• Shashi Patel
• Geetanjali Soni
• Kshama Sharma

Online reviews have become an integral part of the consumer decision-making process. However, the presence of fake or deceptive reviews hinders the reliability and trust worthiness of online platforms. This paper presents Gadget Review Analyser, a web-based application that combines web development using the Django framework with machine learning techniques to provide users with a reliable tool for analysing customer reviews of electronic gadgets on Flipkart. The system utilizes various machine learning models, including Logistic Regression, SVM, Naive Bayes, to accurately classify reviews as either fake or real. In the digital marketplace, product reviews play a crucial role in influencing consumer purchasing decisions. However, the prevalence of fake reviews has undermined the credibility of online platforms, leading to significant economic consequences for both consumers and businesses. This paper explores methodologies for monitoring and removing fake product reviews to enhance the authenticity of ratings and restore consumer trust.

"Fake Product Review Monitoring and Removal for Genuine Rating", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.c693-c696, April-2025, Available :http://www.jetir.org/papers/JETIR2504289.pdf

"Fake Product Review Monitoring and Removal for Genuine Rating", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppc693-c696, April-2025, Available at : http://www.jetir.org/papers/JETIR2504289.pdf