With the more advanced knowledge on Machine learning techniques, there is a boundless amount of applications with the help of Tensorflow Package, a mainstream Machine Learning Package for Python users. With the ability to predict and evaluate the values based on the training and testing sets, we want to explore the landscape of Startups and investigate the key factors that a Startup needs to succeed in the state of California, which is one of the major birthplaces of US Startups. Google, Facebook, Apple, Amazon, Netflix, all these Big-Tech companies that we know today have been through the stage of Startup. Startup describes the young, rising companies that have started from Zero to One. The First Pot of Gold from a Venture Capitalist and any investor is usually the priority of a Startup. To operate a Startup successfully, apart from the stability of the funding, the duration of surviving in the business environment is another indicator for the investor. When a Startup is able to run for more than an extensive time frame, either the company has found an Angel that has faith in the potential of the company or they have established a trust-worthy reputation in the venture capitalists’ world. Regardless of the outcome of a Startup, one of the safest approaches for a Startup to create its legacy is to get acquired by other big-brand companies. Hence, due to the nature of the dataset, the measure of a successful startup is the status of whether getting acquired. The goal of this project is to setup a supervised model that the investor’s can use to predict the success of a Startup. We aim to investigate the average predicted total funding for a Startup and understand some commonalities related to the funding among these successfully acquired Startups.