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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

## K-Means discretization
This discretization algorithm is an unsupervised univariate discretization algorithm that applies the classical K-means clustering to one-dimensional continuous data.

K-Means Clustering algorithm is first used to partition the input data values into clusters. Then, the discretization strategy for the input data is made using the information of maximum and minimum values of the data set, computed cluster centers, and midpoints between every two clusters.

The algorithm works as follows:

1. Step 1: We first create K random centers.
2. Step 2: Then, we associate each point with the closest center.
3. Step 3: Finally, each center position is computed as the barycenter of its associated points.

> Steps 2 and 3 are repeated until we reach convergence. 

There are a few points to consider when working with K-Means discretization, like:

## Points to consider
- This method does not help improve the values spread.
- It handles outliers, even though outliers may influence the centroid.
- It is efficient when combined with categorical encodings.

To implement this method, Run the following code snippet:

# K-Means discretization
This discretization algorithm is an unsupervised univariate discretization algorithm that applies the classical K-means clustering to one-dimensional continuous data.

K-Means Clustering algorithm is first used to partition the input data values into clusters. Then, the discretization strategy for the input data is made using the information of maximum and minimum values of the data set, computed cluster centers, and midpoints between every two clusters.

The algorithm works as follows:

1. Step 1: We first create K random centers.
2. Step 2: Then, we associate each point with the closest center.
3. Step 3: Finally, each center position is computed as the barycenter of its associated points.

> Steps 2 and 3 are repeated until we reach convergence. 

There are a few points to consider when working with K-Means discretization, like:

# Points to consider
- This method does not help improve the values spread.
- It handles outliers, even though outliers may influence the centroid.
- It is efficient when combined with categorical encodings.

To implement this method, Run the following code snippet:

This lesson aims to illustrate a discretization method that uses the k-means algorithm called k-means discretization. We will explore how the algorithm works, some points to consider when working with k-means methods, and of course, the python code for this technique.


K-Means discretization

This lesson aims to illustrate a discretization method that uses the k-means algorithm called k-means discretization. We will explore how the algorithm works, some points to consider when working with k-means methods, and of course, the python code for this technique.

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

K-Means discretization

K-Means discretization