Stock Price Prediction Using Machine Learning: A Comprehensive Review and Comparative Analysis

Stock price prediction remains one of the most challenging tasks in financial forecasting due to the inherent volatility and nonlinearity of financial markets. This paper presents a comprehensive review of machine learning approaches for stock price prediction, comparing traditional statistical methods with modern deep learning architectures. We analyze various algorithms including Support Vector Machines (SVM), Random Forest, Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) networks. Our comparative analysis reveals that LSTM-based deep learning models achieve superior performance with prediction accuracies ranging from 85% to 89%, significantly outperforming traditional regression methods. The study examines key technical indicators, feature engineering techniques, and model optimization strategies that enhance prediction accuracy. We also discuss the challenges of market volatility, overfitting, and computational complexity. The findings demonstrate that sophisticated neural network architectures, particularly LSTM networks, offer more reliable predictions for investors and financial analysts in volatile market conditions. This research contributes to the growing body of knowledge on intelligent financial forecasting systems and provides practical insights for implementing machine learning-based trading strategies.