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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

## What is feature magnitude
Feature values in a given dataset can vary widely. Imagine a dataset with a column for the age of cars (years), and another one for distance traveled (Km) — the range of these two variables is so different. This is called feature magnitude.

Another clear example of a housing dataset with below values: the number of rooms in a house and its price. 

As we can see, the ranges of the first variable are pretty different from the second one.

# What is feature magnitude
Feature values in a given dataset can vary widely. Imagine a dataset with a column for the age of cars (years), and another one for distance traveled (Km) — the range of these two variables is so different. This is called feature magnitude.

Another clear example of a housing dataset with below values: the number of rooms in a house and its price. 

As we can see, the ranges of the first variable are pretty different from the second one.

This lesson will look at feature magnitude, why it is crucial in machine learning, and some models highly affected by feature magnitude.

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

Feature magnitude

What is feature magnitude