The Deep Feature Synthesis (DFS) algorithm
automates feature engineering and is capable of extracting
and applying complicated featuresto a variety of processes.
Due to the novelty of DFS as a method for feature
engineering, critical ways for dealing with missing values
and unwanted data in a dataset have yet to be established.
This paper discusses the usage of mean, median, and mode
imputation to preprocess data before analyzing it.However,
it is only limited to displaying the differences between nonimputed and imputed datasets. This strategy enables users
to obtain more precise results by eliminating biased
estimations. This study demonstrates that there is a distinct
difference between the two datasets. This paper is
concluded by proving that imputing datasets will cause
distinctness in the results compared to the results of the
datasets with missing and unwanted values.
Keywords : Deep Feature Synthesis, Auto Feature Engineering, Imputation