DATA SCIENCE, MADE HANDS-ON

Make sense of
your data.

Explore datasets, learn machine learning by experimenting, and build notebooks you can take anywhere.

Bring in a dataset ↗

START WITH YOUR DATA

Dataset workspace

CSV · XLSX / UP TO 25 MB

↑

Drop a dataset here

CSV or Excel workbook. Your file stays on this computer.

Choose file

FROM DATA TO DOCUMENTED WORK

Notebook builder

REPRODUCIBLE · EDITABLE · YOURS

A useful starting point, generated for you.

Choose algorithms and targets, test them against your uploaded dataset, then download a notebook with those settings already filled in.

Supervised algorithms need a target column. K-means is unsupervised, so it does not use one.

Include selected EDA charts

Histograms Boxplots Scatterplot Correlation heatmap Category bars Category frequenciesChart columnsOptional. Leave all unselected to use every compatible column. Use Ctrl-click (or Cmd-click on Mac) to select several.

Remove duplicate rows in notebook Include current Playground experiment

Upload a dataset to choose algorithms and target columns.

LEARN BY EXPERIMENTING

ML Playground

REAL SCIKIT-LEARN EXPERIMENTS

AlgorithmData source

K-means groups points by distance to a centroid. Cluster IDs are arbitrary labels.

THE TOOLKIT

Algorithms to explore

AVAILABLE IN THE PYTHON LIBRARY

Make sense of
your data.

Dataset workspace

Drop a dataset here

Clean and prepare

Explore with a chart

Notebook builder

A useful starting point, generated for you.

ML Playground

Algorithms to explore

Make sense ofyour data.

Dataset workspace

Drop a dataset here

Clean and prepare

Explore with a chart

Notebook builder

A useful starting point, generated for you.

ML Playground

Algorithms to explore

Make sense of
your data.