Is this good for educators?

Yes — this version of "Clustering Workbench for a time-series forecast" is framed for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs.

Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Educators · Clustering Workbench

Clustering Workbench for a time-series forecast

Built for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs. Simulate a time-series forecast live below — adjust the inputs and watch it respond, right in your browser.

Kmeans

open full solver →

k-Means ClusteringLive

Lloyd's algorithm · live

Controls

Speed1×

Presets

k (centroids)

True clusters

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

Governing equation

J = k = 1 \sum 4 x \in C_{k} \sum ∥ x - μ_{k} ∥^{2}, μ_{k} = \frac{1}{∣ C _{k} ∣} x \in C_{k} \sum x

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.

↓

PCA

open full solver →

Principal Component AnalysisLive

finding the axes of variation

Controls

Presets

Correlation

Spread (x)

PCA finds the directions along which data varies most. The eigenvectors of the covariance matrix are the principal components (cyan = most variance, green = least), and their eigenvalues are the variances along each. Projecting onto the top components is the basis of dimensionality reduction.

▶ Run in Python

Data Inspector

Variance PC10.00

Variance PC20.00

PC1 explains0%

PC1 angle0°

Governing equation

C v = λ v, \frac{λ _{1}}{λ _{1} + λ _{2}} = \frac{0.00}{0.00} = 0.00

Reading this result: PC1 explains 0% of the variance here; the stronger the correlation between features, the more PCA concentrates information on that first component.

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

or unlock everything with Pro →

Also for:Students Researchers Engineers Hobbyists & Makers K-12 Students First Responders General version →

More with Clustering Workbench

Clustering Workbench for an A/B test Clustering Workbench for a survey sample Clustering Workbench for a poll margin Clustering Workbench for a quality-control chart Clustering Workbench for a clinical trial Clustering Workbench for a customer churn Clustering Workbench for a click-through rate Clustering Workbench for an exam-score curve

Frequently asked questions

Is this good for educators?: Yes — this version of "Clustering Workbench for a time-series forecast" is framed for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.