Abstract: Forecasting stock market prices remains a very major concern to economists, financial analysts, and data scientists for the past decades. This paper investigates the application of several machine learning algorithms for stock market price prediction and compares their performance. The algorithms used in this research are the Support Vector Regressor, Random Forest, K-nearest Neighbor, Logistic Regression, Decision Tree, Long Short-Term Memory Networks, Gated Recurrent Units, and a mixture of LSTM and GRU networks. These models are tested on some historical datasets of Reliance stock prices, which are subjected to extensive preprocessing, which includes dealing with missing values and feature engineering. The predictive accuracy of each model is taken as the mean. absolute error and root mean-squared error. In this respect, the paper provides the most in-depth comparison of predictive capabilities of these models available to date and offers potential empirical evidence to benefit researchers and practitioners in the area of financial forecasting.

Keywords: Stock Market Prediction, LSTM, Random Forest, Decision Tree, Financial. Forecasting, Machine Learning, Time. Series Forecasting, GRU, Logistic Regression, K-nearest Neighbor, Support Vector Regressor.

| DOI: 10.17148/IARJSET.2024.11727