One of the long term goals of any college or university is increasing the student retention. The negative impact of student dropout are clear to students, parents, universities and society. The positive effect of decreasing student attrition is also self-evident including higher chance of having a better career and higher standard of life for college graduate. In view of these reasons, directors in higher education feel increasingly pressurized to outline and implement strategies to increase student retention. In this paper, we provide a detailed analysis of the student attrition problem and use statistical methods to predict when students are going to dropout from school using real case data. Our work has a number of advantages with the potential of being employed by higher education administrator of universities. We take advantage of multiple kinds of information about different aspects of student's characteristic and efficiently utilize them to make a personalized decision about the risk of dropout for a particular student.