| Abstrakt: |
The prediction of stock prices poses an intricate and demanding challenge within the realm of finance. The emergence of artificial intelligence (AI) and machine learning (ML) methodologies has escalated the significance of stock price prediction for investors, traders, and financial experts. This study unveils a comparative examination of diverse ML algorithms intended for stock price prediction through AI mechanisms. We assess the efficacy of multiple algorithms, encompassing Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient Boosting Regression, employing a dataset of historical stock prices sourced from Yahoo Finance. Our findings demonstrate that the Gaussian Process Regressor surpasses other algorithms, boasting an impeccable R-squared value of 1.00. Moreover, we delve into the pivotal role played by feature engineering and preprocessing techniques in augmenting the precision of prediction models. This investigation furnishes valuable insights into the integration of AI in the financial domain, with the potential to enlighten investment and trading strategies. [ABSTRACT FROM AUTHOR] |
