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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

## Custom discretization
Generally, when engineering variables in a custom environment (i.e., for a particular business use case), we divide the variable so that the intervals are meaningful to the business. We call it also domain knowledge discretization.

Take for example, the discretization of the Age variable; we can divide it this way: [0–10]
as kids, [10–25] as teenagers, etc. We can adjust these ranges and the associated labels according to the study and the environment in other scenarios.

## Demo
Run the following code to see the result of the Age variable discretization:

# Custom discretization
Generally, when engineering variables in a custom environment (i.e., for a particular business use case), we divide the variable so that the intervals are meaningful to the business. We call it also domain knowledge discretization.

Take for example, the discretization of the Age variable; we can divide it this way: [0–10]
as kids, [10–25] as teenagers, etc. We can adjust these ranges and the associated labels according to the study and the environment in other scenarios.

# Demo
Run the following code to see the result of the Age variable discretization:

If the discretization approaches we saw previously do not reply to your need and study, you can use custom discretization. This lesson will see an excellent example of such cases and how to achieve that with a simple python 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

Custom discretization

Custom discretization