AI & MACHINE LEARNING PROGRAM • LEVEL 15 — ANOMALY DETECTION

Detect an Anomaly with Z-score with Python

Learn detect an anomaly with z-score with python with a short, executable Python example.

IntermediateZ-scoreAnomaly

PROBLEM UNDERSTANDING

Input and expected output

Sample input

No input required

Sample output

3.75 True

COMPLETE PYTHON PROGRAM

Complete Python implementation

value, mean, std = 130, 100, 8
z = abs(value-mean)/std
print(round(z,2), z>3)

CURRENT STEP

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SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

3.75 True

0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

Initialize the sample values used to detect an anomaly with z-score.
Apply Z-score and Anomaly to compute the required result.
Display the result for detect an anomaly with z-score and compare it with the documented sample output.

This example of detect an anomaly with z-score computes the result directly from the prepared sample data. It demonstrates Z-score and Anomaly and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(n)

Auxiliary space

O(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For detect an anomaly with z-score, keep the data shape and value types consistent with Z-score.

Check this

Apply Z-score in the same order shown by the algorithm; changing the order can change the result.

Check this

Check denominators, numeric ranges and rounding before comparing the calculated value.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.