Aim: The main aim of the research work is to predict the stock equity using the Linear Regression (LR) over the K Nearest Neighbor (KNN). Materials and Methods: The two algorithms linear regression and decision tree are compared with a sample size = 10. Sample size is calculated using G power software and determined as 10 per group with pretest power 80%, threshold 0.05% and CI 95%. Results: The analysis of the results shows that the Linear Regression has a high accuracy of (94.38%) in comparison with the Decision Tree (86.19%). Attained Significance Accuracy value is 0.591 (p<0.05). There is a statistically insignificant difference between the study groups with these algorithms. Conclusion: Prediction in classifying from the results it is concluded that the proposed algorithm Linear Regression will produce better results than the Decision tree algorithm.