Abstract

With the increasing popularity of social networking websites, the problem of fake profiles has become a significant concern. Fake profiles, often created by malicious actors for fraudulent purposes, pose threats to user privacy, security, and trustworthiness of online platforms. This project proposes a machine learning-based approach to detect fake profiles on social networking websites. By analyzing various features such as user activity patterns, profile attributes, and network connections, the model identifies potential fake profiles with high accuracy. The system employs a variety of machine learning algorithms, including decision trees, support vector machines (SVM), and random forests, to classify profiles as either genuine or fake. Data preprocessing techniques such as feature extraction, normalization, and outlier detection are applied to enhance the model's performance. The proposed approach is evaluated on a dataset of social network profiles, and its effectiveness is compared to existing methods in terms of precision, recall, and F1-score. The results demonstrate the ability of the machine learning model to detect fake profiles accurately, providing a valuable tool for social networking platforms to protect users from potential threats and improve the overall user experience. This solution can also help in the automated detection of fraudsters and reduce the manual effort required for profile validation.