• Machine Learning Project for personalized investment portfolios based on user risk profiles, assessed through a straightforward questionnaire. • Developed a LLAMA-2-based Large Language Model (LLM), fine-tuned with financial data including reports and articles, to generate user-specific risk-based asset allocations based on their questionnaire responses.. markets • Further Reinforced Learning, DQN is used to study the historical data of assets and further select the best ones based on the risk appetite Tech: Java, Python, MySQL, LLama-2, QLora