Enhancing Stock Price Prediction: A Comparative Study of KNN, LSTM Models for Apple Inc

This study delves into the utilization of machine learning techniques for predicting stock prices, a critical area that heavily influences financial planning and investment decisions. The research focuses on constructing and assessing the effectiveness of various predictive models, with an emphasis on K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN). Using Apple Inc.'s stock data as a case study, the findings reveal that the KNN model demonstrates superior predictive accuracy compared to LSTM and RNN models, suggesting that it may be more effective for stock price forecasting under certain conditions. This comparative analysis highlights the importance of selecting the appropriate model for financial forecasting and showcases the potential advantages of traditional machine learning models like KNN in comparison to more complex deep learning approaches. These results highlight the significance of selecting models that suit the underlying data characteristics. Despite the complexity of LSTM and RNN, the simpler KNN model demonstrates superior performance, suggesting that advanced models may not always guarantee better outcomes. This insight provides valuable guidance for investors and analysts in optimizing stock prediction methods and refining investment strategies by considering model simplicity and effectiveness over complexity. The findings emphasize the importance of model selection in financial forecasting, where simpler methods can sometimes yield more accurate results.