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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

Equal width binning is probably the most popular way of doing discretization. This means that after the binning, all bins have equal width or represent an equal range of the original variable values, no matter how many cases are in each bin.

The following formula calculates the width of intervals:

$$width = \frac{max values - minvalues}{N}$$

Equal-width discretization is the simplest form of discretization. We will learn about this technique and its formula while exploring an example of how it works, alongside some points to keep in mind when using this technique.


Equal-width discretization

Equal-width discretization is the simplest form of discretization. We will learn about this technique and its formula while exploring an example of how it works, alongside some points to keep in mind when using this technique.

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

Equal-width discretization