This project discusses the machine learning algorithms for predicting students chances of admission to a doctoral program. Students will be able to predict their chances of acceptance of ahead of time. I present a novel dataset called Phd_admission_dataset and examine it to determine the performance of several machine learning methods, such as Logistics Regression, KNN. Experimental results show that the KNN model outperforms the Logistics Regression model.