Data Analysis and Modelling with Machine Learning Techniques
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Data Analysis and Modelling with Machine Learning Techniques: A Step-By-Step Guide

Data Analysis and Modelling with Machine Learning Techniques: A Step-By-Step Guide


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About the Book

This book can best be described as the student's guide and the researcher's quick reference. It proposes that students, researchers, and practitioners adopt sound statistical practices in research data analysis and modeling, a concern frequently expressed by reviewers and thesis supervisors. It presents eight chapters cutting across common research design and problem areas, including software applications, parametric and nonparametric inferential methods, various data modelling methods, and machine learning techniques. Chapter 1 provides step-by-step guides to using three software applications – SPSS, R and SmartPLS – for data analysis and modelling, allowing for flexibility in choosing software applications. Chapters 2 and 3 lead the way to generating common research data along with various data diagnostic practices, data summaries, and data visualization techniques. Chapter 4 provides illustrations in applying parametric and nonparametric methods. Chapters 5 and 6 detail robust practices in data modelling. Chapter 7 deploys common machine learning techniques to validate models proposed in Chapter 6, while Chapter 8 provides a guide in achieving the foremost goal in time series analysis, along with an automated ARIMA (Autoregressive Integrated Moving Average) procedure.

About the Author :
Prof John Coker Ayimah is an Associate Professor in the Department of Mathematics and Statistics, Ho Technical University (HTU) (Ghana), with over nineteen years of teaching experience at the tertiary level, teaching courses such as multivariate statistics, regression and model building, statistical computing, and research methods at both undergraduate and graduate level. With expertise in applied statistics – particularly in multivariate analysis – and a strong mathematics background, Prof Ayimah (PhD) co-authored a number of articles in food safety and hygiene, digitized financial service adoption, innovation and self-service technology adoption, occupational hazard assessment, health and safety performance, access to personal protective equipment, gender and career path association with Covid-19, Covid-19 endpoint prediction, and solutions to non-linear partial differential equations applicable in computer algebraic systems. Prof Ayimah also serves as reviewer for top publishing outlets including the African Journal of Economic and Management Studies (AJEMS).

Review :
'Researchers, students, and practitioners alike will find in these pages both a reference and a guidebook—a resource that builds strong foundations while also encouraging curiosity and innovation in data science. I believe this book will serve as a valuable companion to anyone seeking to master data analysis and modelling, and to understand the transformative role of machine learning in our world.'Professor Ezekiel Nii Noye Nortey,Associate Professor, Department of Statistics and Actuarial Science, University of Ghana-Legon


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Product Details
  • ISBN-13: 9781036476786
  • Publisher: Cambridge Scholars Publishing
  • Publisher Imprint: Cambridge Scholars Publishing
  • Edition: Unabridged edition
  • Language: English
  • Returnable: N
  • Width: 148 mm
  • ISBN-10: 1036476782
  • Publisher Date: 01 Sep 2026
  • Binding: Paperback
  • Height: 212 mm
  • No of Pages: 248
  • Sub Title: A Step-By-Step Guide


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Data Analysis and Modelling with Machine Learning Techniques: A Step-By-Step Guide
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Data Analysis and Modelling with Machine Learning Techniques: A Step-By-Step Guide
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