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skills/automl-pipeline-builder/assets/example_dataset.csv

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