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Data Science, Analytics and Machine Learning with R
Offers a practical R-based toolkit for data analysis using different machine learning techniques
Luiz Paulo Favero (Author), Patricia Belfiore (Author), Rafael de Freitas Souza (Author)
9780128242711, Elsevier Science
Paperback, published 25 January 2023
660 pages, 400 illustrations (200 in full color)
27.6 x 21.6 x 4 cm, 1.77 kg
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.
Part I: Introduction
1. Overview of Data Science, Analytics, and Machine Learning
2. Introduction to the R Language
Part II: Applied Statistics and Data Visualization
3. Variables and Measurement Scales
4. Descriptive and Probabilistic Statistics
5. Hypotheses Tests
6. Data Visualization and Multivariate Graphs
Part III: Data Mining and Preparation
7. Building Handcrafted Robots
8. Using APIs to Collect Data
9. Managing Data
Part IV: Unsupervised Machine Learning Techniques
10. Cluster Analysis
11. Factorial and Principal Component Analysis (PCA)
12. Association Rules and Correspondence Analysis
Part V: Supervised Machine Learning Techniques
13. Simple and Multiple Regression Analysis
14. Binary, Ordinal and Multinomial Regression Analysis
15. Count-Data and Zero-Inflated Regression Analysis
16. Generalized Linear Mixed Models
Part VI: Improving Performance and Introduction to Deep Learning
17. Support Vector Machine
18. CART (Classification and Regression Trees)
19. Bagging, Boosting and Uplift (Persuasion) Modeling
20. Random Forest
21. Artificial Neural Network
22. Introduction to Deep Learning
Part VII: Spatial Analysis
23. Working on Shapefiles
24. Dealing with Simple Features Objects
25. Raster Objects
26. Exploratory Spatial Analysis
Part VII: Adding Value to your Work
27. Enhanced and Interactive Graphs
28. Dashboards with R
Subject Areas: Machine learning [UYQM]
