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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
Censoring (or capping) is setting a maximum or a minimum distribution at an arbitrary value.

It simply means the values which are out of range get replaced by these values.

## Key points:
- It does not remove any data.
- It distorts the shape of the distribution of variables.

## Methods of censoring
Censoring can be done using various methods:

### Arbitrarily
This technique is straightforward, where you are free to choose any upper or lower boundary value according to your observation of outliers and use case. 

# Definition
Censoring (or capping) is setting a maximum or a minimum distribution at an arbitrary value.

It simply means the values which are out of range get replaced by these values.

# Key points:
- It does not remove any data.
- It distorts the shape of the distribution of variables.

# Methods of censoring
Censoring can be done using various methods:

## Arbitrarily
This technique is straightforward, where you are free to choose any upper or lower boundary value according to your observation of outliers and use case. 

Censoring is another technique to use when we encounter outliers in the dataset. It is a method that can be implemented in many different manners. We will be exploring all that in this lesson.


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

Censoring

Definition

Key points: