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 1. Classification Using K-Nearest Neighbors

A nearest neighbor algorithm classifies a data instance based on its neighbors. The class of a data instance determined by the k-nearest neighbors algorithm is the class with the highest representation among the k-closest neighbors.

In this chapter, we will cover the following topics:

  • How to implement the basics of the k-NN algorithm using the example of Mary and her temperature preferences
  • How to choose a correctk value so that the algorithm can perform correctly and with the highest degree of accuracy using the example of a map of Italy
  • How to rescale values and prepare them for the k-NN algorithm using the example of house preferences
  • How to choose a good metric to measure distances between data points
  • How to eliminate irrelevant dimensions in higher-dimensional space to ensure that the algorithm performs accurately using the text classification example