The basic tool aimed at increasing the rate of investor's interest in stock markets is by developing a vibrant application for analyzing and predicting stock market prices. In this report we explain, the development and implementation of a stock market price prediction application using a machine learning algorithm. In this report, we try to analyze existing and new methods of stock market prediction. We take three different approaches to solving the problem: Fundamental analysis, Technical Analysis and The application of Machine Learning. We found evidence in support of the weak form of the Efficient Market Hypothesis. We can use Fundamental Analysis and Machine Learning to guide an investor’s decisions. We demonstrate a common flaw in Technical Analysis methodology to show that it produces limited useful information. Based on our findings, algorithmic trading programs are developed and simulated using Quant. During the past few decades, various machine learning techniques have been applied to study the highly theoretical and speculative nature of the stock market by capturing and using repetitive patterns. Different companies use different types of analysis tools for forecasting and the main aim is the accuracy, with which they predict which set of stocks would yield the maximum amount of profit.