AI & MACHINE LEARNING PROGRAM • LEVEL 01 — AI & ML FOUNDATIONS
Separate Features and Target with Python
Learn separate features and target with python with a short, executable Python example.
PROBLEM UNDERSTANDING
Input and expected output
No input required
[[2, 4], [3, 6]] [6, 9]
COMPLETE PYTHON PROGRAM
Complete Python implementation
rows = [[2, 4, 6], [3, 6, 9]] features = [row[:-1] for row in rows] target = [row[-1] for row in rows] print(features, target)
CURRENT STEP
Select Start to walk through the important lines.
SELECTED LINE
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EXPECTED OUTPUT FOR THE SAMPLE
[[2, 4], [3, 6]] [6, 9]
PROGRAM EXPLANATION
Algorithm and explanation
- Initialize the sample values used to separate features and target.
- Process the data step by step using Features and Target.
- Display the result for separate features and target and compare it with the documented sample output.
This example of separate features and target processes the sample values in a controlled iteration. It demonstrates Features and Target and prints a deterministic result that can be checked against the sample output.
EFFICIENCY
Time and space complexity
O(n)
O(n)
DEBUGGING CHECKLIST
Common mistakes
For separate features and target, keep the data shape and value types consistent with Features.
Keep every dependent statement inside the correct indented Python block.
Verify the final Features and Target result against the sample before trying new data.