Background: In this age of technology, online business is playing a vital role in the growth of the economy of the business. Hence, people need feedback on various products, technologies, businesses, etc. Since their opinions are the input for an individual to evaluate and adapt them. Therefore, the Review system is playing a vital role in decision making. So there arises a necessity to evaluate the reviews as well since the business units are trying to generate fake reviews to identify more clients for their products. Methods/ Statistical Analysis: In this paper, we implement two machine learning algorithms SVM and Naïve Bayes algorithm and analyse the data and predict for the new set of data. We also compare the performance of both algorithms. Findings: In this paper, we are trying to develop a Machine learning model which analyses the reviews on various factors and obtain the necessary features and classify the reviews as a fake or non-fake review. This helps in identifying fraudulent reviews and predicts the trustworthiness of the reviews in the future. Improvements: The system can introduce and make available Machine learning techniques and identifying fake reviews at the earliest stage.

Anjali S Dilliwala, Raghavendra G S."Semantic & Behavioral Feature analysis for Detecting Fake Reviews using Machine Learning". International Journal of Computer Engineering In Research Trends (IJCERT), ISSN:2349-7084, Vol.7, Issue 06,pp.40-45, June - 2020, URL:https://ijcert.org/ems/ijcert_papers/V7I606.pdf,

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