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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

## Resampling

Among the multiple approaches available to handle imbalanced datasets, the widely adopted technique for dealing with highly unbalanced datasets is called resampling. It makes the dataset more balanced by removing samples from the majority class and/or adding more examples from the minority class. This technique is straightforward to implement, fast, and an excellent starting point.

Two different methods can achieve resampling:

- **Oversampling**: This method consists of copying some instances from the under-represented class (minority class) and adding them to the training set.

# Resampling

Among the multiple approaches available to handle imbalanced datasets, the widely adopted technique for dealing with highly unbalanced datasets is called resampling. It makes the dataset more balanced by removing samples from the majority class and/or adding more examples from the minority class. This technique is straightforward to implement, fast, and an excellent starting point.

Two different methods can achieve resampling:

- **Oversampling**: This method consists of copying some instances from the under-represented class (minority class) and adding them to the training set.

Resampling is one of the most used techniques for dealing with unbalanced data sets. It is straightforward, fast, and efficient. This lesson will teach about the main resampling methods and their drawbacks.


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

Resampling

Resampling