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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
Standardization is the process of centering the variable at zero and standardizing the variance to 1. 

We subtract the mean from each observation and then divide the result by the standard deviation to standardize features. The formula for standardization is given as:

# Definition
Standardization is the process of centering the variable at zero and standardizing the variance to 1. 

We subtract the mean from each observation and then divide the result by the standard deviation to standardize features. The formula for standardization is given as:

Standardization is one of the techniques used for scaling features, and we will learn about this technique and its formula while exploring an example that illustrates how it works, alongside some points to keep in mind when using this technique and a sample code.


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

Standardization

Definition