Personalized Financial Advice with Gemini 1.5 Pro

My friend Sarah recently came to me, frustrated. She felt lost in the world of investing. Apps offered generic advice and financial advisors seemed expensive. Her experience sparked an idea. What if we could leverage the power of Large Language Models (LLMs) like Google’s Gemini 1.5 Pro to create a personalized financial advisor? LLMs, with their ability to process vast amounts of information and generate human-like text, hold immense potential for revolutionizing how we approach financial planning. This tutorial dives into building a system that does just that, using Gemini 1.5 Pro to generate personalized financial advice tailored to individual circumstances and goals.

Google Gemini

Table of Contents

• Financial Data Acquisition and Preprocessing: Extracting, cleaning and transforming financial data from various sources using Pandas and NumPy.
• Prompt Engineering for Financial Advice: Crafting effective prompts for Gemini 1.5 Pro to generate personalized advice based on financial data and goals.
• Gemini 1.5 Pro Integration: Implementing the Gemini API in Python to interact with the model.
• Risk Assessment and Management: Building a risk profiling module to assess risk tolerance and adjust Gemini prompts.
• Performance Evaluation and Backtesting…