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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
Integer encoding or label encoding converts the categories to digits from 1 to n (or 0 to n-1), where n is the cardinality, which means the number of the variable’s unique categories, randomly assigning these numbers.

See the following figure:

# Definition
Integer encoding or label encoding converts the categories to digits from 1 to n (or 0 to n-1), where n is the cardinality, which means the number of the variable’s unique categories, randomly assigning these numbers.

See the following figure:

In this lesson, we will learn about the integer encoding technique for encoding categorical variables; we will see an illustrative example of how this method works, its advantages and limitations, and finally, a sample code showing how to use this encoder in practice.


Integer or label encoding

In this lesson, we will learn about the integer encoding technique for encoding categorical variables; we will see an illustrative example of how this method works, its advantages and limitations, and finally, a sample code showing how 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

Integer or label encoding

Definition