• Pawan Kumar
• Manmohan Sharma
• Sandeep Kaur
• Sanjay Sood

Machine learning (ML) models are increasing their penetration in different problem domains due to their increasing accuracy. Majority of the advanced ML-based stand high in predictive accuracy but are wanting in terms of explaining their outcomes. This research study has two objectives. First, to develop predictive models that can predict the placement status of undergraduate engineering students studying in India. Second, to understand the behavior of these ML-based models in terms of most important features affecting the prediction outcome. The outcomes of such study are of importance to any university as ability to anticipate placement status of students can help in planning timely interventions. It is concluded that ‘CurrentCGPA’ and ‘AttendancePercentage’ of a student plays an important role towards getting placed. Moreover, understanding of the behavior of a model help building trust in the model and provide multiple insights of importance to different stake holders.

"Identifying factors affecting placement status of engineering students using explainable machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.950-957, December 2018, Available :http://www.jetir.org/papers/JETIRDZ06123.pdf

"Identifying factors affecting placement status of engineering students using explainable machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp950-957, December 2018, Available at : http://www.jetir.org/papers/JETIRDZ06123.pdf