Predictive Analytics Using Rattle and Qlik Sense电子书

Predictive Analytics Using Rattle and Qlik Sense

Table of Contents

Predictive Analytics Using Rattle and Qlik Sense

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Instant updates on new Packt books

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 variables

Loading 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 learning

Summary

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 learning

Summary

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 model

Improving performance

Model evaluation

Scoring new data

Further learning

Summary

Index