  Student dropout prediction is an indispensable for numerous intelligent
systems to measure the education system and success rate of any university as
well as throughout the university in the world. Therefore, it becomes essential
to develop efficient methods for prediction of the students at risk of dropping
out, enabling the adoption of proactive process to minimize the situation.
Thus, this research work propose a prototype machine learning tool which can
automatically recognize whether the student will continue their study or drop
their study using classification technique based on decision tree and extract
hidden information from large data about what factors are responsible for
dropout student. Further the contribution of factors responsible for dropout
risk was studied using discriminant analysis and to extract interesting
correlations, frequent patterns, associations or casual structures among
significant datasets, Association rule mining was applied. In this study, the
descriptive statistics analysis was carried out to measure the quality of data
using SPSS 20.0 statistical software and application of decision tree and
association rule were carried out by using WEKA data mining tool.
