Comparative Analysis of Distinct Regression Algorithm on Stock Price Prediction

This chapter presents a comprehensive comparative analysis of five distinct machine learning algorithms, namely Random Forest, Linear Regression, Bagging Regressor, AdaBoost Regressor, and Decision Tree Regressor, applied to the challenging task of stock price prediction. In an era marked by increasing reliance on data-driven decision-making, accurate stock price forecasting is of paramount importance for investors, traders, and financial analysts. The primary objective of this study was to identify the machine learning algorithm that exhibits superior predictive accuracy in the context of stock price forecasting. To achieve this goal, a meticulously curated dataset comprising historical stock price and relevant financial features was collected and preprocessed. Through a series of rigorous experiments and evaluations, this chapter examines and compares the algorithms' performance in terms of predictive accuracy, robustness, and computational efficiency.