STOCK PRICE PREDICTION USING MACHINE LEARNING – AN UNPRECEDENTED APPROACH

STOCK PRICE PREDICTION USING MACHINE LEARNING – AN UNPRECEDENTED APPROACH

Gandhe Sainath¹, Asst Prof. Nandini P²

Department of Computer Science and Engineering

The National Institute of Engineering, Mysuru

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Abstract:

In this research, we make an effort to put a machine learning method to stock price prediction into practice. Stock price forecasting uses machine learning effectively. In order to make wiser and more accurate financial decisions, the goal is to forecast stock prices. In order to improve stock forecast accuracy and generate lucrative trades, we suggest a stock price prediction system that combines mathematical functions, machine learning, and other external aspects.

There are two different stock types. You may be familiar with intraday trading through the phrase "Day trading". Interday traders frequently hold securities positions for multiple days up to weeks or months, but at least from one day to the next. Because they have the capacity to store historical data, LSTM’s are particularly effective at solving sequence prediction issues. This is significant in our situation since a stock's historical price plays a key role in determining its future price. While predicting a stock's real price is difficult, we can create a model that will predict whether it will rise or fall.

Keywords:

LSTM, CNN, ML, DL, Trade Open, Trade Close, Artificial Intelligence, Stock Market, Machine Learning, Predictions.