Applied Supervised Learning with R: Use machine learning libraries of R to build models that solve business problems and predict future trends

Description

Learn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction. Key Features Study supervised learning algorithms by using real-world datasets Fine tune optimal parameters with hyperparameter optimization Select the best algorithm using the model evaluation framework Book Description R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. To prevent you from overfitting your model, a dedicated section will even demonstrate how you can add various regularization terms. By the end of this book, you will have the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs. What you will learn Develop analytical thinking to precisely identify a business problem Wrangle data with dplyr, tidyr, and reshape2 Visualize data with ggplot2 Validate your supervised machine learning model using k-fold Optimize hyperparameters with grid and random search, and Bayesian optimization Deploy your model on Amazon Web Services (AWS) Lambda with plumber Improve your model's performance with feature selection and dimensionality reduction Who this book is for This book is specially designed for novice and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book. Table of Contents R for Advanced Analytics Exploratory Analysis of Data Introduction to Supervised Learning Regression Classification Feature Selection and Dimensionality Reduction Model Improvements Model Deployment Capstone Project - Based on Research Papers 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 R Vector 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 Join Left 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 tidyr The 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 Features Exercise 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 Exercise 27: Automate Plotting Categorical Variables Exercise 28: Automate Plotting for the Remaining Categorical Variables Exercise 29: Exploring the Last Remaining Categorical Variable and the Target Variable Bivariate Analysis Studying the Relationship between Two Numeric Variables Exercise 30: Studying the Relationship between Employee Variance Rate and Number of Employees Studying the Relationship between a Categorical and a Numeric Variable Exercise 31: Studying the Relationship between the y and age Variables Exercise 32: Studying the Relationship between the Average Value and the y Variable Exercise 33: Studying the Relationship between the cons.price.idx, cons.conf.idx, curibor3m, and nr.employed Variables Studying the Relationship Between Two Categorical Variables Exercise 34: Studying the Relationship Between the Target y and marital status Variables Exercise 35: Studying the Relationship between the job and education Variables Multivariate Analysis Validating Insights Using Statistical Tests Categorical Dependent and Numeric/Continuous Independent Variables Exercise 36: Hypothesis 1 Testing for Categorical Dependent Variables and Continuous Independent Variables Exercise 37: Hypothesis 2 Testing for Categorical Dependent Variables and Continuous Independent Variables Categorical Dependent and Categorical Independent Variables Exercise 38: Hypothesis 3 Testing for Categorical Dependent Variables and Categorical Independent Variables Exercise 39: Hypothesis 4 and 5 Testing for a Categorical Dependent Variable and a Categorical Independent Variable Collating Insights – Refine the Solution to the Problem Summary Chapter 3: Introduction to Supervised Learning Introduction Summary of the Beijing PM2.5 Dataset Exercise 40: Exploring the Data Regression and Classification Problems Machine Learning Workflow Design the Problem Source and Prepare Data Code the Model Train and Evaluate Exercise 41: Creating a Train-and-Test Dataset Randomly Generated by the Beijing PM2.5 Dataset Deploy the Model Regression Simple and Multiple Linear Regression Assumptions in Linear Regression Models Exploratory Data Analysis (EDA) Exercise 42: Exploring the Time Series Views of PM2.5, DEWP, TEMP, and PRES variables of the Beijing PM2.5 Dataset Exercise 43: Undertaking Correlation Analysis Exercise 44: Drawing a Scatterplot to Explore the Relationship between PM2.5 Levels and Other Factors Activity 5: Draw a Scatterplot between PRES and PM2.5 Split by Months Model Building Exercise 45: Exploring Simple and Multiple Regression Models Model Interpretation Classification Logistic Regression A Brief Introduction Mechanics of Logistic Regression Model Building Exercise 46: Storing the Rolling 3-Hour Average in the Beijing PM2.5 Dataset Activity 6: Transforming Variables and Deriving New Variables to Build a Model Interpreting a Model Evaluation Metrics Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) R-squared Adjusted R-square Mean Reciprocal Rank (MRR) Exercise 47: Finding Evaluation Metrics Confusion Matrix-Based Metrics Accuracy Sensitivity Specificity F1 Score Exercise 48: Working with Model Evaluation on Training Data Receiver Operating Characteristic (ROC) Curve Exercise 49: Creating an ROC Curve Summary Chapter 4: Regression Introduction Linear Regression Exercise 50: Print the Coefficient and Residual Values Using the multiple_PM_25_linear_model Object Activity 7: Printing Various Attributes Using Model Object Without Using the Summary Function Exercise 51: Add the Interaction Term DEWP:TEMP:month in the lm() Function Model Diagnostics Exercise 52: Generating and Fitting Models Using the Linear and Quadratic Equations Residual versus Fitted Plot Normal Q-Q Plot Scale-Location Plot Residual versus Leverage Improving the Model Transform the Predictor or Target Variable Choose a Non-Linear Model Remove an Outlier or Influential Point Adding the Interaction Effect Quantile Regression Exercise 53: Fit a Quantile Regression on the Beijing PM2.5 Dataset Exercise 54: Plotting Various Quantiles with More Granularity Polynomial Regression Exercise 55: Performing Uniform Distribution Using the runif() Function Ridge Regression Regularization Term – L2 Norm Exercise 56: Ridge Regression on the Beijing PM2.5 dataset LASSO Regression Exercise 57: LASSO Regression Elastic Net Regression Exercise 58: Elastic Net Regression Comparison between Coefficients and Residual Standard Error Exercise 59: Computing the RSE of Linear, Ridge, LASSO, and Elastic Net Regressions Poisson Regression Exercise 60: Performing Poisson Regression Exercise 61: Computing Overdispersion Cox Proportional-Hazards Regression Model NCCTG Lung Cancer Data Exercise 62: Exploring the NCCTG Lung Cancer Data Using Cox-Regression Summary Chapter 5: Classification Introduction Getting Started with the Use Case Some Background on the Use Case Defining the Problem Statement Data Gathering Exercise 63: Exploring Data for the Use Case Exercise 64: Calculating the Null Value Percentage in All Columns Exercise 65: Removing Null Values from the Dataset Exercise 66: Engineer Time-Based Features from the Date Variable Exercise 67: Exploring the Location Frequency Exercise 68: Engineering the New Location with Reduced Levels Classification Techniques for Supervised Learning Logistic Regression How Does Logistic Regression Work? Exercise 69: Build a Logistic Regression Model Interpreting the Results of Logistic Regression Evaluating Classification Models Confusion Matrix and Its Derived Metrics What Metric Should You Choose? Evaluating Logistic Regression Exercise 70: Evaluate a Logistic Regression Model Exercise 71: Develop a Logistic Regression Model with All of the Independent Variables Available in Our Use Case Activity 8: Building a Logistic Regression Model with Additional Features Decision Trees How Do Decision Trees Work? Exercise 72: Create a Decision Tree Model in R Activity 9: Create a Decision Tree Model with Additional Control Parameters Ensemble Modelling Random Forest Why Are Ensemble Models Used? Bagging – Predecessor to Random Forest How Does Random Forest Work? Exercise 73: Building a Random Forest Model in R Activity 10: Build a Random Forest Model with a Greater Number of Trees XGBoost How Does the Boosting Process Work? What Are Some Popular Boosting Techniques? How Does XGBoost Work? Implementing XGBoost in R Exercise 74: Building an XGBoost Model in R Exercise 75: Improving the XGBoost Model's Performance Deep Neural Networks A Deeper Look into Deep Neural Networks How Does the Deep Learning Model Work? What Framework Do We Use for Deep Learning Models? Building a Deep Neural Network in Keras Exercise 76: Build a Deep Neural Network in R using R Keras Choosing the Right Model for Your Use Case Summary Chapter 6: Feature Selection and Dimensionality Reduction Introduction Feature Engineering Discretization Exercise 77: Performing Binary Discretization Multi-Category Discretization Exercise 78: Demonstrating the Use of Quantile Function One-Hot Encoding Exercise 79: Using One-Hot Encoding Activity 11: Converting the CBWD Feature of the Beijing PM2.5 Dataset into One-Hot Encoded Columns Log Transformation Exercise 80: Performing Log Transformation Feature Selection Univariate Feature Selection Exercise 81: Exploring Chi-Squared Highly Correlated Variables Exercise 82: Plotting a Correlated Matrix Model-Based Feature Importance Ranking Exercise 83: Exploring RFE Using RF Exercise 84: Exploring the Variable Importance using the Random Forest Model Feature Reduction Principal Component Analysis (PCA) Exercise 85: Performing PCA Variable Clustering Exercise 86: Using Variable Clustering Linear Discriminant Analysis for Feature Reduction Exercise 87: Exploring LDA Summary Chapter 7: Model Improvements Introduction Bias-Variance Trade-off What is Bias and Variance in Machine Learning Models? Underfitting and Overfitting Defining a Sample Use Case Exercise 88: Loading and Exploring Data Cross-Validation Holdout Approach/Validation Exercise 89: Performing Model Assessment Using Holdout Validation K-Fold Cross-Validation Exercise 90: Performing Model Assessment Using K-Fold Cross-Validation Hold-One-Out Validation Exercise 91: Performing Model Assessment Using Hold-One-Out Validation Hyperparameter Optimization Grid Search Optimization Exercise 92: Performing Grid Search Optimization – Random Forest Exercise 93: Grid Search Optimization – XGBoost Random Search Optimization Exercise 94: Using Random Search Optimization on a Random Forest Model Exercise 95: Random Search Optimization – XGBoost Bayesian Optimization Exercise 96: Performing Bayesian Optimization on the Random Forest Model Exercise 97: Performing Bayesian Optimization using XGBoost Activity 12: Performing Repeated K-Fold Cross Validation and Grid Search Optimization Summary Chapter 8: Model Deployment Introduction What is an API? Introduction to plumber Exercise 98: Developing an ML Model and Deploying It as a Web Service Using Plumber Challenges in Deploying Models with plumber A Brief History of the Pre-Docker Era Docker Deploying the ML Model Using Docker and plumber Exercise 99: Create a Docker Container for the R plumber Application Disadvantages of Using plumber to Deploy R Models Amazon Web Services Introducing AWS SageMaker Deploying an ML Model Endpoint Using SageMaker Exercise 100: Deploy the ML Model as a SageMaker Endpoint What is Amazon Lambda? What is Amazon API Gateway? Building Serverless ML Applications Exercise 101: Building a Serverless Application Using API Gateway, AWS Lambda, and SageMaker Deleting All Cloud Resources to Stop Billing Activity 13: Deploy an R Model Using plumber Summary Chapter 9: Capstone Project - Based on Research Papers Introduction Exploring Research Work The mlr Package OpenML Package Problem Design from the Research Paper Features in Scene Dataset Implementing Multilabel Classifier Using the mlr and OpenML Packages Exercise 102: Downloading the Scene Dataset from OpenML Constructing a Learner Adaptation Methods Transformation Methods Binary Relevance Method Classifier Chains Method Nested Stacking Dependent Binary Relevance Method Stacking Exercise 103: Generating Decision Tree Model Using the classif.rpart Method Train the Model Exercise 104: Train the Model Predicting the Output Performance of the Model Resampling the Data Binary Performance for Each Label Benchmarking Model Conducting Benchmark Experiments Exercise 105: Exploring How to Conduct a Benchmarking on Various Learners Accessing Benchmark Results Learner Performances Predictions Learners and measures Activity 14: Getting the Binary Performance Step with classif.C50 Learner Instead of classif.rpart Working with OpenML Upload Functions Summary Appendix Index