High-dimensional NLP pipeline for imbalanced comment classification using TF-IDF, feature engineering, and LightGBM (Macro F1: 0.8344)
Benchmarking of MultiClass Classification models
Provides a unified approach to Integrated Gradients: a common API for many models, include tree-based models; and common baseline semantics with prediction-neutral baseline distributions as the default.
Automated classification of 7 different types of dry beans using machine learning techniques. This project leverages computer vision-extracted geometric and shape features (such as Area, Perimeter, and Shape Factors) to accurately identify bean varieties including Barbunya, Bombay, Cali, Dermason, Horoz, Seker, and Sira.
Academic Machine Learning (6 months) Sessional Project
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