Stock Price Prediction Using Machine Learning

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G. Sudha, Thumma Vidya Shiny, C. Ramesh Reddy, Shaik Irfan, K. N. Dharan, J. Vinod Kumar


In this study, we apply a machine learning approach to try to anticipate supply costs. Expert systems are used efficiently to project supply rates. Projecting supply costs is meant to help financiers make better informed and focused financial decisions. We suggest a supply price forecast system that integrates mathematical characteristics, expert systems, and other external factors in order to increase supply projection accuracy and also provide satisfying careers. Stocks come in two different price ranges. The term "day trading," which refers to intraday trading, may be familiar to you. Most frequently, intraday traders keep their positions in safekeeping for a number of days, occasionally even for weeks or months. LSTMs are particularly adept at navigating challenges associated with series prediction because of their ability to retain historical information. This is pertinent to our situation since a supply's historical cost is a key factor in estimating its future rate. Even if it's just a test of our ability to anticipate a stock's true price, we can develop a model that will tell us whether a stock will surely increase in cost or decrease.

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