Sentiment Analysis with SetFit on SST‑5

In this project, I leveraged SetFit, Hugging Face’s prompt‑free few‑shot classification framework, to tackle the challenging SST‑5 task—fine-grained sentiment analysis across five classes (very negative to very positive) What makes SetFit so compelling is its efficiency and simplicity: This project highlights how evolving NLP techniques—from static embeddings to contextual transformers—impact performance and interpretability. Fine-tuned transformer

Applying supervised learning to predict student dropout

Student retention is critical for educational institutions, impacting financial sustainability andacademic success. High dropout rates can lead to revenue losses and reputational damage.Study Group, a global education provider, aims to enhance student success by identifying atrisk students early and implementing proactive interventions. This study applies supervisedmachine learning techniques to predict dropout risks, enabling Study Group