Stock Price Prediction Using Machine Learning

Abstract: Stock price prediction is one of the most challenging tasks in financial analysis due to the market's highly volatile and nonlinear nature. In this study, we propose a machine learning-based approach for predicting stock prices using historical data. The model utilizes regression-based and deep learning algorithms to capture temporal patterns and market trends. Specifically, we employ Linear Regression, Random Forest, and a Long Short-Term Memory (LSTM) network to model the time-series behavior of stock prices. The LSTM model is particularly suited for this task as its architecture allows it to effectively learn and remember long-term dependencies inherent in sequential financial data. The project aims to assist investors and analysts in making informed decisions by forecasting future stock prices with reasonable accuracy. The performance of all models is rigorously evaluated using standard metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R²-score, on a held-out test set. The results demonstrate that the Long Short-Term Memory (LSTM) network significantly outperforms traditional regression models in capturing sequential dependencies in stock market data, achieving the highest R² score of 0.93 and the lowest RMSE of 0.018. This robust performance underscores the suitability of deep learning for complex financial forecasting.

Keywords: Stock prediction, Sequential Financial Data, Temporal patterns, Forecasting, Deep learning, LSTM.