Abstract: - Generally, people trust product based on product reviews and rating. People can remove a review allow spammers to form spam studies about goods, furthermore, administrations for different benefits. Recognizing these fake reviewers and the spam content is a big debated issue of research, and despite the way that various number research has been done already. Up till now, the ways set hardly differentiate spam reviews, and no one shows the significance of every property type. In this investigation, a structure named NetSpam, which uses spam features for demonstrating review data sets as heterogeneous information frameworks to design spam identification method into a group of an issue in this networks. Using the criticalness of spam features help us to obtain good outcomes regarding different metrics on review data sets. The commitment work is when the client search question shows all n-no of items just as the suggestion of the item.Keywords— Fake Review, Machine Learning, Social Media, Social Network, Spammer, Spam Review