International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019 2497 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number C4710098319/2019©BEIESP DOI: 10.35940/ijrte.C4710.098319 Abstract: Campus placement plays a vital role in every educational institution in helping students to achieve their goals. Data mining classification can be used as a useful tool for extracting the associated information from the large scale student dataset. Data mining methods have been used broadly in the area of the education system which involves various methods and approach for discovering knowledge. In this paper, a predictive model is designed which can predict the category of placements (dream companies, super dream companies and mass recruiter companies) in which students are eligible by considering their past performance in academics and other curricular activities. The model will also suggest further skills required for future recruitments which may help the students for placement preparation. The paper also provides real-time experimental results and findings along with performance measures used for model validation which helps in achieving the milestone of outcome-based education (OBE) in educational institutes as it is given utmost importance in present scenario to ensure better placement prospects in students, which would in turn help the students for carrier building. Keywords: Classification, Data Mining, Outcome-based education, Placement Prediction. I. INTRODUCTION An OBE curriculum starts with a clear picture of what students should attain in accordance with the curriculum designed, tutoring methods adopted and graduate attributes to be met. The final attainment achieved will ultimately make sure that learning happens and course outcomes are attained. At this moment, OBE is being adopted at a faster pace at engineering institutions all over India. In India, it is considered to be a giant leap forward for improvement in technical education which would help Indian Engineers to compete globally. OBE, which is a student-centred Revised Manuscript Received on September 20, 2019. * Correspondence Author Abhishek S. Rao*, Dept. of Information Science & Engineering, NMAM Institute of Technology, Nitte, India. Email: abhishekrao@nitte.edu.in Aruna Kumar S V, Dept. of Information Science & Engineering, NMAM Institute of Technology, Nitte, India. Email: arunkumarsv@nitte.edu.in Pranav Jogi, Dept. of Information Science & Engineering, NMAM Institute of Technology, Nitte, India. Email: jogi.pranav17@gmail.com Chinthan Bhat K, Dept. of Information Science & Engineering, NMAM Institute of Technology, Nitte, India. Email: bhatkchinthan@gmail.com Kuladeep Kumar B, Dept. of Information Science & Engineering, NMAM Institute of Technology, Nitte, India. Email: deepsalyan@gmail.com Prashanth Gouda, Dept. of Information Science & Engineering, NMAM Institute of Technology, Nitte, India. Email: prashantgoud98@gmail.com instruction system, focuses on student performance through gauged outcomes as knowledge gained, skills inculcated and attitudes perceived. The institution is given the right to decide on the assessment method for candidates during the program. Various assessment tools for gauging Course Outcomes include class tests, assignments, quiz, project work, labs, presentations, mid-semester and semester-end examinations, employer/alumni feedback which could be incorporated in educational institutions to meet the objectives of OBE. Even though the adoption of OBE at engineering institutions would be a great initiation for higher education in India, but the real success lies in the effective implementation and rigorous accreditation procedures to be met in order to guarantee that quality education is continued. Therefore OBE will help in filling gaps in academics to match with industry standards; thereby helping students in achieving better job prospects. In educational institutions, the student’s placement plays a key role in up-lifting institutional standards. Student’s academic performance and their academic skills are strongly influenced by placements. To attain high-quality placements, students should be adapted with qualities like problem-solving skills, sincerity and hard work, teamwork, and multitasking. It will be a boon to all students if these qualities are compiled in advance before the commencements of placement drives. Considering the above inputs a model can be proposed which may predict the outcome of the student’s placements option, based on their past performance in academics thus bringing the above concept to reality. At present, a huge amount of data is compiled and stored in educational institutions related to student enrollment, progress reports, examination results and many more. Educational data mining (EDM) is an evolving discipline, which is concerned with data set exploration from various sources and developing methods. Many of the techniques that we use in EDM come from computer science in the field of machine learning and data mining; where computers are used to analyze a huge amount of data. An important technique used in EDM is prediction modelling, in which a model could be developed which infers from one specific aspect of the data i.e. predicted variable which would help in future event prediction. In this paper, a model is proposed which could help the students in predicting the placement category by considering their past academic performance. The model also provides the necessary suggestions and guidelines required to improve the student’s skills for the further recruitment process. Abhishek S. Rao, Aruna Kumar S V, Pranav Jogi, Chinthan Bhat K, Kuladeep Kumar B, Prashanth Gouda Student Placement Prediction Model: A Data Mining Perspective for Outcome-Based Education System