STOCK MARKET PREDICTION USING MACHINE LEARNING

Abstract

Stock trading is one of the most essential activities in the financial sector. The technique to analyse past data and find a matching pattern in that by training a machine is called stock market prediction using machine learning. This paper demonstrates how Machine Learning may be used to forecast the performance of a stock. The majority of stockbrokers utilise technical and fundamental analysis, as well as time series analysis when making stock decisions. To forecast the outcome, the computer language is employed. In general, python is used for the prediction of various stocks. In the stock market, which is a sophisticated and difficult process, prediction plays a very critical role. Old school techniques such as statistical and foundational analysis may not guarantee the prediction’s accuracy. It suggests a Machine Learning (ML) technique that will be both efficient and effective. It has trained the machine using publicly available market data and intelligence. Using a trained computer algorithm (Machine Learning) is always preferable since it will give you advice based only on facts, numbers, and data and will not factor in emotions or prejudice. Machine learning for stock market forecasts can assist financial institutions in better managing their clients’ portfolios and making educated decisions in order to maximise earnings.