Data Science, Analytics and Machine Learning with R

Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear. Presents a comprehensive and practical overview of machine learning, data mining and AI techniques for a broad multidisciplinary audience Serves readers who are interested in statistics, analytics and modeling, and those who wish to deepen their knowledge in programming through the use of R Teaches readers how to apply machine learning techniques to a wide range of data and subject areas Presents data in a graphically appealing way, promoting greater information transparency and interactive learning INDICE: Part I: Introduction1. Overview of Data Science, Analytics, and Machine Learning2. Introduction to the R LanguagePart II: Applied Statistics and Data Visualization3. Variables and Measurement Scales4. Descriptive and Probabilistic Statistics5. Hypotheses Tests6. Data Visualization and Multivariate GraphsPart III: Data Mining and Preparation7. Building Handcrafted Robots8. Using APIs to Collect Data9. Managing DataPart IV: Unsupervised Machine Learning Techniques10. Cluster Analysis11. Factorial and Principal Component Analysis (PCA)12. Association Rules and Correspondence AnalysisPart V: Supervised Machine Learning Techniques13. Simple and Multiple Regression Analysis14. Binary, Ordinal and Multinomial Regression Analysis15. Count-Data and Zero-Inflated Regression Analysis16. Generalized Linear Mixed ModelsPart VI: Improving Performance and Introduction to Deep Learning17. Support Vector Machine18. CART (Classification and Regression Trees)19. Bagging, Boosting and Uplift (Persuasion) Modeling20. Random Forest21. Artificial Neural Network22. Introduction to Deep LearningPart VII: Spatial Analysis23. Working on Shapefiles24. Dealing with Simple Features Objects25. Raster Objects26. Exploratory Spatial AnalysisPart VII: Adding Value to your Work27. Enhanced and Interactive Graphs28. Dashboards with R

• ISBN: 9780128242711
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 720
• Fecha Publicación: 01/07/2022
• Nº Volúmenes: 1
• Idioma: Inglés