Data mining is a process, which finds useful patterns from large amount of data. Data mining is looking for hidden, valid, and potentially useful patterns in huge data sets [1]. Data mining is classified into predictive and Descriptive models. In the present work the techniques of data mining used classification, were classification is a predictive method. The predictive method makes prediction about values of data. Classification techniques are using various decision tree algorithms. In this Paper, four decision tree algorithms (ID3, C4.5, Random forest and CART) have been applied on the historical data for predicting the Stock Market performance in Price. The efficiency of various decision tree algorithms can be analyzed based on their accuracy and time taken to derive the tree. The predictions obtained from the system have helped the Investor to identify and improve their performance. Particularly, this work is carried out to compare the four decision tree algorithms in the prediction of the performance accuracy in Stock Market data. All the algorithms are applied for Stock Market data to classify the data set for classification and prediction. Among these four methods, this work concludes the best algorithm for the chosen input data on decision tree supervised learning algorithms to predict the best classifier. This learn about tries to help the buyers in the inventory market to decide the better timing for buying or promoting shares based totally on the information extracted from the historical prices of such stocks. The decision taken will be based on the best decision tree algorithms.