A Survey of Price Prediction using Deep Learning Classifier for Multiple Stock Datasets

Data about stock market prices is produced in vast quantities and is updated instantly. Individuals can either profit financially from the stock market or lose all of their life savings in this complex and difficult system. It is challenging to create an appropriate model since price variance depends on a variety of variables, including media, social media data, firm fundamentals, production, treasury securities, historic pricing, as well as national economics. A predictive algorithm that just takes into account one aspect might not be reliable. Thus, combining numerous variables such as news, data from social media, as well as historical price, could improve accuracy. In this survey, an effort is made to anticipate stock market price using an effective model, and machine learning as well as deep-learning algorithms have been used to analyse stock datasets and estimate the next day's closing price such as naive Bayes, decision tree, support vector machine and Multilayer perceptron algorithm. The models are assessed using the characteristics of precision, recall, and accuracy.