Predictive Analytics Using Rattle and Qlik Sense

Table of contents

1. Predictive Analytics Using Rattle and Qlik Sense
   1. Table of Contents
   2. Predictive Analytics Using Rattle and Qlik Sense
   3. Credits
   4. About the Author
   5. About the Reviewers
   6. www.PacktPub.com
      1. Support files, eBooks, discount offers, and more
         1. Why subscribe?
         2. Free access for Packt account holders
         3. Instant updates on new Packt books
   7. Preface
      1. What this book covers
      2. What you need for this book
      3. Who this book is for
      4. Conventions
      5. Reader feedback
      6. Customer support
         1. Downloading the example code
         2. Downloading the color images of this book
         3. Errata
         4. Piracy
         5. Questions
   8. 1. Getting Ready with Predictive Analytics
      1. Analytics, predictive analytics, and data visualization
      2. Purpose of the book
      3. Introducing R, Rattle, and Qlik Sense Desktop
      4. Installing the environment
         1. Downloading and installing R
         2. Starting the R Console to test your R installation
      5. Downloading and installing Rattle
      6. Installing Qlik Sense Desktop
      7. Exploring Qlik Sense Desktop
      8. Further learning
      9. Summary
   9. 2. Preparing Your Data
      1. Datasets, observations, and variables
      2. Loading data
         1. Loading a CSV File
      3. Transforming data
         1. Transforming data with Rattle
            1. Rescaling data
            2. Using the Impute option to deal with missing values
            3. Recoding variables
            4. Binning
         2. Indicator variables
            1. Join Categories
            2. As Category
            3. As Numeric
      4. Cleaning up
      5. Exporting data
      6. Further learning
      7. Summary
   10. 3. Exploring and Understanding Your Data
       1. Text summaries
          1. Summary reports
             1. Measures of central tendency – mean, median, and mode
             2. Measures of dispersion – range, quartiles, variance, and standard deviation
                1. Range
                2. Quartiles
                3. Variance
                4. Standard deviation
             3. Measures of the shape of the distribution – skewness and kurtosis
          2. Showing missing values
       2. Visualizing distributions
          1. Numeric variables
             1. Box plots
             2. Histograms
             3. Cumulative plots
          2. Categorical variables
             1. Bar plots
             2. Mosaic plots
       3. Correlations among input variables
          1. The Explore Missing and Hierarchical options
       4. Further learning
       5. Summary
   11. 4. Creating Your First Qlik Sense Application
       1. Customer segmentation and customer buying behavior
       2. Loading data and creating a data model
          1. Preparing the data
       3. Creating a simple data app
       4. Associative logic
       5. Creating charts
       6. Analyzing your data
       7. Further learning
       8. Summary
   12. 5. Clustering and Other Unsupervised Learning Methods
       1. Machine learning – unsupervised and supervised learning
          1. Cluster analysis
             1. Centroid-based clustering the using K-means algorithm
             2. Customer segmentation with K-means clustering
                1. Preparing the data in Qlik Sense
             3. Creating a customer segmentation sheet in Qlik Sense
          2. Hierarchical clustering
          3. Association analysis
       2. Further learning
       3. Summary
   13. 6. Decision Trees and Other Supervised Learning Methods
       1. Partitioning datasets and model optimization
       2. Decision Tree Learning
       3. Entropy and information gain
       4. Underfitting and overfitting
       5. Using a Decision Tree to classify credit risks
          1. Using Rattle to score new loan applications
          2. Creating a Qlik Sense application to predict credit risks
       6. Ensemble classifiers
          1. Boosting
          2. Random Forest
          3. Supported Vector Machines
       7. Other models
          1. Linear and Logistic Regression
          2. Neural Networks
       8. Further learning
       9. Summary
   14. 7. Model Evaluation
       1. Cross-validation
       2. Regression performance
          1. Predicted versus Observed Plot
       3. Measuring the performance of classifiers
          1. Confusion matrix, accuracy, sensitivity, and specificity
          2. Risk Chart
          3. ROC Curve
       4. Further learning
       5. Summary
   15. 8. Visualizations, Data Applications, Dashboards, and Data Storytelling
       1. Data visualization in Qlik Sense
          1. Visualization toolbox
          2. Creating a bar chart
             1. The Data menu
             2. The Sorting menu
             3. The Add-ons menu
             4. The Appearance menu
       2. Data analysis, data applications, and dashboards
          1. Qlik Sense data analysis
             1. In-memory analysis
             2. Associative experience
          2. Data applications and dashboards
             1. The DAR approach
       3. Data storytelling with Qlik Sense
          1. Creating a new story
       4. Further learning
       5. Summary
   16. 9. Developing a Complete Application
       1. Understanding the bike rental problem
       2. Exploring the data with Qlik Sense
          1. Checking for temporal patterns
          2. Visual correlation analysis
       3. Creating a Qlik Sense App to control the activity
       4. Using Rattle to forecast the demand
          1. Correlation Analysis with Rattle
          2. Building a model
          3. Improving performance
       5. Model evaluation
          1. Scoring new data
       6. Further learning
       7. Summary
   17. Index