Comparative Analysis of Stock Price Prediction with Multiple Machine Learning Model

This study conducts a thorough comparative analysis of diverse machine learning models to assess their efficacy in accurately predicting stock prices while ensuring both AI and plagiarism are absent. It explores a range of models including Moving Average, k-nearest-neighbors (KNN), Linear Regression, Auto ARIMA, Facebook Prophet, and LSTM (Long Short-Term Memory). Through meticulous experimentation with historical stock price datasets, the research delves into each model's strengths, weaknesses, and predictive capabilities. It emphasizes factors such as model simplicity, interpretability, computational efficiency, and the capacity to capture inherent temporal dependencies in stock price movements. The study benchmarks classical models like Moving Average and Linear Regression for simplicity, evaluates KNN for its flexibility despite reduced interpretability, and assesses Auto ARIMA for its adaptive capability to various patterns. Facebook Prophet's resilience to missing data and outliers, tailored for daily and holiday patterns, is scrutinized, while LSTM's prowess in capturing intricate temporal dependencies is examined. By illuminating the trade-offs between model complexity and predictive accuracy, this research seeks to provide guidance to practitioners and researchers in selecting appropriate models for specific forecasting requirements. The conclusions drawn from the analysis contribute to the advancement of machine learning in financial forecasting and foster innovation in the ever-evolving market landscape.