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  • Book Overview & Buying Predictive Analytics using Rattle and Qlik Sense
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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
4 (5)
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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
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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
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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
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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
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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

Decision Tree Learning

Decision Tree Learning uses past observations to learn how to classify them and also try to predict the class of a new observation. For example, in a bank, we may have historical information on the granting of loans. Usually, past loan information includes a customer profile and whether the customer defaulted or not. Based on this information, the algorithm can learn to predict whether a new customer will default.

We usually represent a Decision Tree as we did in the following diagram. The root node is at the top, and the leaves of the tree are at the bottom, the leaves represent a decision. In order to create rules from a tree, we need to start from the root node, and then we work downwards, towards the leaves. The following diagram represents a sample Decision Tree:

Decision Tree Learning

After studying the preceding diagram of a Decision Tree, we can obtain these rules:

If Purpose = 'Education' AND Sex = 'male' AND Age > 25 Then No Default
If Purpose = 'Education...

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