I need to build a supervised-learning pipeline in R that takes raw data all the way to a trained, validated model I can use in production. The dataset and the business question are ready; what I’m missing is a well-structured workflow that delivers reliable predictions and clear insights. • Explore feature engineering opportunities and create training / test splits. • Evaluate several supervised algorithms in R (e.g., randomForest, xgboost, glmnet or any Tidymodels equivalents), applying cross-validation and hyper-parameter tuning. • Select the best model, document key metric RMSE, and provide concise interpretation of feature importance. • Supply fully commented R script or R Markdown so I can reproduce every step, plus a short report (PDF or HTML) summarising methodology, results, and next-step recommendations. What to include in your bid I’m most interested in your experience: briefly highlight past R machine-learning projects—especially supervised ones—and note the packages or frameworks you prefer. A compact portfolio or GitHub link is perfect; no full proposals required at this stage. I’ll share the dataset and additional context with the selected freelancer. Looking forward to collaborating on a robust, well-documented solution.