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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

## Definition
Scaling to the minimum and maximum values squeezes the values of the variables between 0 and 1. 

To implement this scaling technique, we need to subtract the minimum value from all the observations and divide the result by the value range, the difference between the maximum and minimum values. Here is the formula:

$$\bar{x} =   \frac{X - min (X)}{max(X) - min(X)}$$

# Definition
Scaling to the minimum and maximum values squeezes the values of the variables between 0 and 1. 

To implement this scaling technique, we need to subtract the minimum value from all the observations and divide the result by the value range, the difference between the maximum and minimum values. Here is the formula:

During this lesson, we will explore the Min-Max Scaling technique 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.


Min-Max scaling

During this lesson, we will explore the Min-Max Scaling technique 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

Min-Max scaling

Definition