Stock Market Prediction Performance Analysis by Using Machine Learning Regressor Techniques

Stock market prediction is a highly popular topic among investors, traders, and financial analysts. It involves predicting the future direction of stock prices, which can help investors make decisions about buying or selling stocks. One method of predicting stock prices is through sentiment analysis. Sentiment analysis has become an increasingly popular method for predicting stock prices in the stock market. Stock market forecasting is vital nowadays. In this study, machine learning techniques are used to predict the stock market and are proven effective. The main objective of this study is to enhance the accuracy of machine learning regressor algorithms. This article uses the state of the art of five different machine learning regressors. These are namely Bagging Regressor, XGB Regressor, LGBM Regressor, Hist Gradient Boosting Regressor, and AdaBoost Regressor. Of these five algorithms, Bagging Regressor outperforms the other four, obtaining better R-square and RMSE values. This study shows that the experimental results obtained are reliable. Bagging Regressor produced the R-square value of 99.9774 and RMSE value of 8.305, taking a time of 0.185652733 ms. It summarizes the findings from the experiments conducted and suggests that the Bagging regressor performed well in terms of accuracy.