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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

In the discretization methods' code snippets that we discussed earlier, we used the parameter ```encode = 'ordinal'```, which means integer encoding. In other words, it assigns integers (1,2,3, etc.) to the resulting interval/bins.

In a machine learning model, we can use these numbers in two main ways:
- We can use these numbers immediately if we want to use the intervals as numbers.
- We can consider these numbers as categories; As a result, we can apply one of the categorical encoding techniques that we have seen previously.

> When considering these bin numbers as categories, it would be beneficial to encode them using an encoding technique that creates a monotonic relationship with the target.

Here is an example that encodes each resulting interval to a categorical variable:

This lesson aims to explain what we can do with integer encoding resulting from the discretization techniques that we have seen previously and how to use them to our advantage according to our needs.


How to use it now?

This lesson aims to explain what we can do with integer encoding resulting from the discretization techniques that we have seen previously and how to use them to our advantage according to our needs.

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

How to use it now?