Abstract: - Preceding purchasing an item, individuals typically advise themselves by perusing on the web reviews. To make more benefit dealers frequently attempt to fake client experience. As clients are being misdirected along these lines, perceiving and eliminating fake reviews is critical. This paper examines spam recognition techniques, in light of AI, and presents their outline and results. With the nonstop develop of E-trade frameworks, online reviews are essentially considered as a pivotal factor for building and keeping a decent standing. Besides, they have a powerful part in the dynamic interaction for end clients. Generally, a positive audit for an objective article pulls in more clients and lead to high expansion in deals. These days, tricky or fake surveys are intentionally composed to construct virtual standing and drawing in possible clients. Hence, distinguishing fake reviews is a clear and progressing research region. Recognizing fake reviews depends not just on the critical highlights of the surveys yet additionally on the practices of the commentators. This proposed system is designed to deal with distinguish fake reviews. Notwithstanding the highlights extraction interaction of the surveys, this paper applies a few highlights designing to extricate different practices of the commentators.Keywords: Online Reviews, Spam, KNN, fake reviewers