Machine Learning Approaches for Stock Price Prediction of Amazon Company

Forecasting stock price movements, especially for prominent companies like Amazon, remains a significant challenge and a key focus in both academic research and financial analysis. The stock market is known for its high volatility and unpredictability, making accurate prediction complex. This study explores the application of machine learning techniques to enhance the reliability of stock price forecasting. Specifically, it focuses on deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which are designed to capture sequential patterns in historical data. For comparative analysis, a traditional Linear Regression model is also implemented. The experiments were conducted using real historical stock data from Amazon, employing two different feature sets: one containing fundamental price features (High, Low, Close, Volume), and another enhanced with technical indicators including the Relative Strength Index (RSI), Exponential Moving Average (EMA), and Moving Average Convergence Divergence (MACD). The effectiveness of each model was evaluated using standard performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE).