Developed an end-to-end financial sentiment analysis system by fine-tuning transformer-based Large Language Models (LLMs) on financial news datasets. The project focuses on extracting sentiment signals from financial news, earnings reports, and market commentary to support data-driven investment and business intelligence decisions. Key Contributions: • Fine-tuned BERT-based transformer models using Hugging Face Transformers and PyTorch for domain-specific financial sentiment classification. • Built a full NLP pipeline including data preprocessing, tokenization, feature engineering, and model training on financial news datasets. • Implemented training optimization techniques including learning rate scheduling, early stopping, and class balancing. • Evaluated model performance using multiple NLP metrics including Accuracy, Precision, Recall, F1-score, BLEU, and ROUGE. • Achieved 92% sentiment classification accuracy on financial news datasets. • Conducted bias and model robustness checks to ensure reliable predictions across different financial topics. • Designed the solution to simulate real-world financial intelligence use cases such as market trend detection, investment sentiment tracking, and risk monitoring. Tech Stack: Python, PyTorch, Hugging Face Transformers, BERT, NLP, Pandas, Scikit-learn, Matplotlib Business Value: Demonstrates how fine-tuned LLMs can extract actionable insights from unstructured financial text to support investment analysis, trading signals, and enterprise financial intelligence systems.