Delve into data preprocessing as the basis for predictive modeling. Learn how to normalize data, impute missing values, detect outliers, and engineer features using R. Practical examples, R scripts, and visualization tools guide students and professionals in crafting reliable machine learning models.
Before building predictive models, raw data must be cleaned, transformed, and prepared—a process known as data preprocessing. Effective preprocessing enhances the accuracy and reliability of machine learning algorithms. Statistics with R for Machine Learning: Data Preprocessing for Machine Learning using R provides an in-depth guide to statistical tools and techniques essential for preparing data. The book explains data normalization, missing value imputation, outlier detection, and feature engineering using R programming. It also introduces visualization tools and statistical validation methods. Practical examples and R scripts make it an ideal reference for students and data professionals.
