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Numerical Variables

Default Job

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

There are various techniques for handling outliers that are used according to the problem statement and aim we are approaching; here are the techniques we are going to see:

- Trimming
- Censoring
- Imputing
- Transformation


So, let us get started with each technique.


The purpose of this lesson is to explore a few different techniques and methods that help us engineer outliers after detecting them.


Handling outliers

The purpose of this lesson is to explore a few different techniques and methods that help us engineer outliers after detecting them.

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

Handling outliers