I offer end-to-end machine learning development, from feature engineering to model optimization. With published research in ML (IEEEAccess & IEEE Xplore) and hands-on experience building models for real-world datasets like the Bosch production line, crop yield prediction, and OCR systems, I create reliable, data-driven solutions tailored to your requirements.
Expertise includes:
Feature engineering & dataset preparation
Model development (Random Forest, XGBoost, CatBoost, CNNs, SVM, etc.)
Hyperparameter tuning using Optuna
Performance evaluation with metrics such as F1, MCC, RMSE, R²
Model comparison, debugging, and improvement
Research-style experimentation & documentation
I bring a research-driven, detail-oriented approach and ensure models are well-tested, well-structured, and ready for deployment or further analysis.