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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

## Equal-Frequency discretization
This discretization is performed by equal frequency binning, i.e., the thresholds of all bins are selected so that all bins contain the same number of numerical values.

The discretization by frequency operator creates bins so that the number of unique values in all bins are (almost) equal.

There are a few points to consider when working with equal frequency discretization, like:
- In this method, the interval boundaries correspond to the quantiles.
- This method helps with improving the value spread.
- Equal frequency handles outliers.
- This arbitrary binning can disrupt the relationship with the target.
- This method is convenient when combined with categorical encodings.

See the result of this discretization below:

# Equal-Frequency discretization
This discretization is performed by equal frequency binning, i.e., the thresholds of all bins are selected so that all bins contain the same number of numerical values.

The discretization by frequency operator creates bins so that the number of unique values in all bins are (almost) equal.

There are a few points to consider when working with equal frequency discretization, like:
- In this method, the interval boundaries correspond to the quantiles.
- This method helps with improving the value spread.
- Equal frequency handles outliers.
- This arbitrary binning can disrupt the relationship with the target.
- This method is convenient when combined with categorical encodings.

See the result of this discretization below:

During this lesson, we will explore a discretization technique called equal-frequency discretization while going through a few essential points to keep in mind when working with this method and a code to help you implement it in your projects.


Equal-frequency discretization

During this lesson, we will explore a discretization technique called equal-frequency discretization while going through a few essential points to keep in mind when working with this method and a code to help you implement it in your projects.

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

Equal-frequency discretization

Equal-Frequency discretization