Feature engineering is the process of transforming raw data into meaningful inputs (features) for analytics models and machine learning systems. Features capture patterns, behaviors, and signals that models use to make predictions.

Examples include:

• Days since last purchase

• Average session duration

• Purchase frequency

• Churn risk indicators

In BI, feature engineering supports predictive analytics and AI-driven insights.

Good feature engineering requires domain knowledge, data quality, and iteration. Poor features lead to weak models, regardless of algorithm strength.

Feature engineering bridges raw data and intelligent decision-making, making it a cornerstone of advanced analytics.