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Predictive Analytics Using Rattle and Qlik Sense

By : Ferran Garcia Pagans, Fernando G Pagans
Book Image

Predictive Analytics Using Rattle and Qlik Sense

By: Ferran Garcia Pagans, Fernando G Pagans

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Predictive Analytics Using Rattle and Qlik Sense
Ferran Garcia Pagans | Fernando G Pagans
Jun, 2015 | 242 pages
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Table of Contents (16 chapters)
Predictive Analytics Using Rattle and Qlik Sense
Predictive Analytics Using Rattle and Qlik Sense
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Ready with Predictive Analytics
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
Preparing Your Data
Preparing Your Data
Datasets, observations, and variables
Loading data
Transforming data
Cleaning up
Exporting data
Further learning
Summary
3
Exploring and Understanding Your Data
Exploring and Understanding Your Data
Text summaries
Visualizing distributions
Correlations among input variables
Further learning
Summary
4
Creating Your First Qlik Sense Application
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
Clustering and Other Unsupervised Learning Methods
Clustering and Other Unsupervised Learning Methods
Machine learning – unsupervised and supervised learning
Further learning
Summary
6
Decision Trees and Other Supervised Learning Methods
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
Model Evaluation
Model Evaluation
Cross-validation
Regression performance
Measuring the performance of classifiers
Further learning
Summary
8
Visualizations, Data Applications, Dashboards, and Data Storytelling
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
Developing a Complete Application
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
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:

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' AND Sex = 'male' AND...
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