Structured Data Annotation and Cleaning for Algorithm Evaluation
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ClassificationI independently conducted structured data annotation and cleaning on a high-dimensional business dataset (DS2). This involved applying business logic to clean outliers and systematically annotating categorical features, producing a ground truth dataset for model training. I manually evaluated model outputs using both business interpretability and predictive accuracy, providing standardized human feedback. • Annotated and cleaned a large feature-target dataset to train and evaluate machine learning models • Conducted manual ratings and cross-comparisons to enhance model selection reliability • Reviewed and aligned Python and R algorithms for statistical consistency • Authored a comprehensive 2,500-word model evaluation report and insight summary.