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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

The Yeo-Johnson transformation is very similar to the Box-Cox but does not require the input variables to be strictly positive, which means it can be used with negative values.

We can implement this transformation by running this code:

This lesson will cover another technique we can use to transform variables in our datasets called Yeo-Johnson transformation, its formula, and a sample code to use this transformation in practice.

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

Yeo-Johnson transformation

Yeo-Johnson transformation