Course Content

Abstract:

Placements are considered to be very important for each and every college. The main objective of this model is to predict whether the student gets placed or not in campus recruitment. For this the data considered is the academic history of student like overall percentage, backlogs, credits. The algorithms are applied on the previous years data of the students .A high placement rate is a key entity for any educational institution. Hence such a system has a significant place in educational system of any learning institutions. Placement of scholars is one in every of the vital activities in academic establishments. Admission and name of establishments primarily depends on placements. Hence all institutions strive to strengthen placement department. The main Objective of this paper is to analyze previous year’s student’s historical data and predict placement possibilities of current students and aids to increase the placement percentage of the institutions.

So we proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree and Naïve Bayes to predict that student will be get placed or not based on different parameters like academic results, communicational skills, technical skills etc.

Introduction:

Nowadays educational institutes are growing in high numbers. Aim of every higher educational institute is to get their students a well-paid job through their placement cell. One of the largest challenges that higher learning establishments face nowadays is to boost the placement performance of scholars. The placement prediction is additional complicated once the quality of instructional entities increase. One of the effective ways to address the challenges for improving the quality is to provide new knowledge related to the educational processes and entities to the managerial system. With the machine learning techniques the information are often extracted from operational and historical knowledge that resides at intervals the academic organization’s databases exploitation. The information set for system implementation contains data regarding past data of scholars. These knowledges square measure used for coaching the model for rule identification and for testing the model for classification. The prediction of placement status that students are most likely to achieve will help students to put in more hard work to make appropriate progress in stepping into a career in various technical fields. It will also help the teachers as well as placement cell in an institution to provide proper care towards the improvement of students in the duration of course. A high placement rate is a key entity in building the reputation of an educational institution. Hence such a system has a significant place in the educational system of any higher learning institution.

So we proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree and Naïve Bayes to predict that student will be get placed or not based on different parameters like academic results, communicational skills, technical skills etc.

Objective:

The main aim of this project to predict that student will be get placed or not with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree and Naïve Bayes based on different parameters like academic results, communicational skills, technical skills etc.

Problem Statement

The general Placement Prediction System considers only academic performances in order to predict whether a student can be placed or not. Judging the student based only on his academic performances would be unfair for the student, since a student could be having good aptitude, technical and communication skills but unfortunately might not be good in academic performances.

Proposed System:

We proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree and Naïve Bayes to predict that student will be get placed or not based on different parameters like academic results, communicational skills, technical skills etc