Stock Market Price Prediction Using Machine Learning

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Saranya K
Vijayashaarathi S
Sasirekha N
Koushikrajaa M
Lohith Raksha S


Machine Learning, Linear regression, Long short term memory (LSTM), Neural Networks, ANN, RNN


Stock Market value prediction is changing into a well-liked and vital topic in financial and educational studies. From previous couple of decades, there's associate explosive increase in the average person’s interest for stock exchange. The goal of stock market prediction is to foresee the stock price of a specific company over the long term. Accurate prediction of exchange value may be a terribly difficult task due to its unstable and non-linear nature of the stock markets. With the introduction of computer science Artificial Intelligence (AI) and Advanced Machine Learning strategies of prediction have proved to be efficient in predicting the stock costs. In this Paper, Linear Regression and LSTM techniques are used for predicting the longer-term stock value of two corporations of totally different fields of operation. In order to forecast the stock prices over the long run, these machine learning algorithms employ a dataset of stock prices from previous years. These datasets were obtained from Yahoo Finance with values of Date, Open, High, Low, Close, Volume value. Though share market will never be foretold with hundred Percent accuracy, because of its unstable and imprecise nature, this paper aims at proving the potency of LSTM model for predicting the longer-term stock costs with most possible accuracy.

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