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30. The Machine Learning Workflow Step by Step

30.1 The Five Steps: Data, Features, Train, Evaluate, Predict

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Why. Beginners jump to “fit a model”. Pros walk five steps every time. If you skip a step, Pune ops will not trust the output.

What. The beginner workflow we use in this course:

  1. Data — collect and clean rows.
  2. Features — choose input columns (X).
  3. Train — learn patterns on the training part.
  4. Evaluate — check quality on held-out data.
  5. Predict — score new rows in production or a notebook.

How.

Say the five steps aloud

  1. Write them on paper in order.
  2. Circle which step you are on before every Jupyter cell.
  3. Do not evaluate on the same rows you trained on (Chapter 31 explains why).
  4. Prediction comes last — after you trust evaluate.
steps = ["data", "features", "train", "evaluate", "predict"]
for i, s in enumerate(steps, start=1):
 print(f"{i}. {s}")

Ravindra Bagale's Tip

Workflow wall paper la chipkun theva. Model change zala tari steps same. Bilkul visru naka.