To Forecast Future Stock Data Points by Using Machine Learning, Deep Learning and NLP

Abstract

The main aspiration of this research article is to find out the hidden patterns that are available in datapoints, these datapoints are used to predict the future value of the stock market. Stock prediction is the main aspect in finance sector to find out the risk management in the organization and the main motive is to crack the hidden code present in the datapoints and using those datapoints to predict the further datapoints. So, if we can crack it then automatically, we can predict any kind of hidden patterns present in datapoints with the help of python we can easily plot the future datapoints for better understanding of data in future. Here, the main task is how accurate the datapoints is an important task. And in this paper, we are going to analyze the datapoints with the help of Machine Learning, Deep Learning, and NLP.And the algorithms are AutoRegressive model (AR), Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), NLP with sentimental analysis of stock datapoints as text.