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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

## Handling outliers
When working on a machine learning project, we may encounter many issues in the dataset. One of the most common issues is outliers. This part will detail what an outlier is, the different methods to handle them, and how to implement these methods with some Python code.

## Outliers
Sometimes a dataset can contain extreme values outside the range of what is expected and unlike the other data. These are called outliers, and often machine learning modeling and model skills, in general, can be improved by understanding and even removing these outlier values.



# Handling outliers
When working on a machine learning project, we may encounter many issues in the dataset. One of the most common issues is outliers. This part will detail what an outlier is, the different methods to handle them, and how to implement these methods with some Python code.

# Outliers
Sometimes a dataset can contain extreme values outside the range of what is expected and unlike the other data. These are called outliers, and often machine learning modeling and model skills, in general, can be improved by understanding and even removing these outlier values.



In this lesson of our journey on feature engineering, we will focus on another common issue in most datasets, which is outliers. This lesson will englobe the various widely used techniques to detect and find outliers in a given dataset, including techniques that involve visualizing our data.



Detecting outliers

In this lesson of our journey on feature engineering, we will focus on another common issue in most datasets, which is outliers. This lesson will englobe the various widely used techniques to detect and find outliers in a given dataset, including techniques that involve visualizing our data.

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

Detecting outliers

Handling outliers

Outliers