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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
Trimming (or truncation) simply means removing all the outliers and getting rid of them. We only need to decide on a metric to remove all data points to apply this technique.

This might be the Gaussian approximation or the inter-quantile range proximity rule for skewed features for properly distributed variables.

## Key points:
- This method is fast.
- Trimming should be done with care and caution as it can remove too much data (loss of data).

## Demo

Run the following code to trim some outliers using the Inter Quantile Range proximity rule:


# Definition
Trimming (or truncation) simply means removing all the outliers and getting rid of them. We only need to decide on a metric to remove all data points to apply this technique.

This might be the Gaussian approximation or the inter-quantile range proximity rule for skewed features for properly distributed variables.

# Key points:
- This method is fast.
- Trimming should be done with care and caution as it can remove too much data (loss of data).

# Demo

Run the following code to trim some outliers using the Inter Quantile Range proximity rule:


In this lesson, we will be covering one of the simplest methods to use for handling outliers, alongside some key points to bear in mind, and how to implement it using a sample python 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

Trimming

Definition