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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
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
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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

Further learning

Predictive Analytics and Data Visualizations are huge topics and in this book I just introduced the key concepts.

Related to Qlik Sense, we forget a lot of important things. In my opinion, the data load editor is among the most powerful. In order to learn more about Qlik Sense and the Data load editor, you may want to check out:

Learning Qlik® Sense: The Official Guide, Christopher Ilacqua, Henric Cronström, James Richardson. Packt Publishing.

In order to increase your knowledge and learn more about Qlik Sence, I suggested you some books and web sites. A very good source of information and advice can be the Qlik Community:

https://community.qlik.com

This is the most active community in the BI landscape.

You also learned how to use Rattle to create predictive models. In this book, we've focused on Clustering, Decision Trees, and Linear Regression; these are the most common predictive techniques. You can use these techniques in your own datasets to provide insights. To improve...

End of Section 6
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