Internet Communities are affluent in Fake Accounts. Fake accounts are used to spread spam, give false reviews for products, publish fake news, and even interfere in political campaigns. In business, fake accounts could do massive damage like waste money, damage reputation, legal problems, and many other things. The number of fake accounts is increasing dramatically by the enormous growth of the online social network; thus, such accounts must be detected. In recent years, researchers have been trying to develop and enhance machine learning (ML) algorithms to detect fake accounts efficiently and effectively. This paper applies four Machine Learning algorithms (J48, Random Forest, Naive Bayes, and KNN) and two reduction techniques (PCA, and Correlation) on a MIB Twitter Dataset. Our results provide a detailed comparison among those algorithms. We prove that combining Correlation along with the Random Forest algorithm gave better results of about 98.6%.