Deduplicate rows, impute missing values with 8+ numeric strategies (Mean, Median, KNN, MICE) and 4 categorical strategies, remove outliers via IQR / Z-score / Winsorize, fix skewness, and apply Yeo-Johnson power transform. Download a clean CSV or hand off directly to AutoML.

RF, XGBoost, LightGBM, and CatBoost compete via 5-fold cross-validation. The winner is selected automatically by F1 (classification) or MAE (regression). Optional Optuna tuning and SHAP explanation run on the winner.

log1p, sqrt, Yeo-Johnson, percentile rank, outlier flag, and missing flag per numeric column. Plus binning, polynomial pairs, interaction terms, date extraction, and cyclical encoding (sin / cos). All transforms are fit on training data only — no leakage.

Four methods — Variance Threshold, Correlation Filter (drop >0.9 correlated), RFE (Random Forest), and SelectKBest (Mutual Info) — automatically prune irrelevant or redundant columns before training. Configurable top-K cutoff.

TPE sampler runs up to 30 trials on the AutoML winner to find optimal hyperparameters. Tuning is optional and runs after model selection — not before — so it never inflates the competition score.

Every prediction comes with a SHAP bar chart showing which features drove the result and by how much. FE-derived columns are grouped back to their originals so you see source-feature influence, not transform noise.

Simple voting (VotingClassifier / VotingRegressor) or stacking with a meta-learner on top of the AutoML winners. Reduces variance and improves generalization over any single model.