Aim: The objective of the work is to predict the Stock Price Prediction Using LSTM Model Compared with ARIMA. 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 linear regression (N=10) and Support Vector Machine algorithms (N=10). Result: The result proved that Support Vector Machine (SVM) with better accuracy of 97% than linear regression accuracy of 96% and p=0.14 (p<0.05). It is statistically insignificant with a pretest power of 80%. The two algorithms LSTM and ARIMA are statistically satisfied with the independent sample T-Test value (p<0.001) with confidence level of 95%, Conclusion: prediction of stock price significantly seems to be better in LSTM.