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For K-12 Students · Anomaly Detection

Anomaly Detection for a customer churn

Built for k-12 students learning it in middle or high school. Watch the idea come alive with plain-language steps and everyday examples — perfect for projects and homework. Simulate a customer churn live below — adjust the inputs and watch it respond, right in your browser.

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Distributions

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Probability DistributionsLive

Controls

Presets

▶ Run in Python

Data Inspector

Distributionnormal
Mean0.000
Variance1.000
Std dev1.000

Governing equation

Reading this result: The mean μ only slides the bell sideways while σ sets its width — variance is σ² = 1.00, so doubling σ quadruples the spread.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

k-Means ClusteringLive

Controls

Presets

Lloyd's algorithm: assign each point to its nearest centroid, then move each centroid to the mean of its members. Repeat until stable. Try setting k different from the true cluster count.

▶ Run in Python

Data Inspector

Iteration0
Inertia0.00e+0
k4

Governing equation

Reading this result: k matches the 4 true clusters, so each centroid can settle onto one real group and inertia falls to a clean minimum.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Frequently asked questions

Is this good for k-12 students?
Yes — this version of "Anomaly Detection for a customer churn" is framed for k-12 students learning it in middle or high school. Watch the idea come alive with plain-language steps and everyday examples — perfect for projects and homework.
Do I need to install anything?
No. It runs in any modern browser, free, with no account required.