R for Data Science: Implementing Machine Learning Models

Bys: Yaya, Olaoluwa S. and Adepoju, Mary M.

Unlock the power of machine learning in R with R for Data Science: Implementing Machine Learning Models. This comprehensive guide equips data scientists, analysts, and R enthusiasts with the practical skills needed to build, evaluate, and deploy advanced machine learning solutions across domains. Covering both fundamental and advanced topics, this book blends theory, hands-on examples, and real-world workflows to empower readers to harness R's full capabilities.Learn how to: - Preprocess, clean, and transform data for robust analysis.- Build predictive models with regression, classification, and time series techniques.- Apply natural language processing and text analytics to extract insights from unstructured data.- Explore clustering, dimensionality reduction, and anomaly detection in unsupervised learning.- Optimize models through hyperparameter tuning, ensemble methods, and stacking strategies.- Develop reproducible workflows, pipelines, and deployment-ready solutions in R.

• Format: Paperback
• Language: English
• ISBN: 6209540546
• ISBN13: 9786209540547
• Release Date: February 2026
• Publisher: LAP Lambert Academic Publishing
• Length: 164 Pages
• Weight: 0.50 lbs.
• Dimensions: 0.4" x 6.0" x 9.0"