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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
Target encoding is the process of replacing a categorical value with the mean of the target variable for that category. 
First, we calculate the mean of the target variable for each category and replace the category variable with the mean value.

Here is a detailed illustration of target encoding:

# Definition
Target encoding is the process of replacing a categorical value with the mean of the target variable for that category. 
First, we calculate the mean of the target variable for each category and replace the category variable with the mean value.

Here is a detailed illustration of target encoding:

Mean encoding (also known as target encoding) is a frequently used technique for encoding categorical variables. The objective of this lesson is to learn this method, its advantages and drawbacks, an illustration of the encoding process, and the code needed to use this encoder in practice.


Mean or target encoding

Mean encoding (also known as target encoding) is a frequently used technique for encoding categorical variables. The objective of this lesson is to learn this method, its advantages and drawbacks, an illustration of the encoding process, and the code needed to use this encoder in practice.

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

Mean or target encoding

Definition