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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'})
This happened while the csv dataset builder was generating data using
hf://datasets/mazin903/tourism/processed/cleaned_tourism.csv (at revision 6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98), ['hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/processed/Xtest.csv', 'hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/processed/Xtrain.csv', 'hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/processed/cleaned_tourism.csv', 'hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/processed/ytest.csv', 'hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/processed/ytrain.csv', 'hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/tourism.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/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
Age: double
CityTier: int64
DurationOfPitch: double
NumberOfPersonVisiting: int64
NumberOfFollowups: double
PreferredPropertyStar: double
NumberOfTrips: double
Passport: int64
PitchSatisfactionScore: int64
OwnCar: int64
NumberOfChildrenVisiting: double
MonthlyIncome: double
TypeofContact: string
Occupation: string
Gender: string
ProductPitched: string
MaritalStatus: string
Designation: string
ProdTaken: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2665
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': 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 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
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'})
This happened while the csv dataset builder was generating data using
hf://datasets/mazin903/tourism/processed/cleaned_tourism.csv (at revision 6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98), ['hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/processed/Xtest.csv', 'hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/processed/Xtrain.csv', 'hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/processed/cleaned_tourism.csv', 'hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/processed/ytest.csv', 'hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/processed/ytrain.csv', 'hf://datasets/mazin903/tourism@6f7baf6bb153a30460f0d1fbfad8dbe49aba6f98/tourism.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 | CityTier int64 | DurationOfPitch float64 | NumberOfPersonVisiting int64 | NumberOfFollowups float64 | PreferredPropertyStar float64 | NumberOfTrips float64 | Passport int64 | PitchSatisfactionScore int64 | OwnCar int64 | NumberOfChildrenVisiting float64 | MonthlyIncome float64 | TypeofContact string | Occupation string | Gender string | ProductPitched string | MaritalStatus string | Designation string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
50 | 3 | 14 | 2 | 3 | 3 | 4 | 1 | 5 | 1 | 0 | 21,796 | Company Invited | Large Business | Male | Deluxe | Divorced | Manager |
35 | 1 | 15 | 3 | 2 | 3 | 4 | 0 | 3 | 0 | 2 | 23,082 | Self Enquiry | Small Business | Male | Deluxe | Married | Manager |
41 | 1 | 11 | 3 | 4 | 5 | 7 | 0 | 3 | 0 | 1 | 17,107 | Company Invited | Salaried | Male | Basic | Married | Executive |
27 | 3 | 14 | 2 | 3 | 4 | 2 | 0 | 2 | 0 | 0 | 21,214 | Self Enquiry | Small Business | Female | Deluxe | Divorced | Manager |
33 | 1 | 9 | 2 | 3 | 3 | 2 | 1 | 5 | 1 | 1 | 17,909 | Company Invited | Salaried | Female | Basic | Divorced | Executive |
35 | 1 | 9 | 3 | 5 | 5 | 3 | 0 | 1 | 1 | 1 | 23,059 | Self Enquiry | Small Business | Female | Basic | Single | Executive |
35 | 1 | 15 | 3 | 4 | 5 | 5 | 0 | 5 | 1 | 1 | 23,799 | Self Enquiry | Salaried | Female | Deluxe | Married | Manager |
46 | 1 | 9 | 4 | 5 | 3 | 3 | 0 | 3 | 1 | 1 | 20,952 | Self Enquiry | Salaried | Female | Basic | Single | Executive |
56 | 3 | 9 | 3 | 4 | 3 | 6 | 0 | 1 | 1 | 1 | 23,838 | Self Enquiry | Small Business | Male | Deluxe | Single | Manager |
31 | 2 | 8 | 3 | 4 | 5 | 4 | 0 | 3 | 0 | 2 | 21,410 | Self Enquiry | Salaried | Male | Deluxe | Married | Manager |
46 | 1 | 9 | 3 | 5 | 3 | 3 | 0 | 4 | 1 | 2 | 24,448 | Self Enquiry | Salaried | Female | Deluxe | Married | Manager |
40 | 3 | 12 | 3 | 4 | 3 | 5 | 0 | 2 | 0 | 2 | 20,764 | Self Enquiry | Large Business | Male | Deluxe | Divorced | Manager |
20 | 3 | 8 | 2 | 4 | 3 | 2 | 0 | 4 | 1 | 0 | 17,044 | Self Enquiry | Small Business | Female | Basic | Single | Executive |
43 | 1 | 8 | 3 | 1 | 3 | 2 | 0 | 1 | 1 | 2 | 17,645 | Self Enquiry | Small Business | Female | Basic | Married | Executive |
33 | 1 | 36 | 4 | 4 | 3 | 2 | 0 | 3 | 1 | 1 | 22,703 | Company Invited | Small Business | Female | Basic | Single | Executive |
31 | 3 | 9 | 4 | 4 | 4 | 3 | 0 | 3 | 1 | 1 | 21,154 | Self Enquiry | Large Business | Male | Basic | Married | Executive |
37 | 1 | 25 | 2 | 3 | 3 | 4 | 0 | 1 | 0 | 0 | 20,768 | Self Enquiry | Salaried | Male | Deluxe | Married | Manager |
59 | 1 | 8 | 3 | 4 | 3 | 4 | 1 | 5 | 1 | 0 | 28,726 | Self Enquiry | Salaried | Female | Super Deluxe | Single | AVP |
41 | 2 | 6 | 2 | 4 | 3 | 2 | 0 | 1 | 1 | 1 | 34,189 | Self Enquiry | Salaried | Male | King | Married | VP |
42 | 3 | 15 | 3 | 4 | 4 | 7 | 0 | 3 | 1 | 2 | 23,071 | Self Enquiry | Small Business | Female | Deluxe | Married | Manager |
26 | 1 | 10 | 4 | 4 | 5 | 7 | 0 | 5 | 1 | 2 | 22,709 | Self Enquiry | Small Business | Male | Basic | Divorced | Executive |
46 | 1 | 6 | 2 | 3 | 3 | 1 | 0 | 5 | 1 | 0 | 34,627 | Self Enquiry | Small Business | Male | King | Married | VP |
37 | 1 | 6 | 2 | 3 | 3 | 2 | 0 | 2 | 1 | 1 | 17,115 | Self Enquiry | Salaried | Female | Basic | Single | Executive |
37 | 3 | 18 | 4 | 5 | 3 | 6 | 0 | 1 | 1 | 2 | 25,330 | Self Enquiry | Small Business | Female | Deluxe | Married | Manager |
36 | 1 | 17 | 3 | 4 | 3 | 3 | 0 | 5 | 1 | 1 | 22,595 | Self Enquiry | Salaried | Male | Basic | Married | Executive |
35 | 1 | 31 | 2 | 3 | 3 | 2 | 1 | 3 | 0 | 1 | 25,388 | Self Enquiry | Small Business | Female | Standard | Married | Senior Manager |
30 | 1 | 22 | 4 | 6 | 3 | 2 | 1 | 5 | 1 | 1 | 20,846 | Self Enquiry | Salaried | Female | Basic | Divorced | Executive |
45 | 1 | 7 | 3 | 4 | 4 | 3 | 1 | 3 | 1 | 2 | 21,020 | Company Invited | Small Business | Male | Basic | Married | Executive |
37 | 1 | 13 | 3 | 4 | 4 | 8 | 0 | 4 | 0 | 2 | 23,619 | Self Enquiry | Small Business | Male | Deluxe | Married | Manager |
35 | 3 | 17 | 3 | 4 | 3 | 3 | 1 | 1 | 0 | 2 | 20,898 | Self Enquiry | Salaried | Female | Basic | Married | Executive |
39 | 2 | 9 | 2 | 1 | 4 | 1 | 0 | 1 | 0 | 0 | 21,389 | Self Enquiry | Salaried | Female | Deluxe | Married | Manager |
38 | 1 | 6 | 3 | 3 | 5 | 2 | 0 | 4 | 1 | 1 | 28,582 | Self Enquiry | Large Business | Female | Standard | Married | Senior Manager |
40 | 1 | 16 | 2 | 2 | 3 | 4 | 1 | 3 | 0 | 1 | 17,213 | Self Enquiry | Salaried | Female | Basic | Divorced | Executive |
40 | 1 | 16 | 3 | 4 | 5 | 3 | 0 | 4 | 1 | 1 | 23,829 | Self Enquiry | Small Business | Male | Deluxe | Married | Manager |
44 | 3 | 7 | 2 | 5 | 4 | 7 | 0 | 3 | 0 | 1 | 17,362 | Self Enquiry | Salaried | Male | Deluxe | Single | Manager |
30 | 1 | 7 | 3 | 5 | 5 | 3 | 1 | 2 | 0 | 1 | 20,997 | Self Enquiry | Salaried | Female | Basic | Divorced | Executive |
33 | 1 | 9 | 3 | 5 | 5 | 6 | 0 | 4 | 0 | 2 | 20,854 | Self Enquiry | Large Business | Male | Deluxe | Single | Manager |
28 | 1 | 24 | 3 | 4 | 4 | 2 | 1 | 4 | 1 | 1 | 21,736 | Self Enquiry | Large Business | Male | Basic | Divorced | Executive |
30 | 3 | 11 | 2 | 3 | 3 | 3 | 0 | 4 | 1 | 1 | 24,419 | Self Enquiry | Salaried | Female | Standard | Divorced | Senior Manager |
27 | 3 | 7 | 3 | 5 | 5 | 3 | 0 | 3 | 1 | 2 | 22,972 | Company Invited | Small Business | Male | Deluxe | Single | Manager |
34 | 3 | 15 | 3 | 5 | 3 | 2 | 0 | 1 | 0 | 2 | 21,020 | Company Invited | Salaried | Female | Basic | Single | Executive |
19 | 3 | 12 | 4 | 4 | 4 | 3 | 1 | 4 | 1 | 3 | 20,556 | Company Invited | Small Business | Male | Basic | Single | Executive |
29 | 1 | 24 | 4 | 4 | 5 | 3 | 0 | 1 | 0 | 2 | 23,236 | Self Enquiry | Small Business | Male | Deluxe | Married | Manager |
36 | 3 | 10 | 4 | 4 | 3 | 8 | 0 | 5 | 0 | 3 | 26,501 | Self Enquiry | Salaried | Male | Standard | Married | Senior Manager |
52 | 1 | 18 | 3 | 5 | 4 | 5 | 0 | 1 | 0 | 2 | 31,820 | Self Enquiry | Large Business | Female | Super Deluxe | Single | AVP |
42 | 3 | 6 | 1 | 3 | 3 | 2 | 0 | 3 | 1 | 0 | 19,907 | Self Enquiry | Salaried | Male | Deluxe | Married | Manager |
54 | 3 | 9 | 3 | 5 | 4 | 4 | 0 | 1 | 1 | 1 | 26,203 | Company Invited | Small Business | Female | Standard | Married | Senior Manager |
26 | 1 | 12 | 3 | 3 | 3 | 2 | 1 | 1 | 0 | 1 | 17,659 | Self Enquiry | Salaried | Female | Basic | Married | Executive |
37 | 1 | 6 | 2 | 4 | 3 | 2 | 0 | 2 | 1 | 1 | 21,474 | Self Enquiry | Salaried | Female | Deluxe | Divorced | Manager |
38 | 1 | 17 | 4 | 2 | 3 | 5 | 0 | 4 | 1 | 3 | 23,358 | Self Enquiry | Salaried | Male | Basic | Single | Executive |
36 | 1 | 32 | 4 | 5 | 4 | 5 | 0 | 3 | 1 | 2 | 29,581 | Self Enquiry | Large Business | Male | Standard | Divorced | Senior Manager |
40 | 1 | 7 | 3 | 3 | 3 | 2 | 0 | 3 | 1 | 1 | 28,291 | Self Enquiry | Small Business | Male | Standard | Married | Senior Manager |
31 | 3 | 16 | 2 | 3 | 3 | 3 | 1 | 1 | 0 | 0 | 21,583 | Self Enquiry | Small Business | Female | Deluxe | Married | Manager |
29 | 1 | 34 | 3 | 6 | 5 | 2 | 0 | 4 | 1 | 1 | 23,886 | Self Enquiry | Small Business | Female | Deluxe | Married | Manager |
31 | 3 | 11 | 3 | 3 | 3 | 2 | 0 | 1 | 0 | 2 | 20,476 | Self Enquiry | Salaried | Female | Deluxe | Married | Manager |
46 | 1 | 7 | 4 | 4 | 4 | 3 | 0 | 3 | 1 | 2 | 26,119 | Self Enquiry | Large Business | Male | Standard | Married | Senior Manager |
39 | 3 | 9 | 3 | 4 | 4 | 2 | 0 | 4 | 1 | 2 | 26,029 | Self Enquiry | Small Business | Male | Standard | Single | Senior Manager |
44 | 1 | 21 | 3 | 3 | 3 | 2 | 0 | 3 | 0 | 1 | 22,978 | Self Enquiry | Small Business | Female | Standard | Divorced | Senior Manager |
23 | 1 | 12 | 3 | 3 | 4 | 3 | 1 | 4 | 0 | 1 | 21,006 | Self Enquiry | Salaried | Male | Basic | Married | Executive |
51 | 3 | 10 | 3 | 5 | 3 | 3 | 1 | 4 | 0 | 1 | 21,361 | Self Enquiry | Small Business | Male | Basic | Divorced | Executive |
49 | 1 | 10 | 2 | 4 | 3 | 3 | 0 | 3 | 0 | 1 | 33,711 | Self Enquiry | Small Business | Male | King | Married | VP |
37 | 1 | 10 | 2 | 3 | 3 | 1 | 1 | 5 | 1 | 1 | 17,996 | Self Enquiry | Salaried | Male | Basic | Divorced | Executive |
59 | 1 | 14 | 3 | 5 | 5 | 2 | 1 | 4 | 1 | 1 | 28,686 | Self Enquiry | Small Business | Female | Standard | Divorced | Senior Manager |
33 | 1 | 8 | 3 | 3 | 3 | 5 | 0 | 3 | 0 | 2 | 17,496 | Self Enquiry | Small Business | Male | Basic | Single | Executive |
37 | 3 | 20 | 4 | 5 | 5 | 7 | 1 | 1 | 1 | 1 | 24,812 | Self Enquiry | Small Business | Male | Deluxe | Married | Manager |
34 | 3 | 14 | 2 | 4 | 4 | 2 | 0 | 4 | 0 | 1 | 22,980 | Company Invited | Salaried | Female | Deluxe | Married | Manager |
22 | 3 | 16 | 3 | 4 | 3 | 3 | 0 | 4 | 0 | 1 | 21,288 | Company Invited | Small Business | Male | Basic | Single | Executive |
40 | 1 | 14 | 2 | 4 | 4 | 3 | 0 | 1 | 1 | 1 | 28,757 | Company Invited | Small Business | Male | Standard | Married | Senior Manager |
42 | 1 | 11 | 3 | 3 | 3 | 5 | 0 | 3 | 1 | 0 | 17,093 | Company Invited | Salaried | Male | Basic | Divorced | Executive |
39 | 1 | 18 | 3 | 3 | 4 | 5 | 0 | 3 | 1 | 1 | 20,295 | Self Enquiry | Small Business | Male | Deluxe | Married | Manager |
33 | 1 | 34 | 3 | 3 | 3 | 2 | 1 | 1 | 1 | 0 | 20,207 | Self Enquiry | Salaried | Male | Deluxe | Married | Manager |
58 | 3 | 36 | 3 | 5 | 3 | 5 | 0 | 3 | 0 | 1 | 32,796 | Self Enquiry | Small Business | Male | Super Deluxe | Married | AVP |
33 | 3 | 22 | 3 | 3 | 5 | 3 | 1 | 5 | 0 | 0 | 23,564 | Self Enquiry | Salaried | Female | Standard | Single | Senior Manager |
44 | 3 | 7 | 3 | 3 | 3 | 4 | 0 | 3 | 1 | 1 | 22,978 | Company Invited | Large Business | Male | Basic | Married | Executive |
40 | 3 | 16 | 2 | 4 | 4 | 1 | 0 | 5 | 1 | 1 | 21,852 | Self Enquiry | Large Business | Female | Deluxe | Married | Manager |
35 | 1 | 7 | 3 | 4 | 3 | 3 | 0 | 3 | 1 | 1 | 21,369 | Self Enquiry | Salaried | Male | Basic | Divorced | Executive |
42 | 1 | 14 | 3 | 4 | 3 | 8 | 0 | 3 | 1 | 1 | 23,681 | Self Enquiry | Small Business | Female | Deluxe | Single | Manager |
24 | 1 | 19 | 4 | 4 | 3 | 3 | 0 | 5 | 1 | 1 | 21,325 | Self Enquiry | Salaried | Male | Basic | Single | Executive |
34 | 3 | 6 | 3 | 4 | 3 | 2 | 1 | 1 | 1 | 1 | 22,083 | Self Enquiry | Large Business | Male | Standard | Divorced | Senior Manager |
53 | 3 | 14 | 3 | 3 | 3 | 6 | 0 | 3 | 1 | 0 | 26,836 | Self Enquiry | Small Business | Male | Super Deluxe | Married | AVP |
35 | 3 | 33 | 2 | 3 | 3 | 2 | 1 | 5 | 0 | 0 | 20,813 | Self Enquiry | Salaried | Male | Deluxe | Single | Manager |
52 | 1 | 11 | 3 | 4 | 3 | 2 | 1 | 2 | 1 | 2 | 21,139 | Self Enquiry | Salaried | Male | Basic | Divorced | Executive |
36 | 1 | 7 | 2 | 5 | 3 | 3 | 0 | 4 | 1 | 1 | 21,537 | Self Enquiry | Small Business | Male | Basic | Single | Executive |
37 | 1 | 15 | 2 | 3 | 3 | 2 | 1 | 2 | 0 | 0 | 17,326 | Company Invited | Small Business | Male | Basic | Divorced | Executive |
31 | 3 | 15 | 4 | 4 | 3 | 7 | 0 | 3 | 1 | 1 | 25,942 | Self Enquiry | Salaried | Male | Standard | Married | Senior Manager |
50 | 3 | 5 | 2 | 3 | 3 | 5 | 1 | 5 | 0 | 1 | 34,331 | Self Enquiry | Small Business | Male | King | Married | VP |
56 | 3 | 9 | 3 | 4 | 3 | 6 | 0 | 2 | 0 | 2 | 23,838 | Self Enquiry | Small Business | Male | Deluxe | Single | Manager |
33 | 1 | 7 | 4 | 4 | 5 | 3 | 0 | 1 | 0 | 2 | 21,634 | Self Enquiry | Salaried | Male | Basic | Single | Executive |
27 | 1 | 18 | 3 | 4 | 5 | 3 | 1 | 3 | 1 | 1 | 23,419 | Company Invited | Small Business | Male | Deluxe | Married | Manager |
31 | 1 | 26 | 3 | 3 | 3 | 4 | 0 | 3 | 1 | 0 | 24,824 | Company Invited | Salaried | Male | Standard | Married | Senior Manager |
33 | 1 | 22 | 3 | 4 | 3 | 7 | 0 | 3 | 0 | 2 | 25,345 | Company Invited | Small Business | Female | Deluxe | Married | Manager |
41 | 3 | 6 | 2 | 1 | 5 | 2 | 0 | 3 | 1 | 1 | 23,392 | Self Enquiry | Small Business | Male | Standard | Married | Senior Manager |
35 | 1 | 8 | 3 | 3 | 5 | 2 | 1 | 1 | 1 | 1 | 17,074 | Self Enquiry | Salaried | Female | Basic | Married | Executive |
22 | 1 | 25 | 4 | 4 | 3 | 3 | 0 | 3 | 1 | 3 | 21,371 | Self Enquiry | Salaried | Female | Basic | Single | Executive |
57 | 1 | 16 | 4 | 4 | 3 | 4 | 0 | 2 | 0 | 1 | 21,620 | Company Invited | Small Business | Female | Basic | Divorced | Executive |
37 | 3 | 27 | 2 | 3 | 3 | 6 | 0 | 1 | 1 | 0 | 17,973 | Company Invited | Small Business | Female | Basic | Married | Executive |
28 | 1 | 6 | 2 | 3 | 3 | 1 | 1 | 4 | 0 | 0 | 17,154 | Company Invited | Small Business | Male | Basic | Single | Executive |
35 | 1 | 26 | 4 | 4 | 3 | 2 | 0 | 3 | 0 | 3 | 21,339 | Self Enquiry | Small Business | Male | Basic | Married | Executive |
46 | 1 | 14 | 3 | 4 | 5 | 4 | 0 | 3 | 0 | 1 | 28,402 | Self Enquiry | Salaried | Male | Standard | Married | Senior Manager |
30 | 1 | 16 | 2 | 5 | 3 | 2 | 0 | 2 | 1 | 1 | 22,661 | Self Enquiry | Salaried | Male | Basic | Single | Executive |
End of preview.
Tourism Package Prediction Dataset
This dataset contains customer profile and sales interaction records for Visit with Us. The target column is ProdTaken, where 1 means the customer purchased the offered package and 0 means the customer did not.
Files
tourism.csv: raw source datasetprocessed/cleaned_tourism.csv: cleaned modeling datasetprocessed/Xtrain.csv,processed/Xtest.csv: feature splitsprocessed/ytrain.csv,processed/ytest.csv: target splits
Intended Use
The dataset supports supervised classification for sales prioritization. It should be used to help marketing and sales teams identify customers who are more likely to purchase the Wellness Tourism Package before outreach.
Cleaning Summary
The pipeline removes generated index and customer identifier columns, standardizes categorical values, imputes missing numeric values with medians, imputes missing categorical values with the mode, removes duplicates, and creates stratified train/test splits.
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