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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

## Combining oversampling and undersampling
Oversampling methods duplicate or create new synthetic examples in the minority class, whereas undersampling methods delete or merge examples in the majority class. Both types of resampling can be effective when used in isolation. However, the outcome can be even better when both methods are combined. 

There are two main ways to combine oversampling and undersampling provided by imblearn:

## SMOTE & Tomek Links 

Using this code snippet below, run it to see the result:



# Combining oversampling and undersampling
Oversampling methods duplicate or create new synthetic examples in the minority class, whereas undersampling methods delete or merge examples in the majority class. Both types of resampling can be effective when used in isolation. However, the outcome can be even better when both methods are combined. 

There are two main ways to combine oversampling and undersampling provided by imblearn:

# SMOTE & Tomek Links 

Using this code snippet below, run it to see the result:



We will see in this lesson another method that is often used to balance imbalanced datasets and help the classifier learn and predict more accurately.


Combining oversampling and undersampling

We will see in this lesson another method that is often used to balance imbalanced datasets and help the classifier learn and predict more accurately.

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

Combining oversampling and undersampling

Combining oversampling and undersampling