Paper Key : IRJ************769
Author: Kirankumar Kallangowda Patil
Date Published: 05 Jul 2024
Abstract
India's stock market is extremely variable and indeterministic, which has limitless number of aspects that regulate the directions and trends of the stock market; therefore, predicting the uptrend and downtrend is a complicated process. This paper aims to demonstrates use of recurrent neural networks in finance to predict the closing price of a selected stock and analyze sentiments around it in real-time. By combining both these techniques, the proposed model can give buy or sell recommendations. Weve put the proposed system into action as a web app using Django and React. The React Web App displays all live prices and news received from the self-built Django Server via web scraping. Additionally, the Django server serves as a bridge between the React frontend and the machine learning algorithm built with Keras and further enhanced with Tensorflow. Stock is an unpredictable curve. Prediction in stock market is covered with complexity and instability. The main aim for the persuasion of this topic is to predict stability in future market stocks. So much researchers have conducted their studies on the movement of future market evolution. Stock consists of fluctuating data which makes data as an integral source of efficiency. In the recent trend of Stock Market Prediction, Deep learning has integrated itself in the picture for deployment and prediction of training sets and data models. Deep Learning employs different predictive models and algorithms to predict and automate things of requirement. We use LSTM to predict stock prices.