A stock market or share market is the combination of shoppers and sellers of shares. Prediction of the stock market is a method for calculating the future value of a company's stock. Stock market can be regarded as a specific records of data mining as well as machine learning problem. The daily changes within the stock depends on the profits and losses and many people think that stock market is irregular and uncertain. Based on daily changes we can predict some movements in the stock. In the previous years, researchers have used many machine learning models to know the development of the stock market to enhance the accuracy. However researchers have implemented these machine learning models independently and compared the results of models. It is observed that the group of models produce quite much less noisy compared to independent models. Our vision is to study investment strategies to predict and analyses the stocks using machine learning models such as Random Forest, K Nearest Neighbor, Gaussian Naive bayes and Decision Tree. And these models are ensemble by using different techniques such as Voting Classifier, Adaboost classifier and Bagging Classifier to estimate their accuracies. From the results it is observed that ensemble approach gives maximum accuracy compared to individual machine learning models.