Publisher's Synopsis
Dive into the world of data science with R in this hands-on guide designed to take you through the core concepts of data analysis and model building. Whether you're new to data science or seeking to expand your skills, this book provides a clear and practical approach to analyzing real-world datasets and creating data models using R.
Through step-by-step examples, you'll learn how to clean, analyze, visualize, and interpret data. From building basic statistical models to applying machine learning techniques, this book covers it all, making it perfect for beginners.
What you'll learn:
Setting up R and RStudio for data science projects
Importing and cleaning data with dplyr, tidyr, and other R packages
Visualizing data using ggplot2 and other plotting tools
Applying statistical methods to analyze data trends
Building linear regression and classification models
Introduction to machine learning in R with caret and randomForest
Evaluating model performance using cross-validation and confusion matrices
Handling missing data and outliers effectively
Making predictions and interpreting model results
Exporting results and creating reports with RMarkdown
By the end of this book, you'll be ready to tackle data analysis and build predictive models with R, equipping you with the practical skills to solve real-world problems in data science.
Perfect for beginners who want to get hands-on experience with data analysis and modeling in R.
