Book Image

Artificial Intelligence with Python Cookbook

By : Ben Auffarth
Book Image

Artificial Intelligence with Python Cookbook

By: Ben Auffarth

Overview of this book

Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.
Table of Contents (13 chapters)

Clustering market segments

In this recipe, we'll apply clustering methods in order to find groups of customers for marketing purposes. We'll look at the German Credit Risk dataset, and we'll try to identify different segments of customers; ideally, we'd want to find the groups that are most profitable and different at the same time, so we can target them with advertising.

Getting ready

For this recipe, we'll be using a dataset of credit risk, usually referred to in full as the German Credit Risk dataset. Each row describes a person who took a loan, gives us a few attributes about the person, and tells us whether the person paid the loan back (that is, whether the credit was a good or bad risk).

We'll need to download and load up the German credit data as follows:

import pandas as pd
names = ['existingchecking', 'duration', 'credithistory'...