Predicting Stock Market Movements with Linear Regression and LSTM Machine Learning Model

The prediction of stock prices remains a perpetual challenge and opportunity in the domain of finance and investment. In this study, we delve into the realm of machine learning, employing both Linear Regression and Long Short-Term Memory (LSTM) models, to tackle the intricate task of stock price forecasting. Our research centers on two datasets: Google (GOOGL) and Tesla (TSLA), sourced from Kaggle, with the aim of providing a comprehensive evaluation of model performance and gaining insights into their practical utility. Our findings reveal variations in model performance across the datasets. LSTM models excel in capturing intricate temporal patterns, yielding remarkably low MSE and MAE values. With R-squared values close to unity, LSTM models exhibit a strong ability to explain variance in stock prices. These results underscore the potential of deep learning, particularly LSTM, to enhance stock price prediction accuracy. The implications extend to investment strategies and technical analysis. This research contributes to the ongoing discussion on stock price prediction, offering insights into the efficacy of machine learning models and their relevance in financial decision-making.