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F# for Machine Learning Essentials

You're reading from  F# for Machine Learning Essentials

Product type Book
Published in Feb 2016
Publisher
ISBN-13 9781783989348
Pages 194 pages
Edition 1st Edition
Languages
Author (1):
Sudipta Mukherjee Sudipta Mukherjee
Profile icon Sudipta Mukherjee

Table of Contents (16) Chapters

F# for Machine Learning Essentials
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Introduction to Machine Learning 2. Linear Regression 3. Classification Techniques 4. Information Retrieval 5. Collaborative Filtering 6. Sentiment Analysis 7. Anomaly Detection Index

Predicting a traffic jam using a decision tree: a case study


When I go home from the office, I face a traffic jam, as shown in the following image, as many other commuters in Bangalore do almost every day.

I thought if I could only predict a traffic jam, I would reach home early to play with my son. I observed that if it rains and if it is a weekday and if it is past 4:30 PM in the dial, a traffic jam is highly likely.

So I thought I could use a decision tree to predict whether there will be a traffic jam today or not. In the following example, I used a toy dataset that I fabricated. In the real settings, the data has to be filled following a month or few months' observations, because anomalies do exist. Sometimes, even if it rains and even if it is a weekday, there is no traffic jam. The reason may be that there is a cricket match that people want to watch and they have taken a day off work or are working from home. However, these sorts of situations are not common and shouldn't be considered...

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