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Predictive Analytics using Rattle and Qlik Sense

Predictive Analytics using Rattle and Qlik Sense

By : Ferran Garcia Pagans, Fernando G Pagans
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Predictive Analytics using Rattle and Qlik Sense

Predictive Analytics using Rattle and Qlik Sense

4 (5)
By: Ferran Garcia Pagans, Fernando G Pagans
Buy this Book

Overview of this book

If you are a business analyst who wants to understand how to improve your data analysis and how to apply predictive analytics, then this book is ideal for you. This book assumes you have some basic knowledge of statistics and a spreadsheet editor such as Excel, but knowledge of QlikView is not required.
Table of Contents (11 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Getting Ready with Predictive Analytics
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1. Getting Ready with Predictive Analytics
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Analytics, predictive analytics, and data visualization
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Purpose of the book
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Introducing R, Rattle, and Qlik Sense Desktop
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Installing the environment
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Downloading and installing Rattle
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Installing Qlik Sense Desktop
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Exploring Qlik Sense Desktop
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Further learning
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Summary
2
2. Preparing Your Data
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2. Preparing Your Data
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Datasets, observations, and variables
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Loading data
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Transforming data
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Cleaning up
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Exporting data
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Further learning
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Summary
3
3. Exploring and Understanding Your Data
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3. Exploring and Understanding Your Data
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Text summaries
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Visualizing distributions
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Correlations among input variables
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Further learning
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Summary
4
4. Creating Your First Qlik Sense Application
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4. Creating Your First Qlik Sense Application
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Customer segmentation and customer buying behavior
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Loading data and creating a data model
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Creating a simple data app
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Associative logic
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Creating charts
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Analyzing your data
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Further learning
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Summary
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5. Clustering and Other Unsupervised Learning Methods
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5. Clustering and Other Unsupervised Learning Methods
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Machine learning – unsupervised and supervised learning
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Further learning
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Summary
6
6. Decision Trees and Other Supervised Learning Methods
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6. Decision Trees and Other Supervised Learning Methods
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Partitioning datasets and model optimization
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Decision Tree Learning
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Entropy and information gain
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Underfitting and overfitting
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Using a Decision Tree to classify credit risks
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Ensemble classifiers
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Other models
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Further learning
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Summary
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7. Model Evaluation
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7. Model Evaluation
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Cross-validation
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Regression performance
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Measuring the performance of classifiers
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Further learning
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Summary
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8. Visualizations, Data Applications, Dashboards, and Data Storytelling
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8. Visualizations, Data Applications, Dashboards, and Data Storytelling
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Data visualization in Qlik Sense
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Data analysis, data applications, and dashboards
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Data storytelling with Qlik Sense
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Further learning
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Summary
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9. Developing a Complete Application
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9. Developing a Complete Application
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Understanding the bike rental problem
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Exploring the data with Qlik Sense
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Creating a Qlik Sense App to control the activity
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Using Rattle to forecast the demand
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Model evaluation
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Further learning
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Summary
10
Index
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Index

Understanding the bike rental problem

As in other examples, we're going to use a dataset from the School of Information and Computer Science at the University of California. The dataset is taken from the location specified here:

Note

Bache, K. and Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

In this chapter, we will use the Bike Sharing Dataset. You can access the dataset here https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset. This dataset contains the hourly and daily count of rental bikes between the years of 2011 and 2012 in the Washington bikeshare system, with the corresponding weather and seasonal information. This dataset was originally taken from the location specified here:

Note

Fanaee-T, Hadi, and Gama, Joao, 'Event labeling combining ensemble detectors and background knowledge', Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg...

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