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Predictive Analytics Using Rattle and Qlik Sense

You're reading from  Predictive Analytics Using Rattle and Qlik Sense

Product type Book
Published in Jun 2015
Publisher
ISBN-13 9781784395803
Pages 242 pages
Edition 1st Edition
Languages
Authors (2):
Ferran Garcia Pagans Ferran Garcia Pagans
Profile icon Ferran Garcia Pagans
Fernando G Pagans Fernando G Pagans
Profile icon Fernando G Pagans
View More author details
Toc

Table of Contents (16) Chapters close

Predictive Analytics Using Rattle and Qlik Sense
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Ready with Predictive Analytics 2. Preparing Your Data 3. Exploring and Understanding Your Data 4. Creating Your First Qlik Sense Application 5. Clustering and Other Unsupervised Learning Methods 6. Decision Trees and Other Supervised Learning Methods 7. Model Evaluation 8. Visualizations, Data Applications, Dashboards, and Data Storytelling 9. Developing a Complete Application Index

Other models


In this section, we will see other models provided by Rattle in the Model tab, which aren't supervised learning. These methods are Linear and Logistic Regression, Neural Networks, and Survival Analysis.

Linear and Logistic Regression

Linear Regression is a statistical method to describe the relationship between one or more input variables and one or more output variables. The objective is to create a formula that models the relationships between input and output variables; in this way, we can use this formula to predict new observations.

Imagine you are the manager of a marina, your marina has a gas station and you need to predict the amount of gas oil you will sell during a summer day. On the Mediterranean coast, during the summer, the amount of gas oil sold is correlated to the temperature. The reason is obvious, on sunny days, the temperature rises and more tourists want to use their boats. The example is illustrated in this diagram:

Based on past experience, we know that on...

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