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Enhance your machine learning projects by mastering essential data preparation techniques in R. Learn to clean, transform, and split datasets effectively while addressing missing values, scaling features, and performing stratified sampling. Practical R code examples empower data scientists to build robust models.
List of contents
- Chapter 1 Introduction
- Chapter 2 Clean the Raw Data
- Chapter 3 Splitting Data
About the author
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.
