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

## Engineering mixed variable types
Mixed variables are a type of variable that can contain both numbers and labels. As an example, a smartphone model can have both letters and numbers mixed in each observation (for example, **MCJ19085**)

The simplest way to handle this feature is to extract the categorical part in one variable and the numerical part in a different variable, resulting in two variables. Then we can engineer the newly created variable using the feature engineering techniques just like other categorical or numerical features.

Mixed variables usually come in two different formats:

### 1. Labels and numbers in different observations

In this case, some observations have numeric values, and others have strings or labels. Here is an example of this format before and after engineering it:

# Engineering mixed variable types
Mixed variables are a type of variable that can contain both numbers and labels. As an example, a smartphone model can have both letters and numbers mixed in each observation (for example, **MCJ19085**)

The simplest way to handle this feature is to extract the categorical part in one variable and the numerical part in a different variable, resulting in two variables. Then we can engineer the newly created variable using the feature engineering techniques just like other categorical or numerical features.

Mixed variables usually come in two different formats:

## 1. Labels and numbers in different observations

In this case, some observations have numeric values, and others have strings or labels. Here is an example of this format before and after engineering it:

In this lesson, we will talk about mixed variables, the different formats of a mixed variable, a few examples of each format, and how them, alongside some simple code snippets.


Engineering mixed variables types

In this lesson, we will talk about mixed variables, the different formats of a mixed variable, a few examples of each format, and how them, alongside some simple code snippets.

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

Engineering mixed variables types

Engineering mixed variable types