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 histograms

Let's see how to plot histograms in this recipe. We'll compare two sets of data and build a comparative histogram.

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. We'll compare the production quantity of apples and oranges in this recipe. Let's define some values:

```# Input data
apples = [30, 25, 22, 36, 21, 29]
oranges = [24, 33, 19, 27, 35, 20]

# Number of groups
num_groups = len(apples)```
3. Create the figure and define its parameters:

```# Create the figure
fig, ax = plt.subplots()

# Define the X axis
indices = np.arange(num_groups)

# Width and opacity of histogram bars
bar_width = 0.4
opacity = 0.6```
4. Plot the histogram:

```# Plot the values
hist_apples = plt.bar(indices, apples, bar_width,
alpha=opacity, color='g', label='Apples')

hist_oranges = plt.bar(indices + bar_width, oranges, bar_width,
alpha=opacity, color='b', label='Oranges')```
5. Set the parameters of the plot:

```plt.xlabel('Month')
plt...```