#### 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

## Classifying with a decision tree

Once we have constructed a decision tree from the data with the attributes A1, ..., Am and the classes {c1, ..., ck}, we can use this decision tree to classify a new data item with the attributes A1, ..., Am into one of the classes {c1, ..., ck}.

Given a new data item that we would like to classify, we can think of each node, including the root, as a question for the data sample: What value does that data sample have for the selected attribute, Ai? Then, based on the answer, we select a branch of the decision tree and move on to the next node. Then, another question is answered about the data sample, and another, until the data sample reaches the leaf node. A leaf node has one of the classes {c1, ..., ck} associated with it; for example, ci. Then, the decision tree algorithm would classify the data sample into the class, ci.

### Classifying a data sample with the swimming preference decision tree

Let's construct a decision tree for the swimming preference example...