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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
When scaling to vector unit length, we transform the components of a feature vector so that the transformed vector has a length of 1, or in other words, a norm of 1. It consists of dividing each feature’s vector by the vector's distance. Here is the formula for scaling to vector unit norm:

# Definition
When scaling to vector unit length, we transform the components of a feature vector so that the transformed vector has a length of 1, or in other words, a norm of 1. It consists of dividing each feature’s vector by the vector's distance. Here is the formula for scaling to vector unit norm:

For the distance measure, we have two options:

- The Manhattan distance (L1 norm) with the formula:

$$L1(X) = |x_{1}|+|x_{2}|+|x_{3}|+..+|x_{n}|$$

In this lesson, we will learn another scaling method called scaling to vector unit norm, and we will also see its formula and a sample code to scale your features using this technique.

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

Scaling to vector unit norm

Definition