Dockerfile.tar.gz

Numerical Variables

Default Job

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
Binary encoding is a technique that uses binary code. First, we start by converting each value to a sequence of binary digits. Then associate a column from the dataset with each binary digit. If there are n distinct categories, then the number of features after performing the binary encoding would be equal to ```ln(n) / ln(2)```.

Here is an example:

# Definition
Binary encoding is a technique that uses binary code. First, we start by converting each value to a sequence of binary digits. Then associate a column from the dataset with each binary digit. If there are n distinct categories, then the number of features after performing the binary encoding would be equal to ```ln(n) / ln(2)```.

Here is an example:

This lesson will mainly cover everything we need to know about Binary Encoding, including the pros and cons of using this technique to encode our variables, along with an illustrative example and code snippet.

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

Binary encoding

Definition