Stock Market Analysis using Long Short-Term Memory(LSTM) and linear regression Machine Learning Model

This research delves into the intricate world of stock market dynamics, where prices are in a constant state of flux due to a multitude of influential factors, including news events, a company’s financial performance, economic policies specific to geographic regions, and investor sentiments. Predicting stock prices in such a volatile environment poses a formidable challenge, prompting the adoption of Machine Learning techniques to streamline this task, reduce human effort, and minimize errors. In this study, the author leverages a data-driven approach to forecast stock prices. The primary focus is on evaluating the efficacy of two distinct methodologies: the Long Short-Term Memory (LSTM) architecture and linear regression. These models are designed to utilize historical data and associated influencing factors to make predictions about future stock prices. It addresses the challenge of forecasting stock prices in a dynamic market influenced by diverse factors. The study employs a data-driven approach, compares the performance of LSTM and linear regression models, and evaluates their effectiveness in relation to existing research. The findings provide insights into the feasibility of using these techniques in stock market forecasting.