Many colleges and universities are still in doubt as to how many teaching staff they have in all Iraqi universities. Based on this fact, they alone have to be using a plethora of methods. For instance, they can in decision trees, the algorithm KNN and the logistic regression. The subsequent area of investigation that requires statistical procedures is undoubtedly data mining in education. This is a branch of science and technology that contains a lot of information about academic performance of employees, teachers, and the necessary steps to be taken for their better performance. We have used the dataset from Al-Furat Al-Awsat Technical University Al-Furat Al-Awsat Technical University as a sample for the study. The proposed study frame is split into four modules namely pre-processing, feature selection, algorithm selection, and the performance evaluation. In, the, first step, data, preprocessing is done to get rid of the data set’s imperfections and prepare it for analysis. The second step is concerned with selecting the most important features in differentiating the dataset into classes. The first step that the students have to do is to choose the most suitable machine learning algorithm for data classification. At the end, the performance of the chosen algorithm is evaluated using different methods such as precision, accuracy, recall, F1 score and others.