From raw data to deployed model. 14+ models compared, hyperparameters tuned, explainability included. No PhD required.
from open_mlpipe import run
# One line — full pipeline
ctx = run("dataset.csv", target="price")
# Access results
print(f"Best model: {ctx.best_model_name}")
print(f"Test R²: {ctx.metrics['test_r2']:.4f}")From data loading to model deployment — all automated.
Column hygiene, quality assessment, distributions, statistical tests, VIF analysis — all automatic.
Missingness flags, interaction features, log transforms, datetime extraction — no manual work.
Ridge, XGBoost, LightGBM, RandomForest, Stacking, Voting — auto-selected and compared.
Hyperparameter optimization with 18 model-specific search spaces — finds the best parameters.
Global + local feature importance, dependence plots, waterfall explanations — understand your model.
Full inference pipeline saved — raw data → prediction with no manual preprocessing.
open-mlpipe runs a complete ML pipeline automatically.
Auto-detect task, profile data
Duplicates, outliers, missing
Interactions, log transforms
14+ models head-to-head
Optuna optimization
Production model ready
From linear to ensemble — all tested and ranked.
Install, run, done.
pip install open-mlpipe
from open_mlpipe import run
ctx = run("data.csv", target="price")
print(ctx.best_model_name)
print(ctx.metrics["test_r2"])
Start building in minutes, not weeks.