Stock Price Prediction of Tesla & Apple using LSTM

Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India
Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India
Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India
Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India

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Abstract

The stock market has always piqued academics' interest in predicting. Since they were originally created, machine learning algorithms have been increasingly popular for predicting changes in the stock market. The intricacy and unstable nature of the stock market make it challenging to predict stock values. In recent years, stock value forecasting has used both sentiment analysis and machine learning techniques. In this work, we present a study to anticipate the stock prices of Tesla and Apple using sentiment analysis and long short-term memory (LSTM). Using historical stock prices and sentiment data from Twitter, our LSTM model will be trained and put to the test. To evaluate the proposed model, metrics like normal error, mean square error (MSE), and mean absolute error (MAE) are taken into account. The obtained results showed very promising results and can be incorporated in stock price prediction applications.