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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 imbalanced data

Usually, when working on a classification problem in a machine learning project, we may encounter what we call imbalanced datasets, which is an issue where the classes are not represented equally, causing many problems for some models.
In this part of the course, we will discuss a technique that helps reduce this effect on our machine learning algorithms called 'Resampling.'

## What is an imbalanced dataset?
Imbalanced data typically refers to a problem with classification problems where the classes are not represented equally.
A typical example of imbalanced data is encountered in email classification problems where emails are classified as ```ham``` or ```spam```. The number of spam emails is usually lower than the number of relevant (ham) emails. So, using the original distribution of two classes leads to an imbalanced dataset.

The problem of imbalanced datasets is prevalent in machine learning, where we have datasets with a disproportionate ratio of observations in each class. In other words, this problem arises when one set of classes dominates over another set of classes. It causes the machine learning model to be more biased towards the majority class, resulting in the poor classification of minority classes. 

We can have a class imbalance problem on two-class (binary) classification problems and multi-class classification problems. Most techniques can be used on either.

# Resampling imbalanced data

Usually, when working on a classification problem in a machine learning project, we may encounter what we call imbalanced datasets, which is an issue where the classes are not represented equally, causing many problems for some models.
In this part of the course, we will discuss a technique that helps reduce this effect on our machine learning algorithms called 'Resampling.'

# What is an imbalanced dataset?
Imbalanced data typically refers to a problem with classification problems where the classes are not represented equally.
A typical example of imbalanced data is encountered in email classification problems where emails are classified as ```ham``` or ```spam```. The number of spam emails is usually lower than the number of relevant (ham) emails. So, using the original distribution of two classes leads to an imbalanced dataset.

The problem of imbalanced datasets is prevalent in machine learning, where we have datasets with a disproportionate ratio of observations in each class. In other words, this problem arises when one set of classes dominates over another set of classes. It causes the machine learning model to be more biased towards the majority class, resulting in the poor classification of minority classes. 

We can have a class imbalance problem on two-class (binary) classification problems and multi-class classification problems. Most techniques can be used on either.

Imbalanced datasets are considered to be a big problem for classification tasks. We will learn about this issue during this lesson while exploring a real example for a better illustration.


What is an imbalanced dataset?

Imbalanced datasets are considered to be a big problem for classification tasks. We will learn about this issue during this lesson while exploring a real example for a better illustration.

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

What is an imbalanced dataset?

Resampling imbalanced data