Thinking-data-science-a-data-science-practitioners-guide

10 1 Data Science Process
Fig. 1.5 Seaborn sample outputs
Fig. 1.6 Options in features
engineering
Features Engineering
Selecting appropriate features while keeping the features count low is very important
in training the model efficiently. We call this features engineering. The features
engineering workflow has two paths, features selection and dimensionality reduction.
This is shown in Fig. 1.6.
Feature selection means selecting and excluding certain features without changing
them. Dimensionality reduction reduces the features count by reducing the
features dimensions using many techniques developed by researchers over years.
With domain knowledge, you can eliminate the unwanted features by manual
inspection and simply dropping those columns from the database. You may remove
features that have missing values and low variance or are highly correlated. You may