This repository serves as a platform for exploring stock price prediction methodologies using Python. In this project, we delve into the prediction of future prices for Microsoft stock, employing two distinct approaches: Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA).
Objective: Our primary objective is to compare the effectiveness of LSTM and SARIMA in predicting Microsoft stock prices. Methods: LSTM, a deep learning model known for capturing sequential patterns, and SARIMA, a classic time series analysis technique, are the two methodologies under scrutiny. Implementation: The repository provides dedicated folders for LSTM and SARIMA models, each containing relevant code and documentation. Data: Historical stock price data for Microsoft forms the basis for training and evaluation of the models.