Upload a dataset
Drop a CSV, Excel, JSON, or Parquet file. PrePro Auto reads it, detects the encoding and delimiter automatically, and creates a job you can run the whole pipeline on.
Profile the data
Profiling infers each column's true type, missing rate, and quality, and detects ordered categories. This must run before any cleaning stage.
View dataset
A live view of your data at the current version. It updates after every change anywhere — cleaning stages, transforms, undo/redo. Toggle between the original (raw) data and the current state.
Clean the data
Each stage proposes decisions. Approve, override, or skip each one, then execute the stage to apply them and write a new snapshot. Stages run in order.
Transform
Full manual control over the dataset: create, drop, rename, merge, cast, filter, map, and apply math to columns and rows. Every transform is a new version — undo any time.
Tip: select multiple columns to run the same operation on all of them at once.
Results & export
See exactly what preprocessing changed — the quality score, the column types before vs after, and a data sample — then download your deliverables.
Before & after
Comparison of the raw upload against the cleaned result.
Download
Take your results away.
Clean data
The processed dataset as CSV or Parquet.
Audit report
A PDF listing every transformation and who decided it.
Pipeline code
A standalone Python script reproducing the cleaning.
Train a model
Train a model on top of your cleaning recipe. Preprocessing is re-fit inside every cross-validation fold from the raw data, so the scores are leakage-free and match what you'll see at serving time — not the inflated numbers you get from fitting on all rows first.
Setup
What you're predicting and why. Everything else can stay on defaults.
Custom model advanced
Bring your own estimator — any scikit-learn-compatible model, whether ML, statistical, or a neural net. It runs through the same leakage-safe pipeline and lands on the leaderboard with the rest.
sklearn.svm.SVR, sklearn.linear_model.ElasticNet,
sklearn.neighbors.KNeighborsRegressor, sklearn.ensemble.ExtraTreesClassifier,
xgboost.XGBRegressor. Statistical models live under sklearn.linear_model.*
(Ridge, Lasso, ElasticNet, LogisticRegression, GLMs).