Implementation on Fake Instagram Profile Identification and Classification Using Machine Learning

Abstract

Fake Instagram profiles are a growing problem, as they can be used to spread false information, deceive people, and harass and threaten others. The project aims to develop a robust and scalable system for identifying and classifying fake Instagram profiles using machine learning. This system will collect a large and diverse dataset of Instagram profiles, both real and fake. The data will be preprocessed and cleaned, and then relevant features will be extracted. Various machine learning algorithms will be evaluated to select the best model for identifying and classifying fake Instagram profiles. The trained model will be evaluated on a hold-out test set to ensure that it is able to generalize to new data. Once the model is evaluated and deemed satisfactory, it will be deployed in a production environment to identify and classify fake Instagram profiles in real time. The system is expected to have a significant impact on the fight against fake Instagram profiles. By identifying and classifying fake Instagram profiles, the system can help make the social media platform safer and more secure for everyone.. Key Words: Profile identification, Data preprocessing, Online security, User authentication, Model training, Machine learning.