Estimating Career Path using Supervised Machine Learning on Skills Assessment & Continuous Evaluation for Students

The research paper focuses on revolutionizing student placement predictions in educational institutions through the integration of machine learning and data visualization techniques. Acknowledging the pivotal role of placements in shaping a college's reputation, the study proposes a comprehensive approach to enhance student preparedness and optimize placement outcomes. The paper introduces a unified platform leveraging exploratory data analysis (EDA) and machine learning models, such as Linear Regression (LR), to provide real-time insights into students' placement readiness. Notably, the research addresses the shortcomings of existing systems by emphasizing the need for a consolidated platform that not only predicts placements but also guides students through their preparation journey.