In the context of data analysis and machine learning, inference and prediction are related concepts but have distinct meanings.

Inference refers to the process of drawing conclusions or making generalizations based on available evidence or data. It involves using existing information to make informed judgments or reach logical deductions about a population or a system. In statistical inference, for example, you might infer properties of a larger group based on a sample of that group.

Prediction, on the other hand, involves estimating or forecasting future outcomes or events based on patterns or relationships observed in historical data. It is the process of using available information to make educated guesses about what might happen in the future. Prediction models are typically built using historical data as input and are used to generate predictions for new, unseen data.

To summarize, inference focuses on understanding the existing data and drawing conclusions about the population or system, while prediction focuses on making forecasts or estimates about future events or outcomes based on patterns observed in historical data.