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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook Supervised and unsupervised machine learning simplified

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Length 570 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Machine Learning

Author (1):

Atul Tripathi

View More author details

Table of Contents (15) Chapters

Preface

1. Introduction to Machine Learning FREE CHAPTER

2. Classification

3. Clustering

4. Model Selection and Regularization

5. Nonlinearity

6. Supervised Learning

7. Unsupervised Learning

8. Reinforcement Learning

9. Structured Prediction

10. Neural Networks

11. Deep Learning

12. Case Study - Exploring World Bank Data

Exploring World Bank data

13. Case Study - Pricing Reinsurance Contracts

Pricing reinsurance contracts

14. Case Study - Forecast of Electricity Consumption

Markov chains - the multi-channel attribution model

A customer's journey undergoes a path of different channels before a purchase on an e-commerce website. Multi-channel attribution assigns a value to each step in the journey. The question is how one can identify the value in the actions people take on your website that lead to a conversion. Commonly, businesses use "last click" attribution, which means assigning all the conversion value to the last step in the journey or "first click" attribution. The first step in developing multi-channel attribution analytics is to understand the customer's journey - from awareness through purchase to after-purchase support. The ultimate goal is to develop loyal customers who spend a significant money on purchases, recommend the brand to others, and it potentially becomes a brand.

Getting ready

In order to perform a Markov chains multi-channel attribution model we shall be simulating customer journeys containing three...

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Authors (1)

Atul Tripathi

Atul Tripathi

Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

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