#### 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

## Mary and her temperature preferences

As an example, if we know that our friend, Mary, feels cold when it is 10°C, but warm when it is 25°C, then in a room where it is 22°C, the nearest neighbor algorithm would guess that our friend would feel warm, because 22 is closer to 25 than to 10.

Suppose that we would like to know when Mary feels warm and when she feels cold, as in the previous example, but in addition, wind speed data is also available when Mary is asked whether she feels warm or cold:

 Temperature in °C Wind speed in km/h Mary's perception 10 0 Cold 25 0 Warm 15 5 Cold 20 3 Warm 18 7 Cold 20 10 Cold 22 5 Warm 24 6 Warm

We could represent the data in a graph, as follows:

Now, suppose we would like to find out how Mary feels when the temperature is 16°C with a wind speed of 3 km/h by using the 1-NN algorithm:

For simplicity, we will use a Manhattan metric to measure the distance between the neighbors on the grid. The Manhattan distance dMan of the neighbor N1=(x1,y1) from the neighbor N2=(x2,y2) is defined as dMan=|x1-x2|+|y1-y2|.

Let's label the grid with distances around the neighbors to see which neighbor with a known class is closest to the point we would like to classify:

We can see that the closest neighbor with a known class is the one with a temperature of 15°C (blue) and a wind speed of 5 km/h. Its distance from the point in question is three units. Its class is blue (cold). The closest red (warm) neighbour is at a distance of four units from the point in question. Since we are using the 1-nearest neighbor algorithm, we just look at the closest neighbor and, therefore, the class of the point in question should be blue (cold).

By applying this procedure to every data point, we can complete the graph, as follows:

Note that, sometimes, a data point might be the same distance away from two known classes: for example, 20°C and 6 km/h. In such situations, we could prefer one class over the other, or ignore these boundary cases. The actual result depends on the specific implementation of an algorithm.