Predictive analytics using Rattle and Qlik sense : create comprehensive solutions for predictive analysis using Rattle and share them with Qlik Sense / Ferran Garcia Pagans.

By: Pagans, Ferran Garcia [author.]
Material type: TextTextSeries: Professional expertise distilled: Publisher: Birmingham, UK : Packt Publishing, 2015Description: 1 online resource (1 volume) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9781784390785; 178439078XOther title: Create comprehensive solutions for predictive analysis using Rattle and share them with Qlik SenseSubject(s): Data mining -- Computer programs | Information visualization -- Computer programs | COMPUTERS -- Programming -- GeneralGenre/Form: Electronic books. | Electronic books. Additional physical formats: Print version:: Predictive analytics using Rattle and Qlik Sense : create comprehensive solutions for predictive analysis using Rattle and share them with Qlik Sense.DDC classification: 005.1 LOC classification: QA76.9.D343Online resources: Click here to access online
Contents:
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 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; Chapter 2: Preparing Your Data; Datasets, observations, and variables
Loading dataLoading 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; Chapter 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; Measures of the shape of the distribution -- skewness and kurtosis
Showing missing valuesVisualizing 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; Chapter 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
Chapter 5: Clustering and Other Unsupervised Learning MethodsMachine learning -- unsupervised and supervised learning; Cluster analysis; Centroid-based clustering the using K-means algorithm; Customer segmentation with K-means clustering; Creating a customer segmentation sheet in Qlik Sense; Hierarchical clustering; Association analysis; Further learning; Summary; Chapter 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 risksUsing 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; Chapter 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
Summary: 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.
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Online resource; title from cover (Safari, viewed July 21, 2015).

Includes index.

Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 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; Chapter 2: Preparing Your Data; Datasets, observations, and variables

Loading dataLoading 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; Chapter 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; Measures of the shape of the distribution -- skewness and kurtosis

Showing missing valuesVisualizing 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; Chapter 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

Chapter 5: Clustering and Other Unsupervised Learning MethodsMachine learning -- unsupervised and supervised learning; Cluster analysis; Centroid-based clustering the using K-means algorithm; Customer segmentation with K-means clustering; Creating a customer segmentation sheet in Qlik Sense; Hierarchical clustering; Association analysis; Further learning; Summary; Chapter 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 risksUsing 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; Chapter 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

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.

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