// data exploration & preparation · application
As explored in the Theory section, Data Exploration and Preparation plays a crucial role in the performance of the learning algorithm. Python and R provide great tools to perform all such tasks.
The application of Data Exploration which includes Univariate and Bivariate analysis has been mentioned in the Application section of Basic Statistics as Univariate and Bivariate analysis use the concepts of Descriptive and Inferential Statistics only.
Data Preparation includes certain concepts termed as 'Miscellaneous Methods'. These methods include Consolidation of datasets, Outlier and Missing value treatment. The other part of Data Preparation is 'Feature Engineering' where different operations are executed on the features of the dataset to prepare them so that they can be used for creating Data Models.
A bunch of hypothetical datasets have been used for exploring the 'Miscellaneous Methods' while for certain aspects of Feature Engineering, the Boston Dataset has been used.
TM