Upload your own CSV or pick a sample dataset, choose how many synthetic samples each class should get, select a generation method (classical or generative/GAN-style), and download a file where original and synthetic rows are clearly labelled.
1 · Data input
Selecting a dataset loads it automatically.
Dataset summary
2 · How much should each class grow?
Enter the number of synthetic samples to generate per class, or use a quick preset.
3 · Which augmentation method?
4 · Result
Class distribution — before vs after
Feature space (first two features)
Grey = original samples, coloured = synthetic samples.
Preview of augmented file (first 30 rows)
The file adds three columns: source (original / synthetic), method, and parent_index (which original row(s) the sample came from).