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)

Estimating customer lifetime value

In this recipe, we will learn how to compute lifetime values and the value a customer provides to this company. This is important for the marketing budget – for example, in lead acquisition or ads spent based on customer segments. We'll do this by modeling separately changes in customer purchase patterns over time and purchase values.

Getting ready

We'll need the lifetimes package for this recipe. Let's install it as shown in the following code:

pip install lifetimes

Now we can get started.

How to do it...

Datasets used for customer lifetime values can be either transactional or summarized by the customer.

The summary data should include the following statistics:

  • T: The transaction period; the elapsed time since the first purchase by the customer
  • Frequency: The number of purchases by a customer within the observation period
  • Monetary value: The average value of purchases
  • Recency: The age of the customer at the time of the last...