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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
Ordered label categories replace categories with integers from 1 to n, where n represents the cardinality. However, assigning these integers depends on the target mean information of each category.

Here is a detailed illustration:



# Definition
Ordered label categories replace categories with integers from 1 to n, where n represents the cardinality. However, assigning these integers depends on the target mean information of each category.

Here is a detailed illustration:



During this lesson, we will talk about an encoding technique used for categorical variables called ordered label encoding, its advantages and limitations, an example that illustrates the results after using ordered label encoding, and some code lines to help you implement this method.


Ordered label encoding

During this lesson, we will talk about an encoding technique used for categorical variables called ordered label encoding, its advantages and limitations, an example that illustrates the results after using ordered label encoding, and some code lines to help you implement this method.

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

Ordered label encoding

Definition