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Numerical Variables

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In this course, you will learn how to apply basic and more advanced feature engineering to tabular data with python, during this course,  we will see a range of different techniques and methods to handle many common cases within the data set, so in result, you will create great features so that your machine learning models can predict good results.

Feature Engineering with Python

## Definition
Maximum absolute scaling scales the data to its maximum value; that is, it divides every observation by the maximum value of the variable. 

The formula for maximum absolute scaling is given as:


# Definition
Maximum absolute scaling scales the data to its maximum value; that is, it divides every observation by the maximum value of the variable. 

The formula for maximum absolute scaling is given as:


This lesson will cover a feature scaling technique known as maximum absolute scaling. We will also see an illustrative example, its formula, some points to consider when using this scaling technique, and how to implement the maximum absolute scaling in a few lines of code. 


Maximum absolute scaling

This lesson will cover a feature scaling technique known as maximum absolute scaling. We will also see an illustrative example, its formula, some points to consider when using this scaling technique, and how to implement the maximum absolute scaling in a few lines of code.

Introduction

Variable Types

Common Concerns in Datasets

Handling & Imputing Missing Values

Encoding Categorical Variables

Transforming Variables

Variable Discretization

Handling Outliers

Feature Scaling

Engineering Geospatial Data

Handling Date-Time and Mixed Variables

Resampling Imbalanced Data

Advanced Feature Engineering Techniques

Conclusion

Maximum absolute scaling

Definition