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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
Rare label encoding consists of infrequent grouping categories that appear only in a tiny portion of the observations into one new category called ```Rare```, ```Others```, or any different string indicated by the user.
Since rare labels can cause a few issues like overfitting and generalization, this technique helps avoid those problems.

We need to specify the minimum percentage of observations a category should show to be preserved and the minimum number of unique categories a variable should have to be considered rare.

See the figure below for more illustration: 

# Definition
Rare label encoding consists of infrequent grouping categories that appear only in a tiny portion of the observations into one new category called ```Rare```, ```Others```, or any different string indicated by the user.
Since rare labels can cause a few issues like overfitting and generalization, this technique helps avoid those problems.

We need to specify the minimum percentage of observations a category should show to be preserved and the minimum number of unique categories a variable should have to be considered rare.

See the figure below for more illustration: 

The purpose of this lesson is to cover the rare label encoding technique for encoding categorical variables while going through its advantages and disadvantages and learning how to use this encoder in your code.


Rare label encoding

The purpose of this lesson is to cover the rare label encoding technique for encoding categorical variables while going through its advantages and disadvantages and learning how to use this encoder in your 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

Rare label encoding

Definition