Analytical Assessment of Prospective Technologies Tailored to Industrial Needs

This study presents a comprehensive Placement Cell ERP system augmented with an advanced analytics module that addresses major challenges in the management of placements and skill alignment within higher education. The system utilises automation, machine learning, and natural language processing (NLP) techniques to offer dynamic skill recommendations, analyse e-learning trends, and make personalised job suggestions. The main sources of the data were job posts on LinkedIn and course registrations on Udemy. A mix of methods, such as Selenium, APIs, and custom scripts, has been used to collect an extensive amount of data while maintaining privacy rules. During pre-processing, NLP methods like case normalisation, stopword removal, and keyword filtering were used to draw relevant information and ensure it was consistent and suitable for analysis. The skill recommendation method uses incremental modelling to identify and present the most pertinent skills in an industry through word clouds that are updated on a regular basis. The study looks into the connection between the need for expertise in job settings and e-learning trends. It affirms the notion that when demand goes up, so do enrolments in courses that meet that need. To provide precise and customised recommendations, the job recommendation system aligns student profiles with job descriptions using Word2Vec algorithms and cosine similarity. This research provides a scalable and adaptable solution for modern placement requirements by bridging the gap between academic instruction and industry demands.