Book Image

Smarter Decisions - The Intersection of Internet of Things and Decision Science

By : Jojo Moolayil
Book Image

Smarter Decisions - The Intersection of Internet of Things and Decision Science

By: Jojo Moolayil

Overview of this book

With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach. The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science. By the end of this book, you’ll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it

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Smarter Decisions - The Intersection of Internet of Things and Decision Science
Jojo Moolayil
Jul, 2016 | 392 pages
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Table of Contents (15 chapters)
Smarter Decisions – The Intersection of Internet of Things and Decision Science
Smarter Decisions – The Intersection of Internet of Things and Decision Science
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
eBooks, discount offers, and more
eBooks, discount offers, and more
Preface
Preface
Free Chapter
1
IoT and Decision Science
IoT and Decision Science
Understanding the IoT
Demystifying M2M, IoT, IIoT, and IoE
Digging deeper into the logical stack of IoT
The problem life cycle
The problem landscape
The art of problem solving
The problem solving framework
Summary
2
Studying the IoT Problem Universe and Designing a Use Case
Studying the IoT Problem Universe and Designing a Use Case
Connected assets & connected operations
Defining the business use case
Sensing the associated latent problems
Designing the heuristic driven hypotheses matrix (HDH)
Summary
3
The What and Why - Using Exploratory Decision Science for IoT
The What and Why - Using Exploratory Decision Science for IoT
Identifying gold mines in data for decision making
Exploring each dimension of the IoT Ecosystem through data (Univariates)
Studying relationships
Exploratory data analysis
Root Cause Analysis
Summary
4
Experimenting Predictive Analytics for IoT
Experimenting Predictive Analytics for IoT
Resurfacing the problem - What's next?
Linear regression - predicting a continuous outcome
Decision trees
Logistic Regression - Predicting a categorical outcome
Summary
5
Enhancing Predictive Analytics with Machine Learning for IoT
Enhancing Predictive Analytics with Machine Learning for IoT
A Brief Introduction to Machine Learning
Ensemble modeling - random forest
Ensemble modeling - XGBoost
Neural Networks and Deep Learning
Packaging our results
Summary
6
Fast track Decision Science with IoT
Fast track Decision Science with IoT
Setting context for the problem
Defining the problem and designing the approach
Exploratory Data Analysis and Feature Engineering
Building predictive model for the use case
Packaging the solution
Summary
7
Prescriptive Science and Decision Making
Prescriptive Science and Decision Making
Using a layered approach and test control methods to outlive business disasters
Connecting the dots in the problem universe
Story boarding - Making sense of the interconnected problems in the problem universe
Implementing the solution
Summary
8
Disruptions in IoT
Disruptions in IoT
Edge/fog computing
Cognitive Computing - Disrupting intelligence from unstructured data
Next generation robotics and genomics
Autonomous cars
Privacy and security in IoT
Summary
9
A Promising Future with IoT
A Promising Future with IoT
The IoT Business model - Asset or Device as a Service
Smartwatch – A booster to Healthcare IoT
Smart healthcare - Connected Humans to Smart Humans
Evolving from connected cars to smart cars
Summary

Exploratory Data Analysis and Feature Engineering

We will now focus on diving deep into the data and performing exploratory data analysis. The following code downloads the data from my public Git repository and creates a data frame. We'll start by exploring the data at a high level:

>#Read Solar Panel IoT use case CSV data from public repository 
 
>url<-"https://github.com/jojo62000/Smarter_Decisions/raw/master/Chapter%206/Data/Final_SolarData.csv" 
 
>#Load the data into a dataframe 
>data<-read.csv(url) 
 
>#Check the dimensions of the dataframe 
>dim(data) 
[1] 119296     23 
 
>#Take a glimpse into each column of the dataframe 
>str(data) 
 
'data.frame':    119296 obs. of  23 variables: 
 $ location              : Factor w/ 1 level "Peru":  1 1 1 ... 
 $ date_time             : Factor w/ 119308 levels "2015-12-02 00:01:40",... 
 $ solarvoltage          : num  0 0 0 0 0...
Previous Section
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