Predictive Analytics using Rattle and Qlik Sense
作者: Ferran Garcia Pagans
出版社: Packt Publishing - ebooks Account
出版年: 2015-6-29
页数: 174
定价: USD 34.99
装帧: Paperback
ISBN: 9781784395803
Table of Contents
Predictive Analytics Using Rattle and Qlik Sense
Credits
About the Author
About the Reviewers
www.PacktPub.com
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Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
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 R
Starting the R Console to test your R installation
Downloading and installing Rattle
Installing Qlik Sense Desktop
Exploring Qlik Sense Desktop
Further learning
Summary
2. Preparing Your Data
Datasets, observations, and variablesLoading data
Loading a CSV File
Transforming data
Transforming data with Rattle
Rescaling data
Using the Impute option to deal with missing values
Recoding variables
Binning
Indicator variables
Join Categories
As Category
As Numeric
Cleaning up
Exporting data
Further learning
Summary
3. Exploring and Understanding Your Data
Text summaries
Summary reports
Measures of central tendency – mean, median, and mode
Measures of dispersion – range, quartiles, variance, and standard deviation
Range
Quartiles
Variance
Standard deviation
Measures of the shape of the distribution – skewness and kurtosis
Showing missing values
Visualizing distributions
Numeric variables
Box plots
Histograms
Cumulative plots
Categorical variables
Bar plots
Mosaic plots
Correlations among input variables
The Explore Missing and Hierarchical options
Further learningSummary
4. Creating Your First Qlik Sense Application
Customer segmentation and customer buying behavior
Loading data and creating a data model
Preparing the data
Creating a simple data app
Associative logic
Creating charts
Analyzing your data
Further learning
Summary
5. Clustering and Other Unsupervised Learning Methods
Machine learning – unsupervised and supervised learning
Cluster analysis
Centroid-based clustering the using K-means algorithm
Customer segmentation with K-means clustering
Preparing the data in Qlik Sense
Creating a customer segmentation sheet in Qlik Sense
Hierarchical clustering
Association analysis
Further learning
Summary
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
Using Rattle to score new loan applications
Creating a Qlik Sense application to predict credit risks
Ensemble classifiers
Boosting
Random Forest
Supported Vector Machines
Other models
Linear and Logistic Regression
Neural Networks
Further learningSummary
7. Model Evaluation
Cross-validation
Regression performance
Predicted versus Observed Plot
Measuring the performance of classifiers
Confusion matrix, accuracy, sensitivity, and specificity
Risk Chart
ROC Curve
Further learning
Summary
8. Visualizations, Data Applications, Dashboards, and Data Storytelling
Data visualization in Qlik Sense
Visualization toolbox
Creating a bar chart
The Data menu
The Sorting menu
The Add-ons menu
The Appearance menu
Data analysis, data applications, and dashboards
Qlik Sense data analysis
In-memory analysis
Associative experience
Data applications and dashboards
The DAR approach
Data storytelling with Qlik Sense
Creating a new story
Further learning
Summary
9. Developing a Complete Application
Understanding the bike rental problem
Exploring the data with Qlik Sense
Checking for temporal patterns
Visual correlation analysis
Creating a Qlik Sense App to control the activity
Using Rattle to forecast the demand
Correlation Analysis with Rattle
Building a modelImproving performance
Model evaluation
Scoring new data
Further learning
Summary
Index
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