Framework for Data Preparation Techniques in Machine Learning

Last Updated on July 17, 2020

There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project.

In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner.

Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike.

The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.

In this tutorial, you will discover a framework that provides a structured approach to both thinking about and grouping data preparation techniques for predictive modeling with structured data.

After completing this tutorial, you will know:

• The challenge and overwhelm of framing data preparation as yet an
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