#### 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

## Plotting bubble plots

Let's see how to plot bubble plots. The size of each circle in a 2D bubble plot represents the amplitude of that particular point.

### How to do it…

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

```import numpy as np
import matplotlib.pyplot as plt ```
2. Define the number of values that we should generate:

```# Define the number of values
num_vals = 40```
3. Generate random values for `x` and `y`:

```# Generate random values
x = np.random.rand(num_vals)
y = np.random.rand(num_vals)```
4. Define the area value for each point in the bubble plot:

```# Define area for each bubble
# Max radius is set to a specified value
```# Generate colors
```# Plot the points
7. The full code is in the `bubble_plot.py` file that's already provided to you. If you run this code, you will see the following figure: