Applied Supervised Learning with R : Use Machine Learning Libraries of R to Build Models That Solve Business Problems and Predict Future Trends.

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Cover; FM; Table of Contents; Preface; Chapter 1: R for Advanced Analytics; Introduction; Working with Real-World Datasets; Exercise 1: Using the unzip Method for Unzipping a Downloaded File; Reading Data from Various Data Formats; CSV Files; Exercise 2: Reading a CSV File and Summarizing its Column; JSON; Exercise 3: Reading a JSON file and Storing the Data in DataFrame; Text; Exercise 4: Reading a CSV File with Text Column and Storing the Data in VCorpus; Write R Markdown Files for Code Reproducibility; Activity 1: Create an R Markdown File to Read a CSV File and Write a Summary of Data

Data Structures in RVector; Matrix; Exercise 5: Performing Transformation on the Data to Make it Available for the Analysis; List; Exercise 6: Using the List Method for Storing Integers and Characters Together; Activity 2: Create a List of Two Matrices and Access the Values; DataFrame; Exercise 7: Performing Integrity Checks Using DataFrame; Data Table; Exercise 8: Exploring the File Read Operation; Data Processing and Transformation; cbind; Exercise 9: Exploring the cbind Function; rbind; Exercise 10: Exploring the rbind Function; The merge Function; Exercise 11: Exploring the merge Function

Inner JoinLeft Join; Right Join; Full Join; The reshape Function; Exercise 12: Exploring the reshape Function; The aggregate Function; The Apply Family of Functions; The apply Function; Exercise 13: Implementing the apply Function; The lapply Function; Exercise 14: Implementing the lapply Function; The sapply Function; The tapply Function; Useful Packages; The dplyr Package; Exercise 15: Implementing the dplyr Package; The tidyr Package; Exercise 16: Implementing the tidyr Package

Activity 3: Create a DataFrame with Five Summary Statistics for All Numeric Variables from Bank Data Using dplyr and tidyrThe plyr Package; Exercise 17: Exploring the plyr Package; The caret Package; Data Visualization; Scatterplot; Scatter Plot between Age and Balance split by Marital Status; Line Charts; Histogram; Boxplot; Summary; Chapter 2: Exploratory Analysis of Data; Introduction; Defining the Problem Statement; Problem-Designing Artifacts; Understanding the Science Behind EDA; Exploratory Data Analysis; Exercise 18: Studying the Data Dimensions; Univariate Analysis

Exploring Numeric/Continuous FeaturesExercise 19: Visualizing Data Using a Box Plot; Exercise 20: Visualizing Data Using a Histogram; Exercise 21: Visualizing Data Using a Density Plot; Exercise 22: Visualizing Multiple Variables Using a Histogram; Activity 4: Plotting Multiple Density Plots and Boxplots; Exercise 23: Plotting a Histogram for the nr.employed, euribor3m, cons.conf.idx, and duration Variables; Exploring Categorical Features; Exercise 24: Exploring Categorical Features; Exercise 25: Exploring Categorical Features Using a Bar Chart

Exercise 26: Exploring Categorical Features using Pie Chart

Applied Supervised Learning with R will make you a pro at identifying your business problem, selecting the best supervised machine learning algorithm to solve it, and fine-tuning your model to exactly deliver your needs without overfitting itself.

Includes bibliographical references.

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