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  • 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

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
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Ready with Predictive Analytics
1. Getting Ready with Predictive Analytics
Analytics, predictive analytics, and data visualization
Purpose of the book
Introducing R, Rattle, and Qlik Sense Desktop
Installing the environment
Downloading and installing Rattle
Installing Qlik Sense Desktop
Exploring Qlik Sense Desktop
Further learning
Summary
2
2. Preparing Your Data
2. Preparing Your Data
Datasets, observations, and variables
Loading data
Transforming data
Cleaning up
Exporting data
Further learning
Summary
3
3. Exploring and Understanding Your Data
3. Exploring and Understanding Your Data
Text summaries
Visualizing distributions
Correlations among input variables
Further learning
Summary
4
4. Creating Your First Qlik Sense Application
4. Creating Your First Qlik Sense Application
Customer segmentation and customer buying behavior
Loading data and creating a data model
Creating a simple data app
Associative logic
Creating charts
Analyzing your data
Further learning
Summary
5
5. Clustering and Other Unsupervised Learning Methods
5. Clustering and Other Unsupervised Learning Methods
Machine learning – unsupervised and supervised learning
Further learning
Summary
6
6. Decision Trees and Other Supervised Learning Methods
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6. Decision Trees and Other Supervised Learning Methods
Partitioning datasets and model optimization
Decision Tree Learning
Entropy and information gain
Underfitting and overfitting
Using a Decision Tree to classify credit risks
Ensemble classifiers
Other models
Further learning
Summary
7
7. Model Evaluation
7. Model Evaluation
Cross-validation
Regression performance
Measuring the performance of classifiers
Further learning
Summary
8
8. Visualizations, Data Applications, Dashboards, and Data Storytelling
8. Visualizations, Data Applications, Dashboards, and Data Storytelling
Data visualization in Qlik Sense
Data analysis, data applications, and dashboards
Data storytelling with Qlik Sense
Further learning
Summary
9
9. Developing a Complete Application
9. Developing a Complete Application
Understanding the bike rental problem
Exploring the data with Qlik Sense
Creating a Qlik Sense App to control the activity
Using Rattle to forecast the demand
Model evaluation
Further learning
Summary
10
Index
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...

End of Chapter 6
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