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You're reading from Mastering Predictive Analytics with R

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Published inJun 2015

Reading LevelExpert

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ISBN-139781783982806

Edition1st Edition

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RStudio

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Predictive Analytics

Authors (2):

Rui Miguel Forte

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Predicting the energy efficiency of buildings

In this section, we will investigate how neural networks can be used to solve a real-world regression problem. Once again, we turn to the UCI Machine Learning Repository for our data set. We've chosen to try out the energy efficiency data set available at http://archive.ics.uci.edu/ml/datasets/Energy+efficiency. The prediction task is to use various building characteristics, such as surface area and roof area, in order to predict the energy efficiency of a building, which is expressed in the form of two different metrics—heating load and cooling load.

This is a good example for us to try out as we can demonstrate how neural networks can be used to predict two different outputs with a single network. The full attribute description of the data set is given in the following table:

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Mastering Predictive Analytics with R

Published in: Jun 2015Publisher: ISBN-13: 9781783982806

Authors (2)

Rui Miguel Forte

Why do you think this reviewer is suitable for this book? Mr. Rui Miguel Forte has authored a book for Packt titled “Mastering Predictive Analytics with R”. The book has received a 5 star rating. He has 3 years experience as a Data Scientist. He has knowledge of Scala, Python, R, PHP. • Has the reviewer published any articles or blogs on this or a similar tool/technology ? [Provide Links and References] A brief of Unsupervised learning has been covered in his book “Mastering Predictive Analytics with R” https://www.safaribooksonline.com/library/view/mastering-predictive-analytics/9781783982806/ https://www.linkedin.com/profile/view?id=AAkAAAC5YUIBYL7LyLCWZ6LsR0ENJxByC2jU9AU&authType=NAME_SEARCH&authToken=c1Pg&locale=en_US&trk=tyah&trkInfo=clickedVertical%3Amynetwork%2CclickedEntityId%3A12149058%2CauthType%3ANAME_SEARCH%2Cidx%3A1-1-1%2CtarId%3A1444032603690%2Ctas%3ARui%20Miguel%20Forte • Feedback on the Outline (in case outline has been shared with the reviewer) The author said the outline is good to go. • Did the reviewer share any concerns or questions regarding the reviewing process? (related to the schedule, commitment, or any additional comments) No
Read more about Rui Miguel Forte

Rui Miguel Forte

Rui Miguel Forte is currently the chief data scientist at Workable. He was born and raised in Greece and studied in the UK. He is an experienced data scientist, having over 10 years of work experience in a diverse array of industries spanning mobile marketing, health informatics, education technology, and human resources technology. His projects have included predictive modeling of user behavior in mobile marketing promotions, speaker intent identification in an intelligent tutor, information extraction techniques for job applicant resumes and fraud detection for job scams. He currently teaches R, MongoDB, and other data science technologies to graduate students in the Business Analytics MSc program at the Athens University of Economics and Business. In addition, he has lectured in a number of seminars, specialization programs, and R schools for working data science professionals in Athens. His core programming knowledge is in R and Java, and he has extensive experience working with a variety of database technologies such as Oracle, PostgreSQL, MongoDB, and HBase. He holds a Master’s degree in Electrical and Electronic Engineering from Imperial College London and is currently researching machine learning applications in information extraction and natural language processing.
Read more about Rui Miguel Forte

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Column name	Type	Definition
`relCompactness`	Numerical	Relative compactness
`surfArea`	Numerical	Surface area
`wallArea`	Numerical	Wall area
`roofArea`	Numerical	...

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Predicting the energy efficiency of buildings

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