Video narration
If you have a table and basic Python, making a model can sound like you need to invent its sorting rule. Imagine fruit labeled by the answer: apple or orange. Color and weight are clues; the label is what you want the sorter to learn. You don't need to invent the rule. scikit-learn provides ready-made models you train; choose one that fits your task and data. Before training, set some labeled fruit aside for a test. Use the rest to teach the model how features connect to labels. In scikit-learn, fit means learning from those examples. Then use predict-ask for labels for fruit it hasn't seen-and compare its guesses with the answers you held back. That checks this set, not every future basket. You don't build the sorter from scratch. Give it labeled fruit, train it, and check its guesses on unseen fruit.