Data preparation is the foundation of any successful machine learning project. This volume provides a comprehensive guide to cleaning, transforming, and splitting data for machine learning using R, including handling missing values, feature scaling, and stratified sampling. Practical examples and R code demonstrate how to optimize datasets for predictive modeling. The volume is essential for data scientists and machine learning practitioners seeking to build robust models.
Mohsen Nady is a pharmacist with a M.D. in Microbiology and a Diploma in Industrial Pharmacy. Besides, Mohsen has more than 10 years of experience in Statistics and Data Analytics. Mohsen has applied his skills to different projects related to Genomics, Microbiology, Biostatistics, Six Sigma, Data Analytics, Data Visualization, Building Apps, Geography, Market Analysis, Business Analysis, Machine Learning, etc. Mohsen also published his thesis in a high-impact journal that attracted many citations, where all the statistical analyses were performed by him in addition to the methodological part. Furthermore, Mohsen has earned different certificates, from top universities (Harvard, Johns Hopkins, Denmark, etc) in Statistics, Data Analytics, Data Visualization, and Machine Learning that highlight his outstanding diverse skills.
