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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

Using a Decision Tree to classify credit risks

In this section, we will create a model to classify credit risks. In this section, we will create the model; we won't look at the performance of the model. We'll evaluate the performance of the model and improve it in the next chapter.

As we did before, to create this example, we'll download a dataset from the UCI Machine Learning Repository. We'll use a dataset called Statlog (German Credit Data) Dataset. The source of the dataset is Professor Dr. Hans Hofmann from Institut für Statistik und Ökonometrie, Universität Hamburg. The dataset classifies people described by a set of attributes as good or bad credit risks.

The dataset is downloaded from the following link:

https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29

In the following screenshot, you can see the original form of this dataset. The screenshot shows us the top ten lines of the dataset. The dataset doesn't have a header line. It contains 20 attributes, and the...

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