#### Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Python Machine Learning Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
The Realm of Supervised Learning
Visualizing Data
Index

## Finding the nearest neighbors

Nearest neighbors model refers to a general class of algorithms that aim to make a decision based on the number of nearest neighbors in the training dataset. Let's see how to find the nearest neighbors.

### How to do it…

1. Create a new Python file, and import the following packages:

```import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors```
2. Let's create some sample two-dimensional data:

```# Input data
X = np.array([[1, 1], [1, 3], [2, 2], [2.5, 5], [3, 1],
[4, 2], [2, 3.5], [3, 3], [3.5, 4]])```
3. Our goal is to find the three closest neighbors to any given point. Let's define this parameter:

```# Number of neighbors we want to find
num_neighbors = 3```
4. Let's define a random datapoint that's not present in the input data:

```# Input point
input_point = [2.6, 1.7]```
5. We need to see what this data looks like. Let's plot it, as follows:

```# Plot datapoints
plt.figure()
plt.scatter(X[:,0], X[:,1], marker='o', s=25, color='k')```
6. In order to find the nearest...