Placement Readiness Analytical Engine
A machine learning-powered career analytics platform that evaluates student placement readiness, segments users using clustering, and generates personalized insights, career guidance, and improvement strategies.
A machine learning-powered career analytics platform that evaluates student placement readiness, segments users using clustering, and generates personalized insights, career guidance, and improvement strategies.
Students lack clarity about their placement readiness. They do not know their current level, missing skills, or what actions are required to improve and secure a job.
Developed a system that evaluates multiple student attributes and generates a personalized analytics report with readiness level, strengths, weaknesses, and actionable recommendations.
Used K-Means clustering to segment students into: Ready, Almost Ready, and Not Ready categories based on performance patterns.
Python (Pandas, NumPy, Scikit-learn), NLP techniques, Flask backend, HTML & CSS frontend, PDF generation tools.