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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
It is also called Mode imputation; it replaces all occurrences of missing values (NA) within a variable by the mode, which refers to the most frequent value or most frequent category.

> Practically, this method is used with categorical variables, but it works with numerical and categorical variables.

This method can be applied when:
- Data is missing completely at random
- No more than 5% of the variable contains missing data.
For example:

# Definition
It is also called Mode imputation; it replaces all occurrences of missing values (NA) within a variable by the mode, which refers to the most frequent value or most frequent category.

> Practically, this method is used with categorical variables, but it works with numerical and categorical variables.

This method can be applied when:
- Data is missing completely at random
- No more than 5% of the variable contains missing data.
For example:

This lesson will mainly cover everything you need to know about frequent category imputation, including the pros and cons of using this technique, along with an 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

Frequent category imputation

Definition