Forecasting in data science involves using historical data and analytical models to predict future outcomes.

It combines statistical techniques, machine learning algorithms, and domain knowledge to identify patterns and trends that inform decision-making.

Effective forecasting enables organizations to optimize resources, anticipate market demands, and reduce risks by providing actionable insights.

Techniques such as time series analysis, regression models, and neural networks are commonly employed to improve the accuracy and reliability of forecasts across various industries.