Implementing Predictive Models and Machine Learning Techniques
Deepti Gupta

#SAS
#R
#Machine_Learning
#Algorithms
#Data_science
Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language.
This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms.
Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills.
What You'll Learn
Who This Book Is For
Data scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data
Table of Contents
Chapter 1: Data Analytics and Its Application in Various Industries
Chapter 2: Banking Case Study
Chapter 3: Retail Case Study
Chapter 4: Telecommunication Case Study
Chapter 5: Healthcare Case Study
Chapter 6: Airline Case Study
Chapter 7: FMCG Case Study
Deepti Gupta completed her MBA in Finance and PGPM in operation research in 2010. She has worked with KPMG and IBM private limited as Data Scientist and is currently working as a data science freelancer. Deepti has extensive experience in predictive modeling and machine learning with an expertise in SAS and R. Deepti has developed data science courses, delivered data science trainings, and conducted workshops for both corporate and academic institutions. She has written multiple blogs and white papers. Deepti has a passion for mentoring budding data scientists.









