Feature engineering steps

Feature engineering refers to a process of selecting and transforming variables when creating a predictive model using machine learning or statistical modeling (such as deep learning, decision trees, or regression). The process involves a combination of data analysis, applying rules of thumb, and judgement.

Feature engineering involves applying business knowledge, mathematics and statistics to transform data into a form that machine learning models can use . Algorithms depend on data to drive machine learning algorithms. A user who understands historical data can detect the pattern and then develop a hypothesis.

The feature engineering process is: