Advanced predictive modeling and machine learning techniques offer the allure of being able to extract complex relationships between predictors and the response with little effort by the analyst. This hands-off approach to modeling will only put the analyst at a disadvantage. Spending time visualizing the response, predictors, relationships among the predictors, and relationships between predictors and the response can only lead to better understandings of the data. Moreover, this knowledge may provide crucial insights as to what features may be missing in the data and may need to be included to improve a model’s predictive performance.
Data visualization is a foundational tool of feature engineering. The next chapter uses this base and begins the development of feature engineering for categorical predictors.