
3
Standard
4 Star
Divorced
3
0
3
1
0
Senior Manager
25,725
56
Self Enquiry
Tier 1
15
Small Business
Male
2
3
Super Deluxe
3 Star
Married
1
0
4
0
0
AVP
26,103
30
Company Invited
Tier 1
10
Large Business
Male
2
3
Basic
3 Star
Single
19
1
4
1
1
Executive
17,285
26
Self Enquiry
Tier 1
6
Small Business
Male
3
3
Basic
5 Star
Single
1
0
5
1
2
Executive
17,867
33
Self Enquiry
Tier 1
13
Small Business
Male
2
3
Standard
3 Star
Married
1
0
4
1
0
Senior Manager
26,691
24
Self Enquiry
Tier 1
23
Salaried
Male
3
4
Basic
4 Star
Married
2
0
3
1
1
Executive
17,127
30
Self Enquiry
Tier 1
36
Salaried
Male
4
6
Deluxe
3 Star
Married
2
0
5
1
3
Manager
25,062
33
Company Invited
Tier 3
8
Small Business
Female
3
3
Deluxe
4 Star
Single
1
0
1
0
0
Manager
20,147
53
Company Invited
Tier 3
8
Small Business
Female
2
4
Standard
4 Star
Married
3
0
1
1
0
Senior Manager
22,525
29
Company Invited
Tier 3
14
Salaried
Male
3
4
Deluxe
5 Star
Unmarried
2
0
3
1
2
Manager
23,576
39
Self Enquiry
Tier 1
15
Small Business
Male
2
3
Deluxe
5 Star
Married
2
0
4
1
0
Manager
20,151
46
Self Enquiry
Tier 3
9
Salaried
Male
4
4
Deluxe
4 Star
Married
2
0
5
1
3
Manager
23,483
35
Self Enquiry
Tier 1
14
Salaried
Female
3
4
Standard
4 Star
Single
2
0
3
1
1
Senior Manager
30,672
35
Company Invited
Tier 3
9
Small Business
Female
4
4
Basic
3 Star
Married
8
0
5
0
1
Executive
20,909
33
Company Invited
Tier 1
7
Salaried
Female
4
5
Basic
4 Star
Married
8
0
3
0
3
Executive
21,010
29
Company Invited
Tier 1
16
Salaried
Female
2
4
Basic
3 Star
Unmarried
2
0
4
1
0
Executive
21,623
41
Company Invited
Tier 3
16
Salaried
Male
2
3
Deluxe
3 Star
Single
1
0
1
0
1
Manager
21,230
43
Self Enquiry
Tier 1
36
Small Business
Male
3
6
Deluxe
3 Star
Unmarried
6
0
3
1
1
Manager
22,950
35
Company Invited
Tier 3
13
Small Business
Female
3
6
Basic
3 Star
Married
2
0
4
0
2
Executive
21,029
41
Self Enquiry
Tier 3
12
Salaried
Female
3
3
Standard
3 Star
Single
4
1
1
0
0
Senior Manager
28,591
33
Self Enquiry
Tier 1
6
Salaried
Female
2
4
Deluxe
3 Star
Unmarried
1
0
4
0
0
Manager
21,949
40
Company Invited
Tier 1
15
Small Business
Female
2
3
Standard
3 Star
Unmarried
1
0
4
0
0
Senior Manager
28,499
26
Company Invited
Tier 1
9
Large Business
Male
3
3
Basic
5 Star
Single
1
0
3
0
1
Executive
18,102
41
Self Enquiry
Tier 1
25
Salaried
Male
2
3
Deluxe
5 Star
Married
3
0
1
0
0
Manager
18,072
37
Company Invited
Tier 1
17
Salaried
Male
2
3
Standard
3 Star
Married
2
1
3
0
1
Senior Manager
27,185
31
Self Enquiry
Tier 3
13
Salaried
Male
2
4
Basic
3 Star
Married
4
0
4
1
1
Executive
17,329
45
Self Enquiry
Tier 3
8
Salaried
Male
3
6
Deluxe
4 Star
Single
8
0
3
0
2
Manager
21,040
33
Company Invited
Tier 1
9
Salaried
Male
3
3
Basic
5 Star
Single
2
1
5
1
2
Executive
18,348
33
Self Enquiry
Tier 1
9
Small Business
Female
4
4
Basic
4 Star
Divorced
3
0
4
0
1
Executive
21,048
33
Self Enquiry
Tier 1
14
Salaried
Male
3
3
Deluxe
3 Star
Unmarried
3
1
3
0
2
Manager
21,388
30
Self Enquiry
Tier 3
18
Large Business
Female
2
3
Deluxe
3 Star
Unmarried
1
0
2
1
0
Manager
21,577
42
Company Invited
Tier 1
25
Small Business
Male
2
2
Basic
3 Star
Married
7
1
3
1
1
Executive
17,759
46
Self Enquiry
Tier 1
8
Salaried
Male
2
3
Super Deluxe
3 Star
Married
7
0
5
1
0
AVP
32,861
51
Self Enquiry
Tier 1
16
Salaried
Male
4
4
Basic
3 Star
Married
6
0
5
1
3
Executive
21,058
30
Self Enquiry
Tier 1
8
Salaried
Female
2
5
Deluxe
3 Star
Single
3
0
1
1
0
Manager
21,091
37
Company Invited
Tier 1
25
Salaried
Male
3
3
Basic
3 Star
Divorced
6
0
5
0
1
Executive
22,366
28
Company Invited
Tier 2
6
Salaried
Male
2
3
Basic
3 Star
Married
2
0
4
0
1
Executive
17,706
42
Self Enquiry
Tier 1
12
Small Business
Male
2
3
Standard
5 Star
Married
1
0
3
1
0
Senior Manager
28,348
44
Self Enquiry
Tier 1
10
Small Business
Male
2
3
Deluxe
4 Star
Single
1
0
2
1
0
Manager
20,933
39
Company Invited
Tier 1
9
Small Business
Female
3
5
Basic
4 Star
Single
3
0
1
1
1
Executive
21,118
42
Self Enquiry
Tier 1
23
Salaried
Female
2
2
Deluxe
5 Star
Unmarried
4
1
2
0
0
Manager
21,545
39
Company Invited
Tier 1
28
Small Business
Female
2
3
Standard
5 Star
Unmarried
2
1
5
1
1
Senior Manager
25,880
28
Company Invited
Tier 1
6
Salaried
Female
2
5
Deluxe
3 Star
Divorced
1
0
3
1
0
Manager
21,674
43
Self Enquiry
Tier 1
20
Salaried
Male
3
3
Super Deluxe
5 Star
Married
7
0
5
1
1
AVP
32,159
45
Self Enquiry
Tier 1
22
Small Business
Female
4
4
Standard
3 Star
Divorced
3
0
3
0
2
Senior Manager
26,656
53
Self Enquiry
Tier 1
13
Large Business
Male
4
4
Deluxe
5 Star
Married
5
1
4
1
2
Manager
24,255
42
Self Enquiry
Tier 1
16
Salaried
Male
4
4
Basic
5 Star
Married
4
0
1
0
1
Executive
20,916
36
Self Enquiry
Tier 1
33
Small Business
Male
3
3
Deluxe
3 Star
Divorced
7
0
3
1
0
Manager
20,237
22
Self Enquiry
Tier 1
7
Large Business
Female
4
5
Basic
4 Star
Single
3
1
5
0
3
Executive
20,748
37
Self Enquiry
Tier 1
12
Salaried
Male
4
4
Deluxe
4 Star
Unmarried
2
0
2
0
3
Manager
24,592
30
Company Invited
Tier 3
20
Large Business
Female
3
4
Deluxe
4 Star
Unmarried
7
0
3
0
2
Manager
24,443
36
Company Invited
Tier 1
18
Small Business
Male
4
5
Standard
5 Star
Married
4
1
5
1
3
Senior Manager
28,562
40
Self Enquiry
Tier 1
10
Small Business
Female
2
3
King
3 Star
Divorced
2
0
5
0
1
VP
34,033
51
Company Invited
Tier 1
14
Salaried
Male
2
5
Standard
3 Star
Unmarried
3
0
2
0
1
Senior Manager
25,650
39
Self Enquiry
Tier 3
7
Salaried
Male
3
5
Basic
5 Star
Unmarried
6
0
3
0
2
Executive
21,536
43
Self Enquiry
Tier 1
18
Salaried
Male
2
4
Super Deluxe
4 Star
Married
2
0
3
0
1
AVP
29,336
35
Self Enquiry
Tier 1
10
Salaried
Male
3
3
Basic
3 Star
Married
2
0
4
0
0
Executive
16,951
40
Company Invited
Tier 1
9
Large Business
Female
4
4
Standard
3 Star
Single
2
0
2
1
2
Senior Manager
29,616
27
Self Enquiry
Tier 3
17
Small Business
Male
3
4
Deluxe
3 Star
Unmarried
3
0
1
0
1
Manager
23,362
26
Company Invited
Tier 1
8
Salaried
Male
2
3
Basic
5 Star
Divorced
7
1
5
1
0
Executive
17,042
43
Company Invited
Tier 3
32
Salaried
Male
3
3
Super Deluxe
3 Star
Divorced
2
1
2
0
0
AVP
31,959
32
Self Enquiry
Tier 1
18
Small Business
Male
4
4
Deluxe
5 Star
Divorced
3
1
2
0
3
Manager
25,511
35
Self Enquiry
Tier 1
12
Small Business
Female
3
5
Standard
5 Star
Single
4
0
2
0
1
Senior Manager
30,309
34
Self Enquiry
Tier 1
11
Small Business
Female
3
5
Basic
4 Star
Married
8
0
4
0
2
Executive
21,300
31
Self Enquiry
Tier 1
14
Salaried
Female
2
4
Basic
4 Star
Single
2
0
4
0
1
Executive
16,261
35
Self Enquiry
Tier 3
16
Salaried
Female
4
4
Deluxe
3 Star
Married
3
0
1
0
1
Manager
24,392
42
Company Invited
Tier 3
16
Salaried
Male
3
6
Super Deluxe
3 Star
Married
2
0
5
1
2
AVP
24,829
34
Self Enquiry
Tier 1
14
Salaried
Female
2
3
Deluxe
5 Star
Married
4
0
5
1
1
Manager
20,121
34
Self Enquiry
Tier 1
9
Salaried
Female
3
4
Basic
5 Star
Divorced
2
0
3
1
1
Executive
21,385
34
Self Enquiry
Tier 1
13
Salaried
Female
2
3
Standard
4 Star
Unmarried
1
0
3
1
0
Senior Manager
26,994
39
Self Enquiry
Tier 1
36
Large Business
Male
3
4
Deluxe
3 Star
Divorced
5
0
2
0
2
Manager
24,939
29
Self Enquiry
Tier 1
12
Large Business
Male
3
4
Basic
3 Star
Unmarried
3
1
1
0
1
Executive
22,119
35
Company Invited
Tier 1
8
Small Business
Male
2
3
Deluxe
3 Star
Married
3
0
3
0
1
Manager
20,762
26
Self Enquiry
Tier 3
10
Small Business
Male
2
4
Deluxe
3 Star
Single
2
1
2
1
1
Manager
20,828
37
Self Enquiry
Tier 1
10
Salaried
Female
3
4
Basic
3 Star
Married
7
0
2
1
1
Executive
21,513
35
Company Invited
Tier 1
16
Salaried
Male
4
4
Deluxe
5 Star
Married
6
0
3
0
2
Manager
24,024
40
Company Invited
Tier 1
9
Salaried
Male
3
4
Super Deluxe
3 Star
Married
2
0
3
1
1
AVP
30,847
33
Self Enquiry
Tier 3
11
Small Business
Female
2
3
Basic
3 Star
Single
2
1
2
1
0
Executive
17,851
38
Self Enquiry
Tier 3
15
Small Business
Male
3
4
Basic
4 Star
Divorced
1
0
4
0
0
Executive
17,899
End of preview.