Publication

Abstract : Because of high job transitions in the software industry, career trajectory prediction has become increasingly important. It has an impact on the industry’s economy in terms of the cost and time involved in replacing qualified human resources. Analyzing an individual’s career trajectory patterns clarifies the skills acquired and the various factors that contribute to each job transition. Identifying the right candidate is difficult with so much Human resource available across the globe. Online hiring and networking platforms generate huge volume of relevant and frequently updated profile of professionals. By identifying and analyzing patterns of learning and predicting career transitions can improve industry stability. Hiring and learning platforms based on social networking presented a challenge of more open opportunities for industries and individuals to choose the right opportunity. Huge volume of data from professional networking sites like Glassdoor and Linkedin are used to forecast patterns and the impact of career trends on the shift. This paper tries to identify the relative moving pattern among the IT professional and supporting the individual and industry for better decision making. Proposed methodology is verified over the dataset collected from the linkedin platform and shows 80% accurate prediction of career transition.