gh-jeremylongshore-claude-code-plugins-plus-plugins-ai-ml-data-preprocessing-pipeline/skills/data-preprocessing-pipeline/assets/example_data.csv

1	# example_data.csv
2	# This CSV file provides sample data to demonstrate the functionality of the data_preprocessing_pipeline plugin.
3	#
4	# Column Descriptions:
5	# - ID: Unique identifier for each record.
6	# - Feature1: Numerical feature with some missing values.
7	# - Feature2: Categorical feature with multiple categories and potential typos.
8	# - Feature3: Date feature in string format.
9	# - Target: Binary target variable (0 or 1).
10	#
11	# Placeholders:
12	# - [MISSING_VALUE]: Represents a missing value to be handled by the pipeline.
13	# - [TYPO_CATEGORY]: Represents a typo in a categorical value.
14	#
15	# Instructions:
16	# - Feel free to modify this data to test different preprocessing scenarios.
17	# - Ensure the data adheres to the expected format for each column.
18	# - Use the `/preprocess` command to trigger the preprocessing pipeline on this data.
19	ID	Feature1	Feature2	Feature3	Target
20	1	10.5	CategoryA	2023-01-15	1
21	2	12.0	CategoryB	2023-02-20	0
22	3	[MISSING_VALUE]	CategoryC	2023-03-25	1
23	4	15.2	CategoryA	2023-04-01	0
24	5	9.8	CateogryB	[MISSING_VALUE]	1
25	6	11.3	CategoryC	2023-05-10	0
26	7	13.7	CategoryA	2023-06-15	1
27	8	[MISSING_VALUE]	CategoryB	2023-07-20	0
28	9	16.1	CategoryC	2023-08-25	1
29	10	10.0	CategoryA	2023-09-01	0
30	11	12.5	[TYPO_CATEGORY]	2023-10-10	1
31	12	14.9	CategoryB	2023-11-15	0
32	13	11.8	CategoryC	2023-12-20	1
33	14	13.2	CategoryA	2024-01-25	0
34	15	9.5	CategoryB	2024-02-01	1