Feature Engineering 1 - Using Correlation Analysis to Describe Behavior over Time
Feature Engineering is an important tool for (supervised) machine learning. Model accuracy and interpretability benefits from variables that describe the behavior of the analysis subjects. In many cases these feature are derived from transactional data which are recorded, e.g. over time. In some cases, simple descriptive measures like the mean or the sum provide a very good picture of the analysis subjects. In many cases it is important to dig deeper to adequately describe the behavior. Analytic methods help to calculate KPIs that measure the trend over time per customer, the accordance with predefined patter or the correlation of individual customer with the average customer.
This webinar focuses on “correlation analysis between individual customers and the average customer” and shows the rationale, the coding and the interpretation of the results.
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