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Stock market prediction remains a complex and dynamic challenge due to its vast dimensionality and intricate nature. This study focuses on the development of a predictive large action model using historical data for stock market analysis. Publicly accessible platforms such as Yahoo Finance were utilized to collect baseline historical data, while the python library, pandas-ta, was leveraged for computation of various technical indicators including variants of momentum oscillators, bollinger bands, and moving averages. The processed data was then used to train and evaluate the proposed model, with the goal of identifying patterns and trends within the stock price movements. Various machine learning techniques were explored to find the optimal solution for the highest predictive accuracy. The results highlight the potential of the model in providing accurate insights of a stock price's future directional movement. This study contributes to the ongoing efforts made towards financial prediction by taking advantage of publicly accessible data and advanced computational methods.