Abstract
Stock price prediction plays an essential function in the investment landscape, allowing investors to estimate the future value of a company’s share. More individual/retail investors have been investing in stock market since last few years. Therefore, accurately forecasting stock prices has become important and also challenging. This paper explores different machine learning algorithms like Linear Regression (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR) and Support Vector Regressor (SVR). The obtained machine learning models were evaluated under various performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and $R2$ Error. After implementing and evaluating these models, we compared their performance, considering the ten years of data from the dataset available in Yahoo Finance. Our findings revealed that linear regression consistently outperformed the other algorithms, making it the most effective choice for stock price prediction. This insight underscores the importance of leveraging machine learning methods in financial forecasting and supports the growing need for reliable investment strategies.
