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R machine learning projects provides hands-on tutorials and source code to help users implement real-world machine learning models using R. Covering essential concepts such as data preprocessing, feature engineering, model selection, and performance evaluation, it walks learners through step-by-step coding examples using popular R libraries like caret, randomForest, and xgboost.
From regression and classification to clustering and deep learning, this resource explores various machine learning techniques with practical applications.
Whether you're a beginner looking to build your first ML model or an experienced data scientist aiming to refine your skills, this guide equips you with the knowledge and tools to develop, train, and optimize machine learning models efficiently in R.