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Visual sklearn for CSVs — copilot builds the DAG, you keep the code.
50 jobs / month, 100k rows, no credit card.
Sample already loaded. Click Run on the canvas. Upload of your own file stays locked until you sign up.
Iris (sample)
150 × 4
Standard Scaler
mean / std
K-Means
k = 3
Cluster plot
Run → chart
Iris in one click — no signup to run the sample
Train Random Forest on Iris, see hold-out metrics, then export labels or probabilities.
Run K-Means on a sample table. Inspect the plot, then swap in HDBSCAN or GMM.
Copilot draws the DAG. You keep the nodes, the metrics, and the Python.
Researchers bounce from black-box SaaS. Hold-out is labeled separately from training scores. You can leave with Python.
Optimistic training scores are marked. The number you ship is hold-out.
Export P(class), not only Yes/No. Download the ROC/PR curve.
from sklearn.cluster import KMeans pipe.fit(X) labels = pipe.predict(X)
Export a sklearn script or notebook. The DAG is the artifact, not a chat log.
Your file is used to run your job. Sign up when you want to upload it, save the graph, or download exports.
Dedicated pages for each family — also under Modules in the top bar, including from the canvas.
Unsupervised Learning
Group similar data points together without labels. Discover hidden patterns, segment customers, detect anomalies, and explore the natural structure in your data.
Open clusteringSupervised Learning
Train models to predict categorical labels from features. Build classifiers with confusion matrices, ROC curves, feature importance, and calibration diagnostics.
Open classificationContinuous Prediction
Predict continuous numeric values from features. From linear baselines to gradient boosting — with R², residual plots, and feature importance diagnostics.
Open regressionData Preparation
Clean, transform, and encode datasets through a sequential Transformation Chain. Impute missing values, scale features, encode categoricals, and filter columns — then export to Pipeline or Python.
Open preprocessingTransformation & DR
Reduce high-dimensional data for visualization, noise removal, and feature extraction. From classic PCA to modern manifold techniques like UMAP and t-SNE.
Open dim. reductionCanvas builder
Chain preprocessing, models, and plots. Ask copilot in English or drag nodes yourself.
Open canvasFree account. No card. Export labels, probabilities, and ROC. 50 jobs / 100k rows a month.
Signup unlocks upload, save, and download — samples stay free to run