sanjiv22/placement-prediction-project

My final year project developed in J2EE using Bootstrap, JSP Servlets and JDBC. Logistic Regression was used to train the data sets.

Placement-Prediction-Project

• Live Working Project Demo is present at: http://placement-prediction.herokuapp.com/Student

• Implemented in J2EE Technology, this Web application determined the chances of a student getting placed during the On-Campus Placement Drive, using parameters like GPA, Technical knowledge among others

• This project consists of 3 Module: Student, Admin and Company. Student module is for the colllege student to access the portal and take advantage of the resources present like Technical tests, scores and performance analysis using various graphs for test scores comparisions among peers of the college. Once a Student takes all the Technical tests, then we determine the chances of student getting placed, using Logistic Regression technique.

• Built and trained Logistic Model using real time data from the past years. System achieved accuracy of 71%.

• The Company module is present for recruiting companies coming to the College for the Placement activities, to see the overall progress and stats of a student studying in the college. The company representative will have access to all the college student's overall exam scores and technical tests results. The Company module can be accessed using the following: http://placement-prediction.herokuapp.com/Company

• The Admin module is present for the College faculty and mentors in order to see any student's performance and progress. The faculty and mentors can view the student's performance, and suggest improvements so that a student's chances of getting placed can be increased. The Admin module can be accessed using the following: http://placement-prediction.herokuapp.com/Admin

• Published a paper in IJTRA Journal, which gave an overview how logistic regression can be used to predict future.

• Published a paper in ICIATE Conference 2017 which described an efficient algorithm to generate coefficients of Logistic Regression Model.

• IJTRA Paper Link: http://www.ijtra.com/view/logistic-regression-analysis-as-a-future-predictor.pdf

• ICIATE Paper updated in folder