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AI Stock Price Prediction Using Large Language Models in Python

This tutorial demonstrates how to build a combined AI and machine learning pipeline to predict stock prices with just a laptop. I use Meta Llama 3, an advanced open source large language model (LLM), with a local Ollama server on my laptop to run a sentiment analysis of recent financial news headlines scraped from the web using a Python API library. This sentiment data is then used to create a measure to predict stock prices using time series forecasting methods. The analysis is run through an interactive Streamlit web app, making it user friendly. The goal of this tutorial is to show you the process of how to use AI transformer neural network models to gain insight into stock prices with a Python code workflow. A more comprehensive analysis would include additional time series training data for stock prices and news headlines, a deep dive into the accuracy of the Llama 3 model on classifying sentiment, the addition of other relevant features for predicting stock price movement, and a more deliberate decision for the ML modeling method. ***Important Note: This video is not financial or investing advice. It is an educational tutorial on how to use AI models within a machine learning pipeline.** Like, Comment, and Subscribe to the Deep Charts Channel for more informative AI and Machine Learning tutorials. Full Code: https://github.com/deepcharts/project... *Data Science Resources* Ollama: https://ollama.com/ Streamlit: https://streamlit.io/