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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

## Exponential or power transformation

$$f(x) = x^2$$
$$g(x) = x^3$$
$$h(x) = x^m$$
$$k(x) = exp(x)$$


# Exponential or power transformation

$$f(x) = x^2$$
$$g(x) = x^3$$
$$h(x) = x^m$$
$$k(x) = exp(x)$$


Exponential transformation is also known as power transformation, is another transformation that can be used on variables. We will talk about it in this lesson, its multiple formulas, and a code sample to use in practice.


Exponential or power transformation

Exponential transformation is also known as power transformation, is another transformation that can be used on variables. We will talk about it in this lesson, its multiple formulas, and a code sample to use in practice.

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

Exponential or power transformation

Exponential or power transformation