• Shanamgari Vyshnavi
• Mekala Aarthi
• Thikka Vinuthna Patel
• Aadherlla Divyasree

The aim of this project is to utilize machine learning techniques, specifically artificial neural networks, to determine the authenticity of friend requests on Facebook. This involves developing a model capable of accurately distinguishing between genuine friend requests and potentially fraudulent ones, thereby enhancing user security and privacy within the platform. Key components of the project include the utilization of relevant classes and libraries in the machine learning domain, alongside the implementation of the sigmoid function for classification purposes. Furthermore, the determination and utilization of weights within the neural network play a crucial role in the model's decision-making process. Additionally, the project aims to address broader concerns regarding the vulnerability of personal data, particularly in the context of bots and fake profiles. These entities pose significant threats to user privacy, often engaging in web scraping activities to gather sensitive information clandestinely. Despite the legality of web scraping, its potential misuse underscores the importance of robust security measures within social networking environments. By exploring these challenges and emphasizing the significance of parameters within social network pages, this project seeks to contribute to the ongoing efforts towards safeguarding user data and enhancing online security protocols..

Fake Profile Detection, Artificial Neural Network (ANN), Social Media, Machine Learning, Data Mining, User Behavior Analysis, Feature Extraction, Classification Algorithms, Pattern Recognition, Fraud Detection, Identity Verification.

"Fake Profile Detection on Social Media using ANN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.e43-e49, March-2024, Available :http://www.jetir.org/papers/JETIR2403406.pdf

"Fake Profile Detection on Social Media using ANN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppe43-e49, March-2024, Available at : http://www.jetir.org/papers/JETIR2403406.pdf