Authors - Nitika Sharma, Rohan Patel, Hardikkumar Jayswal, Nilesh Dubey, Hasti Vakani, Mithil Mistry, Dipika Damodar, Shital Sharma Abstract - This study explores the use of advanced machine learning models to forecast trends in Apple’s stock market performance. Stock market forecasting presents a formidable challenge, given the inherent volatility and unpredictability of market behavior. The study investigates various advanced models, such as Logistic Regression, XGBoost, Artificial Neural Networks, Recurrent Neural Networks, Long Short-Term Memory (LSTM), and ARIMA, for predicting stock prices. Analyzing historical data spanning from 2014 to 2024, which includes Apple's daily stock prices and trading volume metrics, the research applies Grid Search optimization to fine-tune model parameters, thus enhancing predictive accuracy. The findings reveal that LSTM achieved the highest accuracy at 96.50%, followed closely by ARIMA at 90.91%. These results highlight the critical role of machine learning in improving stock price predictions, thereby facilitating more informed investment decisions.
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