#### 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

## Building a logistic regression classifier

Despite the word regression being present in the name, logistic regression is actually used for classification purposes. Given a set of datapoints, our goal is to build a model that can draw linear boundaries between our classes. It extracts these boundaries by solving a set of equations derived from the training data.

### How to do it…

1. Let's see how to do this in Python. We will use the `logistic_regression.py` file that is provided to you as a reference. Assuming that you imported the necessary packages, let's create some sample data along with training labels:

```import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt

X = np.array([[4, 7], [3.5, 8], [3.1, 6.2], [0.5, 1], [1, 2], [1.2, 1.9], [6, 2], [5.7, 1.5], [5.4, 2.2]])
y = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])```

Here, we assume that we have three classes.

2. Let's initialize the logistic regression classifier:

`classifier = linear_model.LogisticRegression(solver='liblinear', C=100...`