gh-jeremylongshore-claude-code-plugins-plus-plugins-ai-ml-automl-pipeline-builder/skills/automl-pipeline-builder/assets/example_dataset.csv

1	# Sample dataset for AutoML pipeline builder plugin
2	# This dataset is a simplified example and may not be suitable for all AutoML tasks.
3	# Replace this with your actual dataset for optimal results.
4	#
5	# Columns:
6	# feature1: Numerical feature (e.g., age, income)
7	# feature2: Categorical feature (e.g., city, product type) - encoded as strings
8	# target: Target variable (e.g., churn, conversion) - binary (0 or 1)
9	feature1,feature2,target
10	25,New York,0
11	30,Los Angeles,1
12	40,Chicago,0
13	22,Houston,0
14	35,Phoenix,1
15	48,Philadelphia,1
16	28,San Antonio,0
17	32,San Diego,1
18	45,Dallas,0
19	27,San Jose,0
20	31,Austin,1
21	38,Jacksonville,0
22	24,Fort Worth,0
23	41,Columbus,1
24	29,Charlotte,0
25	33,San Francisco,1
26	46,Indianapolis,1
27	23,Seattle,0
28	36,Denver,1
29	49,Washington,1
30	# Add more data rows here. Aim for a larger dataset (hundreds or thousands of rows) for better AutoML performance.
31	# Example:
32	# 52,Miami,0
33	# 39,Boston,1
34	# Consider adding missing values (e.g., empty strings) to test the pipeline's handling of missing data.
35	# For categorical features with many unique values, consider using techniques like one-hot encoding or target encoding.