#### 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
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Classification Using K-Nearest Neighbors
Time Series Analysis
Python Reference
Statistics
Glossary of Algorithms and Methods in Data Science
Other Books You May Enjoy
Index

## Swim preference – representing data using a decision tree

We may have certain preferences that determine whether or not we would swim. These can be recorded in a table, as follows:

 Swimming suit Water temperature Swim preference None Cold No None Warm No Small Cold No Small Warm No Good Cold No Good Warm Yes

The data in this table can alternatively be presented in the following decision tree:

Figure 3.1: Decision tree for the Swim preference example

At the root node, we ask the question—do you have a swimming suit? The response to the question separates the available data into three groups, each with two rows. If the attribute is `swimming suit = none`, then two rows have the swim preference attribute as `no`. Therefore, there is no need to ask a question about the temperature of the water, as all the samples with the `swimming suit = none` attribute would be classified as `no`. This is also true for the `swimming suit = small` attribute. In the case of `swimming suit = good`, the remaining two rows can be divided into two...