The price prediction of the stock is crucial, and it is an equally difficult task due to the extreme fluctuations in the stock market. In this article, the effectiveness of various machine learning algorithms, such as Neural Network, SVM regression algorithm, Random Forest, Regression Tree, KNN regression algorithm, Gradient Boosting Algorithms, Lasso & Ridge regression, and Elastic Net Regression, are employed for predicting stock open prices for software companies listed on the BSE Sensex 50 of Indian stock market. Even though it is not possible to predict open prices very precisely, intrigued by the importance of accurate stock price predictions for financial well-being, the study undertakes a comparative approach to evaluate the performance of these algorithms systematically. This comparison aims to provide insights into algorithms that demonstrate superior performance. The superiority of Ridge Regression and Lasso Regression over other algorithms is observed. The results of this study will be expedient for practitioners and researchers in the finance field. The most influencing factors for predicting stock prices are also obtained.