Stock Market Prediction using Machine Learning Technique

Abstract

The stock exchange has grown to be one of most important events in today's financial world. The current state of the stock market has a significant impact on the global economy. People from many walks of life, whether they come from business or academic backgrounds, have been drawn to the stock market with great success. The stock market's nonlinear character has made research on it among the most important and popular topics worldwide. People choose to make investments in the stock market based on their predictions or knowledge from earlier studies. In terms of forecasting, people frequently seek out instruments or strategies that would reduce their risks and maximize their earnings; as a result, stock price forecasting assumes a significant position in the always competitive stock market industry. Adopting conventional methods like fundamental and technical analysis doesn't seem to guarantee the predictability's consistency and accuracy. As a result, machine learning technologies have emerged as the most recent trend in stock market forecasting, with predictions based on current market values because of training on earlier values. In order to forecast the present trend of the stock market, this article focuses upon Machine Leaning, Analysis and LSTM (Long Short Term Memory) technology.