Making predictions from data sets involves systematic preparation, modeling, validation, and deployment.

Data Preparation includes cleaning, handling outliers, encoding variables, and splitting data.

Feature Engineering creates predictive signals through ratios, interactions, and transformations.

Model Selection matches algorithms to the task—classification or regression problems.

Validation evaluates prediction quality on held-out data using appropriate metrics.