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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
This is an effortless technique where we consider all the outliers as missing values and use the different missing value imputation techniques we talked about previously to fill the outliers with values.

As a reminder, we can use the following techniques. Some of them work with numerical variables since you can have outliers in numerical features and also categorical variables:

- Mean or median imputation
- Arbitrary value imputation
- End of tail imputation
- Frequent category imputation
- Add a missing category
- Complete case analysis
- Add a missing indicator
- Random sample imputation


# Definition
This is an effortless technique where we consider all the outliers as missing values and use the different missing value imputation techniques we talked about previously to fill the outliers with values.

As a reminder, we can use the following techniques. Some of them work with numerical variables since you can have outliers in numerical features and also categorical variables:

- Mean or median imputation
- Arbitrary value imputation
- End of tail imputation
- Frequent category imputation
- Add a missing category
- Complete case analysis
- Add a missing indicator
- Random sample imputation


The imputing technique can also be considered an approach to handling outliers in a given dataset. How does it work? We will talk about that in this lesson.


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

Imputing

Definition