Aim:The Main target of this work is comparative study of Novel Decision Tree Algorithm and Linear Discriminant Analysis(LDA) Algorithm for optimizing Stock price prediction to improve the Accuracy of Stock Exchange. Materials and Methods: Novel Decision Tree Algorithm (N=10) and Linear Discriminant Analysis Algorithm (N=10) are simulated by varying the Novel Decision Tree parameter and Linear Discriminant Analysis parameter to optimize the pH. Sample size is calculated using Gpower 80% for two groups and there are 20 samples used in this work. Results:The examination of Accuracy rate is finished by independent Sample size utilizing SPSS Software. The Linear Discriminant Analysis Algorithm produces 16.91% Accuracy though Decision Tree algorithm produces 87.29% Accuracy. The Statistical significance difference between Decision Tree and LDA was found to be 0.063(p<0.05). Conclusion: The Outcome shows that the performance of Decision Tree is better than the performance of Linear Discrimination in the terms of Accuracy