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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
The weight of evidence (WOE) tells the predictive power of an independent variable in relation to the dependent variable. This technique is usually used to encode categorical variables for classification.

The mathematical formula to calculate the WOE is :
$$ WOE = ln (p (1) / p (0)) $$  

Where ```p(1)``` is the probability of the target being 1, and ```p(0)``` is the probability of the target being 0.

> You can change the sign of the output (result) if it is negative.

- ```WOE > 0``` means the probability of the target being ```0``` is more significant.
- ```WOE < 0``` means the probability of the target being ```1``` is more significant.

As a result, the WOE encoding creates an excellent visual representation of the variable. By looking at the value of the WOE encoded variable, we can tell which category favors the target being 0 or 1.

Here is an example that explains the principle of WOE: 

# Definition
The weight of evidence (WOE) tells the predictive power of an independent variable in relation to the dependent variable. This technique is usually used to encode categorical variables for classification.

The mathematical formula to calculate the WOE is :
$$ WOE = ln (p (1) / p (0)) $$  

Where ```p(1)``` is the probability of the target being 1, and ```p(0)``` is the probability of the target being 0.

> You can change the sign of the output (result) if it is negative.

- ```WOE > 0``` means the probability of the target being ```0``` is more significant.
- ```WOE < 0``` means the probability of the target being ```1``` is more significant.

As a result, the WOE encoding creates an excellent visual representation of the variable. By looking at the value of the WOE encoded variable, we can tell which category favors the target being 0 or 1.

Here is an example that explains the principle of WOE: 

This lesson will talk about a categorical encoding technique called the weight of evidence encoding. We will also cover the advantages and disadvantages of its use and an example and a sample code to apply the weight of the evidence encoder.


Weight of evidence encoding

This lesson will talk about a categorical encoding technique called the weight of evidence encoding. We will also cover the advantages and disadvantages of its use and an example and a sample code to apply the weight of the evidence encoder.

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

Weight of evidence encoding

Definition