Is this good for hobbyists & makers?

Yes — this version of "Recommender System for a survey weighting" is framed for hobbyists & makers exploring it for fun. Play with real physics and math, no license and no setup — just open and tinker.

Do I need to install anything?

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

For Hobbyists & Makers · Recommender System

Recommender System for a survey weighting

Built for hobbyists & makers exploring it for fun. Play with real physics and math, no license and no setup — just open and tinker. Simulate a survey weighting live below — adjust the inputs and watch it respond, right in your browser.

Pagerank

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PageRankLive

the algorithm that built Google

Controls

Presets

Damping factor d

PageRank ranks nodes by importance: a page is important if important pages link to it. It is computed by imagining a random surfer who follows links with probability d and jumps randomly otherwise, then finding where they spend the most time. This eigenvector of the link matrix launched Google and now ranks everything from proteins to social influence.

▶ Run in Python

Data Inspector

Damping0.85

Top node23.6%

Methodpower iteration

Governing equation

P R (p) = \frac{1 - 0.85}{6} + 0.85 q \sum \frac{P R ( q )}{L ( q )}

Reading this result: PageRank is the stationary distribution of a random surfer who follows links with probability d and teleports to a random page with probability 1−d. At d=0.85 link structure dominates but teleport still keeps every node reachable.

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

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Kmeans

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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.

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Also for:Students Researchers Educators Engineers K-12 Students First Responders General version →

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Frequently asked questions

Is this good for hobbyists & makers?: Yes — this version of "Recommender System for a survey weighting" is framed for hobbyists & makers exploring it for fun. Play with real physics and math, no license and no setup — just open and tinker.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.