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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

Count or frequency encoding replaces the categories by the count of the observations that show that category in the dataset.

See the illustration below:

In some cases where the frequency is related somewhat to the target variable, this method helps the model understand and assign the weight in direct and inverse proportion.

## Advantages of the count or frequency encoding
- Easy implementation.
- It does not expand the feature space.
- It can perform well with tree-based algorithms.

## Disadvantages of the count or frequency encoding
- Inadequate for linear models.
- It is unable to handle new categories in the test set automatically.
- Risk losing valuable information if two different categories have the same occurrence frequency since they are replaced with the same number.

Run this Python code below to implement this method:

# Definition

Count or frequency encoding replaces the categories by the count of the observations that show that category in the dataset.

See the illustration below:

In some cases where the frequency is related somewhat to the target variable, this method helps the model understand and assign the weight in direct and inverse proportion.

# Advantages of the count or frequency encoding
- Easy implementation.
- It does not expand the feature space.
- It can perform well with tree-based algorithms.

# Disadvantages of the count or frequency encoding
- Inadequate for linear models.
- It is unable to handle new categories in the test set automatically.
- Risk losing valuable information if two different categories have the same occurrence frequency since they are replaced with the same number.

Run this Python code below to implement this method:

Count or frequency encoding is another approach used to transform labels into numbers. We will talk about this method in this lesson while exploring the benefits and drawbacks of using this encoder, an illustrative example, and the code needed to apply this encoding technique in your projects.


Count or frequency encoding

Count or frequency encoding is another approach used to transform labels into numbers. We will talk about this method in this lesson while exploring the benefits and drawbacks of using this encoder, an illustrative example, and the code needed to apply this encoding technique in your projects.

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

Count or frequency encoding

Definition