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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
A missing indicator consists of conserving the observations having missing data with adding a new binary variable (1/0) that indicates whether the value for that observation is missing.

This method is only an indicator, and you need to impute the missing value using a different imputation technique that assumes the data is missing at random, like:
- Mean, median, mode imputation.
- Random sample imputation.

> This method works with both categorical and numerical variables.

Take a look at this example using the missing indicator method, alongside a random sample imputation:




# Definition
A missing indicator consists of conserving the observations having missing data with adding a new binary variable (1/0) that indicates whether the value for that observation is missing.

This method is only an indicator, and you need to impute the missing value using a different imputation technique that assumes the data is missing at random, like:
- Mean, median, mode imputation.
- Random sample imputation.

> This method works with both categorical and numerical variables.

Take a look at this example using the missing indicator method, alongside a random sample imputation:




This lesson will talk about a data imputation technique used for categorical and numerical variables called missing indicators. We will also cover the assumptions of the method, the advantages, and disadvantages of its use, and an example and a sample code to apply missing indicator imputation.


Missing indicator

This lesson will talk about a data imputation technique used for categorical and numerical variables called missing indicators. We will also cover the assumptions of the method, the advantages, and disadvantages of its use, and an example and a sample code to apply missing indicator imputation.

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

Missing indicator

Definition