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 studied the analysis of time-dependent data. The two most important factors in this analysis are trends and seasonality.

The analysis of trends can be considered as determining the function around which the data is distributed. Using the fact that data is dependent on time, this function can be determined using regression. Many phenomena have a linear trend line, whereas others may not follow a linear pattern.

We also learned that the analysis of seasonality tries to detect regular patterns occurring in time repeatedly, such as higher sales before Christmas. To detect a seasonal pattern, it is essential to divide data into the different seasons in such a way that a pattern reoccurs in the same season. This division can divide a year into months, a week into days or into workdays and the weekend, and so on. An appropriate division into seasons and analyzing patterns in those is the key to good seasonal analysis.

Once trend and seasonality have been analyzed in the...