PREDICTING UNIVERSITY DROPOUT STUDENTS THROUGH DATA ANALYSIS

University dropout will effects the all universities students in the world, with consequences such as reduced registration, reduce the revenue for the university, lossing the money for state that funds the studies ,and joining the constitutes a social effects for college students, their families, and also society. The importance of predicting university dropout is finding the dropout students before leaving the college, so as to style methods to tackle the effects of it. By proofing the large knowledge technology to store the students attendance, checking marks, communication skills to find the exact students future Marks who has got the highest marks from the dropout students. We are trying to use different kinds of learning system to remove the most choices of being dropout .This may reduce the dropout rates of the university students and their total marks .As wells as find and detailing the efficiency of comparative study with finding the most effective accurancy apply in varied supervised machine learning technique through the given dataset with interface based mostly application by given dataset. Decades of analysis on artificial neural networks (ANNs) have published the thought that ANNs square measure per sensitive to missing/incomplete inputs at prediction time .Studies on dependable ANNs show that a neural network can’t be thought of in and of itself fault tolerant ,and it’s unimaginable to induce complete error masking once a fault occurred ,even within the presence of learning. Specific methodologies and neural design have, thus, been planed to enforce fault tolerance,however largely restricted to failure in hidden neurons.