Programming in R with Applications in Machine Learning and Data Analytics
Programming in R with Applications in Machine Learning and Data Analytics
₹535.50 ₹595.00 Save: ₹59.50 (10%)
Go to cartISBN: 9789395654296
Bind: Paperback
Year: 2023
Pages: 168
Size: 6 x 9 Inch
Publisher: Viva Books Originals
Sales Territory: Worldwide
Description:
Designed for self-study, this book follows a logical progression from basic concepts to more advanced topics. It can serve as an introduction to programming and introductory R for beginners with no previous experience, or a self-study guide for those with some programming background. The book is also ideal for gaining practical, real-world programming skills that can be applied to data analysis using R.
Target Audience:
Useful for all computer science students.
It serves as an introduction to programming and introductory R for beginners with no previous experience, or a self-study guide for those with some programming background. If you are a programmer, data analyst, or someone with a quantitative background interested in machine learning and data analytics, this book is for you. The book is perfect for all data science aspirants who want to leverage the power of R for data analytics.
Contents:
Preface
Chapter 1. Introduction to R • Features of R • Evolution of R • R Studio Installation Guide • R Environment/R IDE • R Packages • Summary • References • Exercises
Chapter 2. Basics of R Programming • Basic Syntax • Variables • Data Types • Operators • Decision-making Statement • Looping Statements • Functions • String • Summary • References • Exercises
Chapter 3. Data Structures in R • Vectors • List • Data Frame • Factor • Array • Summary • References • Exercises
Chapter 4. Working with Data in R • Directory and File Handling • Handling .CSV File • Handling .XLS File • Handling .XML File • Handling .JSON File • R Database • Summary • References • Exercises
Chapter 5. Data Visualization and Graphical Analysis • R Packages for Visualization • Graphics in R • Pie Chart • Bar Chart • Boxplot • Histogram • Line Graph • Scatter Plot • Summary • References • Exercises
Chapter 6. Statistical Analysis using R • Concepts of Mean, Median and Mode • Variance • Standard Deviation • Min Function • Max Function • Chi-square Test • Anova • Covariance • Binomial Distribution • Normal Distribution • Summary • References • Exercises
Chapter 7. Machine Learning using R • Machine Learning • Types of Machine Learning • Building Machine Learning Model • R Classification • Decision Tree • Naïve Bayes Classifier • K-NN Classifier • Clustering • K-means Clustering • Support Vector Machine • Summary • References • Exercises
Chapter 8. Trend Analysis using R • Regression using R • Linear Regression • Sample Data • lm() Function • Multiple Linear Regression • Time Series Analysis • Summary • References • Exercises
Chapter 9. Soft Computing with R • Neural Network • Fuzzy Logic • Genetic Algorithm • Summary • References • Exercises
About the Authors
About the Authors:
Priyanka P. Shinde is working as Assistant Professor at the Government College of Engineering, Karad
Varsha P. Desai is working as Assistant Professor at the V. P. Institute of Management Studies and Research, Sangli.
Kavita S. Oza has been working in machine learning applications for over a decade and is a life member of the Computer Society of India.
Rajanish K. Kamat is Vice Chancellor of Dr Homi Bhabha State University, Mumbai.
