# example_data.csv
# This CSV file provides sample data to demonstrate the functionality of the data_preprocessing_pipeline plugin.
#
# Column Descriptions:
#   - ID: Unique identifier for each record.
#   - Feature1: Numerical feature with some missing values.
#   - Feature2: Categorical feature with multiple categories and potential typos.
#   - Feature3: Date feature in string format.
#   - Target: Binary target variable (0 or 1).
#
# Placeholders:
#   - [MISSING_VALUE]: Represents a missing value to be handled by the pipeline.
#   - [TYPO_CATEGORY]: Represents a typo in a categorical value.
#
# Instructions:
#   - Feel free to modify this data to test different preprocessing scenarios.
#   - Ensure the data adheres to the expected format for each column.
#   - Use the `/preprocess` command to trigger the preprocessing pipeline on this data.

ID,Feature1,Feature2,Feature3,Target
1,10.5,CategoryA,2023-01-15,1
2,12.0,CategoryB,2023-02-20,0
3,[MISSING_VALUE],CategoryC,2023-03-25,1
4,15.2,CategoryA,2023-04-01,0
5,9.8,CateogryB,[MISSING_VALUE],1
6,11.3,CategoryC,2023-05-10,0
7,13.7,CategoryA,2023-06-15,1
8,[MISSING_VALUE],CategoryB,2023-07-20,0
9,16.1,CategoryC,2023-08-25,1
10,10.0,CategoryA,2023-09-01,0
11,12.5,[TYPO_CATEGORY],2023-10-10,1
12,14.9,CategoryB,2023-11-15,0
13,11.8,CategoryC,2023-12-20,1
14,13.2,CategoryA,2024-01-25,0
15,9.5,CategoryB,2024-02-01,1