DATA SCRAPING AND BUSINESS SUCCESS RATE PREDICTION

Abstract

As we know, Businesses are increasing day by day and due to this rapid increase, the people who have a product to sell have a dire need for prediction of the success rate, keeping this scenario in mind a way in which investors can use their money which can bring them success. A person who wants to do shopping sometimes doesn’t know about the outlets quality so this provides a solution in which a person will get to know about the outlets value through sentiment analysis and choose on customer’s point of view. Predicting the success of a start-up is commonly defined as two-way strategy that makes a large amount of money to its founders, investors and first employees with a focus on how a start-up or an investor could explore all this knowledge for a better decision making in investment strategy and monetary gain, the study intends, using scraping tools to scrap the data from the google maps and then by applying algorithms, to create a predictive model that has to classify whether a start-up is (already) successful or not. So, in this project we will create web application of machine learning algorithms to generate success rate. Everybody prefers quality over quantity so that’s why a sentiment analysis will be done over reviews of any outlet so a person who doesn’t know about the reputation of an outlet can easily decide