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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
We center the variable at zero in mean normalization and rescale the distribution to the value range. This procedure involves subtracting the mean from each observation and dividing the result by the difference between the minimum and maximum values. The general formula for normalization is given as:

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

# Definition
We center the variable at zero in mean normalization and rescale the distribution to the value range. This procedure involves subtracting the mean from each observation and dividing the result by the difference between the minimum and maximum values. The general formula for normalization is given as:

In this lesson, we will explore a simple scaling technique called mean normalization, the principle by which this technique works, its formula, and a sample code to use in practice.


Mean normalization

In this lesson, we will explore a simple scaling technique called mean normalization, the principle by which this technique works, its formula, and a sample code 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

Mean normalization

Definition