Overview

Before building predictive models, raw data must be cleaned, transformed, and prepared--a process known as data preprocessing. Effective preprocessing enhances the accuracy and reliability of machine learning algorithms. Statistics with R for Machine Learning: Data Preprocessing for Machine Learning using R provides an in-depth guide to statistical tools and techniques essential for preparing data. The book explains data normalization, missing value imputation, outlier detection, and feature engineering using R programming. It also introduces visualization tools and statistical validation methods. Practical examples and R scripts make it an ideal reference for students and data professionals.

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9781779569516

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Author Information

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.

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