#### 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

## Playing chess example

We will again use the examples from Chapter 2, Naive Bayes, and Chapter 3, Decision Tree, as follows:

 Temperature Wind Sunshine Play Cold Strong Cloudy No Warm Strong Cloudy No Warm None Sunny Yes Hot None Sunny No Hot Breeze Cloudy Yes Warm Breeze Sunny Yes Cold Breeze Cloudy No Cold None Sunny Yes Hot Strong Cloudy Yes Warm None Cloudy Yes Warm Strong Sunny ?

However, we would like to use a random forest consisting of four random decision trees to find the result of the classification.

### Analysis

We are given M=4 variables from which a feature can be classified. Thus, we choose the maximum number of the variables considered at the node to:

We are given the following features:

```[['Cold', 'Strong', 'Cloudy', 'No'], ['Warm', 'Strong', 'Cloudy', 'No'], ['Warm', 'None', 'Sunny',
'Yes'], ['Hot', 'None', 'Sunny', 'No'], ['Hot', 'Breeze', 'Cloudy', 'Yes'], ['Warm', 'Breeze',
'Sunny', 'Yes'], ['Cold', 'Breeze', 'Cloudy', 'No'], ['Cold', 'None', 'Sunny', 'Yes'], ['Hot', 'Strong', 'Cloudy', 'Yes'], ['Warm', 'None', 'Cloudy', 'Yes...```