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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Customer segmentation for various domains


Note that we are considering e-commerce data here, but you can consider other datasets of various domains. You can build customer segmentation for a company providing travel services, financial services, and so on. The data points will vary from domain to domain.

For travel services, you could consider how frequently a user is booking flights or rooms using the traveling platform. Demographic and professional information helps a great deal, say, how many times a user uses promotional offers. The data for user activity is important as well.

If you are building a segmentation application for the financial domain, then you can consider the data points such as: the transaction history of the account holder, for example, the frequency of using a debit card or a credit card, per-month income, per-month expenditure, the average balance the customer is maintaining in their bank account(s), the type of account user have, professional information of the customer...

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