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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

## Variable discretization
Discretization (or Binning) is how we can transform continuous variables into discrete variables. We do this by creating a set of contiguous intervals (or bins) that go across the range of our desired variable.


# Variable discretization
Discretization (or Binning) is how we can transform continuous variables into discrete variables. We do this by creating a set of contiguous intervals (or bins) that go across the range of our desired variable.


During this lesson, we will cover the concept of variable discretization. We will get to know some of the supervised and unsupervised approaches to discretize the continuous variables in our datasets.


Discretization approaches

During this lesson, we will cover the concept of variable discretization. We will get to know some of the supervised and unsupervised approaches to discretize the continuous variables in our datasets.

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

Discretization approaches

Variable discretization