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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

## Perform clustering
One of the techniques to deal with point coordinates is to treat them in relation to each other by performing clustering and assigning each cluster-id to a point. This technique will create a new categorical variable that we can one-hot encode to get better results.
After testing many clustering algorithms like K-means, DBSCAN, and hierarchical clustering, the latter two seem to give the best results for geospatial features. K-means works well when maximizing variance, which is good if the feature space is linear in nature. However, if it is non-linear, then hierarchical clustering and DBSCAN will perform better.

The number of clusters depends on the problem, but we have to try and see what gives better results.

Run the following code snippet using K-means and hierarchical clustering algorithms with 5 clusters each:



# Perform clustering
One of the techniques to deal with point coordinates is to treat them in relation to each other by performing clustering and assigning each cluster-id to a point. This technique will create a new categorical variable that we can one-hot encode to get better results.
After testing many clustering algorithms like K-means, DBSCAN, and hierarchical clustering, the latter two seem to give the best results for geospatial features. K-means works well when maximizing variance, which is good if the feature space is linear in nature. However, if it is non-linear, then hierarchical clustering and DBSCAN will perform better.

The number of clusters depends on the problem, but we have to try and see what gives better results.

Run the following code snippet using K-means and hierarchical clustering algorithms with 5 clusters each:



We can perform clustering techniques as a way to handle geospatial data. We will talk about it in this lesson, alongside some illustrations of how this technique works and a sample code. 

Perform clustering

We can perform clustering techniques as a way to handle geospatial data. We will talk about it in this lesson, alongside some illustrations of how this technique works and a sample code.

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

Perform clustering

Perform clustering