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


In this chapter, we learned how the clustering of data is very efficient and can be used to facilitate the faster classification of new features by classifying a feature as belonging to the class that is represented in the cluster of that feature. An appropriate number of clusters can be determined through cross-validation, by choosing the one that results in the most accurate classification.

Clustering orders data according to its similarity. The more clusters there are, the greater the similarity between the features in a cluster, but the fewer features in a cluster there are.

We also learned that the k-means algorithm is a clustering algorithm that tries to cluster features in such a way that the mutual distance of the features in a cluster is minimized. To do this, the algorithm computes the centroid of each cluster and a feature belongs to the cluster whose centroid is closest to it. The algorithm finishes the computation of the clusters as soon as they or their centroids no longer...