AI & MACHINE LEARNING PROGRAM • LEVEL 05 — DATA PREPARATION
Apply Min-max Scaling with Python
Learn apply min-max scaling with python with a short, executable Python example.
PROBLEM UNDERSTANDING
Input and expected output
No input required
[0.0, 0.5, 1.0]
COMPLETE PYTHON PROGRAM
Complete Python implementation
values = [10, 20, 30] low, high = min(values), max(values) print([(value - low) / (high - low) for value in values])
CURRENT STEP
Select Start to walk through the important lines.
SELECTED LINE
No line selected
EXPECTED OUTPUT FOR THE SAMPLE
[0.0, 0.5, 1.0]
PROGRAM EXPLANATION
Algorithm and explanation
- Initialize the sample values used to apply min-max scaling.
- Process the data step by step using Min-max scaling.
- Display the result for apply min-max scaling and compare it with the documented sample output.
This example of apply min-max scaling processes the sample values in a controlled iteration. It demonstrates Min-max scaling 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 apply min-max scaling, keep the data shape and value types consistent with Min-max scaling.
Keep every dependent statement inside the correct indented Python block.
Check denominators, numeric ranges and rounding before comparing the calculated value.