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Data Science, Analytics and Machine Learning with R de Luiz Paulo Favero

Data Science, Analytics and Machine Learning with R

Autor Luiz Paulo Favero, Patricia Belfiore, Rafael de Freitas Souza
en Limba Engleză Paperback – 24 ian 2023
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
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Specificații

ISBN-13: 9780128242711
ISBN-10: 012824271X
Pagini: 660
Ilustrații: 400 illustrations (200 in full color)
Dimensiuni: 216 x 276 x 35 mm
Greutate: 1.51 kg
Editura: ELSEVIER SCIENCE

Public țintă

Data science and data engineering professionals, as well as researchers and engineers who need to ensure consistent, accurate and reliable data across their company, lab, hospital, etc.
Graduate students in data science, compute science, and big data

Cuprins

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