Identification of Phony Profiles on Social Platforms using Random Forest and Decision Tree Algorithms

Given the pervasiveness of social media in today's world, the problem of identifying fraudulent accounts on sites like Social Media has taken on great significance. Python is the main technology used in this project, "Social Media Fake Account Detection using Machine Learning," to address this issue. The system employs 2 effective machine learning algorithms, the Classifier of Random Forests and its Decision Tree Classifier, to do this. The random forest classification system performs exceptionally well, achieving an accuracy of 100% on the training database and a startling accuracy rate of 93% on the test dataset. This Decision Tree Classifier simultaneously shows its efficiency by achieving ninety-two testing and ninety-two accuracy when trained. 576 records make up the dataset used for this project, and each record has 12 unique features. The presence or absence of a profile picture, the ratio of numbers of individuals in usernames and passwords the splitting of the full name into phrase tokens, the equality of login credentials and full names, the total length of user bios, which stands for the existence of other people's URLs, the private position of user accounts, the number of posts, the number of followers, the amount of takes into account followed, the and the last classification of a profile as "phony" or "Real" are just a few of the important aspects of online social networking user profiles covered by these characteristics. This project seeks to provide an efficient and dependable approach for recognizing Fake user profiles using Python and these advanced machine learning models. It contributes to keeping the software's validity and privacy of users intact.