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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
Random Sample Imputation is a method that extracts a random sample from the available observations of the variable and uses it to fill out the missing values randomly. 

> This method works with both numerical and categorical variables.

Here is an example of how this technique works:



# Definition
Random Sample Imputation is a method that extracts a random sample from the available observations of the variable and uses it to fill out the missing values randomly. 

> This method works with both numerical and categorical variables.

Here is an example of how this technique works:



The purpose of this lesson is to explore a technique for imputing missing values called random sample imputation, which is suitable for both numerical and categorical variables. We will also explore its pros and cons and learn how to implement it in a few lines of code.


Random sample imputation

The purpose of this lesson is to explore a technique for imputing missing values called random sample imputation, which is suitable for both numerical and categorical variables. We will also explore its pros and cons and learn how to implement it in a few lines of code.

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

Random sample imputation

Definition