Ebook: Mastering predictive analytics with R: master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts

Cover -- Copyright -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Preface -- Chapter 1: Gearing Up for Predictive Modeling -- Models -- Learning from data -- The core components of a model -- Our first model: k-nearest neighbors -- Types of models -- Supervised, unsupervised, semi-supervised, and reinforcement learning models -- Parametric and nonparametric models -- Regression and classification models -- Real time and batch machine learning models -- The process of predictive modeling -- Defining the model's objective -- Collecting the data -- Picking a model -- Pre-processing the data -- Exploratory data analysis -- Feature transformations -- Encoding categorical features -- Missing data -- Outliers -- Removing problematic features -- Feature engineering and dimensionality reduction -- Training and assessing the model -- Repeating with different models and final model selection -- Deploying the model -- Performance metrics -- Assessing regression models -- Assessing classification models -- Assessing binary classification models -- Summary -- Chapter 2 : Linear Regression -- Linear regression -- Assumptions of linear regression -- Simple linear regression -- Estimating the regression coefficients -- Multiple linear regression -- Predicting CPU performance -- Predicting the price of used cars -- Assessing linear regression models -- Residual analysis -- Significance tests for linear regression -- Performance metrics for linear regression -- Comparing different regression models -- Test set performance -- Problems with linear regression -- Multicollinearity -- Outliers -- Feature selection -- Regularization -- Ridge regression -- Least absolute shrinkage and selection operator (lasso) -- Implementing regularization in R -- Summary -- Chapter 3 : Logistic Regression.;This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.