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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

## Reciprocal transformation

$$f(x) = \frac{1}{x}$$



# Reciprocal transformation

$$f(x) = \frac{1}{x}$$



Reciprocal Transformation is another technique used to transform numerical variables. We will talk about this method in this lesson while looking at the reciprocal transformation formula and the code needed to apply this encoding technique in our projects.


Reciprocal transformation

Reciprocal Transformation is another technique used to transform numerical variables. We will talk about this method in this lesson while looking at the reciprocal transformation formula and the code needed to apply this encoding technique in our 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

Reciprocal transformation

Reciprocal transformation