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Quick Start

30-Second Start

from open_mlpipe import run

# One line — full pipeline
ctx = run("dataset.csv", target="price")

That's it. The pipeline will:

  1. Load your data
  2. Run EDA
  3. Clean the data
  4. Engineer features
  5. Compare 14+ models
  6. Tune the best model
  7. Save a production-ready pipeline

What You Get Back

# Best model name
print(ctx.best_model_name)  # "lightgbm"

# All metrics
print(ctx.metrics)
# {'test_r2': 0.847, 'test_mae': 0.292, 'test_rmse': 0.447, ...}

# Saved model path
print(ctx.reports["model_path"])  # "artifacts/model_v1.joblib"

Load and Use the Saved Model

import joblib
import pandas as pd

# Load the saved model
model = joblib.load("artifacts/model_v1.joblib")

# Prepare new data
new_data = pd.DataFrame({
    "feature1": [1.0, 2.0, 3.0],
    "feature2": ["a", "b", "c"],
})

# Predict
predictions = model.predict(new_data)

CLI Alternative

open-mlpipe run --data dataset.csv --target price

Next Steps