Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Predictive Analytics using Rattle and Qlik Sense
  • Toc
  • feedback
Predictive Analytics using Rattle and Qlik Sense

Predictive Analytics using Rattle and Qlik Sense

By : Ferran Garcia Pagans, Fernando G Pagans
4 (5)
close
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)
close
close
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
chevron up
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

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...
End of Section 3
Next Section
bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete