Data-Driven Insights into Training and Placement Dynamics in Technical Careers for Women

This research investigates the internship and placement patterns in a government-run technical university for women in India, focusing on factors influencing recruitment after college. Working on a self-annotated corpus from the university’s Training and Placement Cell, the study analyzes data using both a data-centric and machine learning approach. The findings reveal insights into internship trends, placement dynamics, average packages, and factors affecting the recruitment process. Key observations include the prevalence of opportunities in IT and Computer Science, the strong correlation between discipline and placement success, and the influence of pre-placement offers (PPOs). The study also explores reasons for non-placement and suggests future research directions to understand evolving recruitment strategies in the twenty-first century.