Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. The financial data: Open, High, Low and Close prices of stock are used for creating new variables which are used as inputs to the model. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price. The Stock Market prediction task is interesting as well as divides researchers and academics into two groups those who believe that we can devise mechanisms to predict the market and those who believe that the market is efficient and whenever new information comes up the market absorbs it by correcting itself, thus there is no space for prediction. The artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. In existing system, Artificial Neural Network and Random Forest techniques have been utilized for predicting the next day closing price.in this system, it doesn’t efficient for large number of dataset. The predictive result is low. In our process, we have to take the input from the input dataset. The input dataset is time series dataset. It contains the open, high, low and close price. After that we have to implement the regression algorithms. The regression such as Lasso regression and linear regression. The final result, we have to find the error values. In time series dataset, we should find error values. Next, we have to visualize the data.