Aim: The objective of the work is to predict the Stock Price Prediction Using LSTM (Long Short Term Memory) Model Compared with SVM (Support Vector Machine). To achieve accuracy a novel SVClassifier is used, Method and Materials: Accuracy and loss are performed with a DATA dataset from the keras library. The total sample size is 20. The two groups Convolutional Long Short Term Memory (N=10) and Support Vector Machine algorithms (N=10), Results: The result proved that LSTM with better accuracy of 97% than SVM accuracy of 85%. The independent sample T-Test value (p < 0.05) with confidence level of 95% p=0.082 (p<0.05) it is statistically insignificant with a pretest power of 80%. The two algorithms LSTM and SVM are statistically satisfied with Conclusion: prediction of stock price significantly seems to be better in LSTM