Understand the dataset before you touch model training
LLMs usually do not read or analyze tabular datasets deeply enough to recommend proper models or preprocessing. This page exists to expose faults first, then convert them into an auto-composed pipeline.
Workflow on this page
Upload a CSV, inspect dataset faults, choose the target, and review the preprocessing graph that the app composes on your behalf.
Start here
Upload the dataset the model will actually see
The point is not just to upload a CSV. The point is to reveal null pockets, suspicious text columns, imbalance, and target candidates before any model recommendation is trusted.
Preprocessing faults
Missing values, text-heavy columns, and high-cardinality fields are captured before training.
Pipeline composition
Those findings become imputation, scaling, encoding, and evaluation choices.
Drag & drop your dataset
Or browse files