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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

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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