Features and labels

After this lesson you can: Turn a real problem into a table a model can learn from.

Watch

• Feature Engineering for AI: Transforming Raw Data into Predictions - IBM Technology, 6 min. Concise IBM explainer likely frames raw data transformation into prediction-ready features for practical engineers.
• What is feature engineering | Feature Engineering Tutorial Python # 1 - codebasics, 5 min. Beginner-friendly title and short duration fit a first lesson on choosing usable input columns.
• Step By Step Process In EDA And Feature Engineering In Data Science Projects - Krish Naik, 14 min. Broader project workflow can help connect feature choices to real datasets, though less tightly focused.

Notes

A machine-learning model learns from examples arranged like a table. Each row is one example: one customer, one support ticket, one transaction, one image, or one request. The features are the input columns you give the model, such as account_age_days, number_of_failed_logins, country, or message_length. The label is the answer column you want it to learn to predict, such as churned, fraud, delivery_time_minutes, or priority_level.

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