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
Contributors
Preface
Glossary of Algorithms and Methods in Data Science
Index

Gender classification – clustering to classify


The following data is taken from the gender classification example, Problem 6, Chapter 2, Naive Bayes:

Height in cm

Weight in kg

Hair length

Gender

180

75

Short

Male

174

71

Short

Male

184

83

Short

Male

168

63

Short

Male

178

70

Long

Male

170

59

Long

Female

164

53

Short

Female

155

46

Long

Female

162

52

Long

Female

166

55

Long

Female

172

60

Long

?

 

To simplify matters, we will remove the column entitled Hair length. We will also remove the column entitled Gender, since we would like to cluster the people in the table based on their height and weight. We would like to establish whether the eleventh person in the table is more likely to be a man or a woman using clustering:

Height in cm

Weight in kg

180

75

174

71

184

83

168

63

178

70

170

59

164

53

155

46

162

52

166

55

172

60

Analysis

We may apply scaling to the initial data, but to simplify matters, we will use the unscaled data in the algorithm. We will cluster the data we have into two clusters, since there are two possibilities for gender—male or female. Then, we will...