Forecast Pupil Performance using SVM & Logistic Regression

The primary reason why machine learning has gained so much prominence nowadays is that it enables accurate and reliable decision making by extracting hidden relationships between various features present in the data. For this purpose the technique such as supervised methodologies and unsupervised methodologies are used. For this reason, machine learning can be used in almost any area of work to help in proper decision making and predictions. In our current project, we are trying to predict this student performance that utilizes supervised machine learning methodologies like support vector machines , logistic regression, random forests etc. We have also tried to publish this model to a web application so that it can be used by the academic community. The information extracted and the knowledge gained by extracting information from the educational data set would be helpful for predicting the student grades and their future performance. The main intention of the project used to predict student performance beforehand and help them get good grades in future. This would help in increasing the motivation levels of the students, improving their grades, decreasing their dropout ratio, and preparing better students for a better world