Aim: The aim is to improve accuracy and develop a Linear Regression model for prediction of stock market prices for better investments and huge profits using a novel method. Materials and Methods: Supervised Machine learning techniques such as Linear Regression Algorithm is being compared with Support Vector machine algorithm for predicting Stock market values. Sample size is determined by using G power calculator and found to be 20 per group. G power is predicted to be 80%. Total of 2 groups are used.Statistical analysis is done on SPSS Software. Significance value is observed to be 1.000. Results: Based on the analysis Linear Regression Algorithm method has an accuracy of 85% and Support vector machine has 77% and the significance value achieved is 1.000 (p>0.05). It shows that two groups are statistically insignificant. Conclusion: It is concluded that based on the execution analysis Linear regression shows the better accuracy when compared to the Support vector machine algorithm.