The stock market is a dynamic, constantly evolving system that generates enormous volumes of real-time data on price updates. Participants in the market have the opportunity to make significant financial gains or run the danger of losing everything they have worked for. However, developing a suitable model that takes price variability into account is a difficult task. Precision, recall, and accuracy are the three criteria that make up the evaluation of models. The various variables that affect stock prices include things like media coverage, social media data, corporate fundamentals, output, treasury securities, historical pricing, and world economic situations, to name just a few. As a result, relying solely on a prediction algorithm built on a single element might not produce reliable results. To improve the accuracy of stock price forecasts, many factors must be considered simultaneously. Prediction accuracy might be improved by combining numerous aspects including news, social media data, and historical pricing. In order to find patterns and generate predictions based on those patterns, machine learning algorithms may learn from previous data. By analysing a lot of data, deep learning, a kind of artificial neural network, may learn and enhance its performance over time. The algorithms will examine the forecast. On the basis of measures for precision, recall, and accuracy, the models’ performance will be assessed. The proportion of accurately anticipated stock price movements out of all predicted movements is known as precision; the percentage out of all actual movements is known as recall; and the percentage out of all forecasts is known as accuracy.