Employing Unsupervised Learning Algorithm for Stock Market Analysis and Prediction

Predicting the stock market is the typical task because of the unstructured nature of stock market data. There are multiple factors involved like physical, rational, and so on. Thus, all these later aspects make stock prices volatile and extremely hard to estimate accurately. In today’s era, artificial intelligence is playing in front of technological innovation. Every single business is using data science and AI technique directly or indirectly. In that queue machine learning, which is a subset of AI is letting AI systems learn and automate things without being explicitly programmed. In this paper, we have unsupervised a learning algorithm to predict future instances of stock market supplied the volatile and unstructured nature of stock data. The algorithm used in ml is taken from statistics itself and adhere better precision, this, we have used Arima model, LSTM for predicting stock prices with the accuracy of 88.7%.