Employability Prediction using Facebook Prophet for Computer Science and Engineering Graduates

Student career prospects are critical for colleges and universities since it is frequently used as a criterion of success. Interestingly, the employment market scenario is changing faster than ever as a result of globalization, digitization, and recent developments in Artificial Intelligence (AI). Understanding the key factors affecting employability as well as the requirements of the new job market may be helpful to all educational institutions. Educational institutions all across the globe are eager to create, design, and make adjustments in the Computer Science and Engineering (CSE) curriculum that will help their graduates succeed in employment opportunities. Time-series analysis is a popular tool for predicting the future by analyzing previous data. In educational institutions, fact-driven and Machine Learning (ML) methodologies are frequently used. More and more research is looking at ML approaches for predicting employment prospects. This research explores the employability of CSE students in campus placement using a time-series method. Facebook Prophet (FBP) was used in this study to estimate the employability of Computer Science and Engineering (CSE) students in campus placement by taking into account two global parameters: over-hiring and recession. Facebook Prophet with global factors consideration was analyzed and evaluated in terms of accuracy. The FBP model with global factors consideration has yielded an accuracy of 93.6%. This study came to the additional conclusion that significant global issues, including the recession and overhiring during the pandemic, affected the placement status of CSE students.