Book Image

Data Science Algorithms in a Week - Second Edition

By : David Natingga
Book Image

Data Science Algorithms in a Week - Second Edition

By: David Natingga

Overview of this book

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Table of Contents (16 chapters)
Title Page
Packt Upsell
Glossary of Algorithms and Methods in Data Science

Chapter 5. Clustering into K Clusters

Clustering is a technique for dividing data into clusters, with the same features in the same cluster.

In this chapter, we will cover the following topics:

  • How to use the k-means clustering algorithm, using an example involving household incomes
  • How to classify features by clustering them first with the features, along with the known classes, using an example of gender classification 
  • How to implement the k-means clustering algorithm in Python in the Implementation of k-means clustering algorithm section
  • An example of house ownership and how to choose an appropriate number of clusters for your analysis
  • How to use the example of house ownership to scale a given set of numerical data appropriately to improve the accuracy of classification by using a clustering algorithm
  • An understanding of how different numbers of clusters alter the meaning of the dividing line between those clusters, using an example of document clustering