Stock Price Prediction of AAPL Stock by Using Machine Learning Techniques: A Comparative Study

In today's world, everyone is looking for smart way of investing his/her hard earned money. There are several ways of investment, such as purchasing land, investing in real estate, fixed deposit in banks and investing in the stock market. In all other investment options except for the stock market, require a lot of initial funds. This is one of the reasons why many people are motivated to invest in the stock market. But stock market is very volatile, dynamic and uncertain. So its prediction is a very challenging and tough task in today's competitive and open environment. Earlier this prediction work was done by expert stock brokers and traders. This prediction by human being was possible because data was limited, simple, restricted to local area and powerful computing machines were not available. Now a day data is unlimited, complex and its generating speed is very fast that's why data prediction is done by machine learning algorithms. Selection of appropriate machine learning model is again a difficult task. This paper evaluates the performance of five different ML algorithms: linear regression, support vector machines (SVM), random forest, neural networks, and long short-term memory (LSTM) for predicting the stock price of Apple Inc. (AAPL). Accuracy of all five machine learning models is evaluated using values of performance metrics like root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Goodness of fit of all five machine learning models is also checked by using values of coefficient of determination $(\mathbf{R}^{2})$. This paper predicts AAPL stock value by using five different algorithms and then graph is plotted between actual & predicted prices for all models. Plot between actual & predicted AAPL stock value, measurement metrics (RMSE, MSE, MAE, MAPE) value demonstrate that regression algorithms, especially linear regression, neural networks, and LSTM achieve good accuracy on the AAPL stock price data. Value of coefficient of determination $(\mathbf{R}^{2})$ proves that LSTM model is fit for stock price prediction of AAPL stock.