35 lines
1.3 KiB
CSV
35 lines
1.3 KiB
CSV
# example_data.csv
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# This CSV file provides sample data to demonstrate the functionality of the data_preprocessing_pipeline plugin.
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#
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# Column Descriptions:
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# - ID: Unique identifier for each record.
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# - Feature1: Numerical feature with some missing values.
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# - Feature2: Categorical feature with multiple categories and potential typos.
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# - Feature3: Date feature in string format.
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# - Target: Binary target variable (0 or 1).
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#
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# Placeholders:
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# - [MISSING_VALUE]: Represents a missing value to be handled by the pipeline.
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# - [TYPO_CATEGORY]: Represents a typo in a categorical value.
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#
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# Instructions:
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# - Feel free to modify this data to test different preprocessing scenarios.
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# - Ensure the data adheres to the expected format for each column.
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# - Use the `/preprocess` command to trigger the preprocessing pipeline on this data.
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ID,Feature1,Feature2,Feature3,Target
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1,10.5,CategoryA,2023-01-15,1
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2,12.0,CategoryB,2023-02-20,0
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3,[MISSING_VALUE],CategoryC,2023-03-25,1
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4,15.2,CategoryA,2023-04-01,0
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5,9.8,CateogryB,[MISSING_VALUE],1
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6,11.3,CategoryC,2023-05-10,0
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7,13.7,CategoryA,2023-06-15,1
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8,[MISSING_VALUE],CategoryB,2023-07-20,0
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9,16.1,CategoryC,2023-08-25,1
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10,10.0,CategoryA,2023-09-01,0
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11,12.5,[TYPO_CATEGORY],2023-10-10,1
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12,14.9,CategoryB,2023-11-15,0
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13,11.8,CategoryC,2023-12-20,1
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14,13.2,CategoryA,2024-01-25,0
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15,9.5,CategoryB,2024-02-01,1 |