Humongous volumes of data are being generated every minute by individual users as well as organizations. This data can be turned into a valuable asset only if it is analyzed, interpreted, and used for improving processes or for benefiting users. One such source that is contributing huge data every year is a large number of web-based crowd-funding projects. These projects and related campaigns help ventures to raise money by acquiring small amounts of funding from different small organizations and people. The funds raised for crowdfunded projects and hence their success depends on multiple elements of the project. The current work predicts the success of a new venture by analysis and visualization of the existing data and determining the parameters on which success of a project depends. The prediction of a project's outcome is performed by application of machine learning algorithms on crowd-funding data stored in the NoSQL database, MongoDB. The results of this work can prove beneficial for the investors to have an estimate about the success of a project before investing in it.
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