Data Exploration and Preparation play a critical role in the success of the learning algorithm, as we have seen in the Theory section. All of the mentioned tasks can be accomplished with Python and R.

Data Exploration, which includes Univariate or Bivariate analysis, has been discussed in the Application section. Univariate/Bivariate analysis uses the concepts of Descriptive Statistics only.

Data preparation includes some concepts called ‘Miscellaneous methods.’ These methods include Outliers, Consolidation of data, and Missing value treatment. Data Preparation also includes ‘Feature Engineering,’ where different operations are performed on data features to prepare them for use in creating Data Models.

For exploring the ‘Miscellaneous Methods,’ a bunch of hypothetical datasets was used. However, the Boston Dataset was used for certain aspects of Feature Engineering. Python has been used to execute the code.