AI Data Trainer & Machine Learning Practitioner (Personal ML Project)
Participated in end-to-end machine learning projects focused on high-quality data labeling, model evaluation, and documenting feedback in Python-based environments. Processed and evaluated predictive models for structured energy consumption datasets, emphasizing quality and methodological accuracy. Demonstrated applied data annotation and feedback skills, enabling improvements in model performance and dataset reliability. • Completed supervised model training and structured pipeline documentation. • Conducted data preprocessing, feature engineering, and annotation quality checks. • Used cross-validation metrics and feedback loops for continuous quality improvement. • Publicly documented results and methodology to ensure reproducibility and transparency.