Book Image

Machine Learning with scikit-learn Quick Start Guide

By : Kevin Jolly
Book Image

Machine Learning with scikit-learn Quick Start Guide

By: Kevin Jolly

Overview of this book

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
Table of Contents (10 chapters)

Implementing the k-means algorithm in scikit-learn

Now that you understand how the k-means algorithm works internally, we can proceed to implement it in scikit-learn. We are going to work with the same fraud detection dataset that we used in all of the previous chapters. The key difference is that we are going to drop the target feature, which contains the labels, and identify the two clusters that are used to detect fraud.

Creating the base k-means model

In order to load the dataset into our workspace and drop the target feature with the labels, we use the following code:

import pandas as pd
#Reading in the dataset
df = pd.read_csv('fraud_prediction.csv')
#Dropping the target feature & the index
df = df.drop([&apos...