Data mining methodology can analyze relevant information results and produce different perspectives to understand more about the students’ activities. When designing an educational environment, applying data mining techniques discovers useful information that can be used in formative evaluation to assist educators establish a pedagogical basis for taking important decisions. Mining in education environment is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational database and can used for decision making in educational system.
In this study, we collected the student’s data that have different information about their previous and current academics records and then apply different classification algorithm using Data Mining tools (WEKA) for analysis the student’s academics performance for Training and placement.
This study presents a proposed model based on classification approach to find an enhanced evaluation method for predicting the placement for students. This model can determine the relations between academic achievement of students and their placement in campus selection.

In the last two decades, number of Engineering Institutes and Universities grows rapidly in India. This causes a tight competition among these institutions and Universities while attracting the student to get admission in these Institutions/Universities. Most of the institutions and courses opened in Universities are in self finance mode, so all time they focused to fill all the seats of the courses not on the quality of students. Therefore a large number of students drop the course after first year. This paper presents a data mining application to generate predictive models for student's dropout management of Engineering. Given new records of incoming students, the predictive model can produce accurate prediction list identifying students who tend to need the support from the student dropout program most. The results show that the machine learning algorithm is able to establish effective predictive model from the existing student dropout data.

Higher education faculty staffs lack behind any prior training program of teaching. Mostly staffs teach students in his/her ways. They are unaware of the qualities of a teacher which they must possess as how to tackle the problems arising in teaching, what key points must be remembered while teaching etc. This may cause a teacher to be unsuccessful in classroom. So the problem is the amount of knowledge a staff has of a teaching process. Educationist finds few qualities of a good teacher. But their method is qualitative. In this paper a quantitative approach i.e. data mining is used to measure the quality of a teacher and suggest them what qualities they have.