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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
Categorical variables represent types of data that may be divided into groups. Examples of categorical variables are race (Arab, Asian, …) and gender (male, female).

There are two types of categorical variables:
- Ordinal variables
- Nominal variables

Here is an example of categorical variable:

# Definition
Categorical variables represent types of data that may be divided into groups. Examples of categorical variables are race (Arab, Asian, …) and gender (male, female).

There are two types of categorical variables:
- Ordinal variables
- Nominal variables

Here is an example of categorical variable:

This lesson will cover what we call categorical variables, their two different types: ordinal and nominal categorical variables, with some examples of both.


Categorical variables

This lesson will cover what we call categorical variables, their two different types: ordinal and nominal categorical variables, with some examples of both.

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

Categorical variables

Definition