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.
-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating
Machine Learning with scikit-learn Quick Start Guide
By :
Machine Learning with scikit-learn Quick Start Guide
By:
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)
Preface
Free Chapter
Introducing Machine Learning with scikit-learn
Predicting Categories with K-Nearest Neighbors
Predicting Categories with Logistic Regression
Predicting Categories with Naive Bayes and SVMs
Predicting Numeric Outcomes with Linear Regression
Classification and Regression with Trees
Clustering Data with Unsupervised Machine Learning
Performance Evaluation Methods
Other Books You May Enjoy
Customer Reviews
