• Kadimi Mounika Ram
• Jammigumpula Reshma
• Yallampalli Gowtham
• Thanniru Prashanth
• B. Sai Deepthi

The "A PERSONALIZED STUDENT ADVISORY SYSTEM" is a multifaceted project designed to streamline the career planning and job search process for students and job seekers. The project encompasses a user-friendly web application featuring a central homepage with accessible options for career guidance. Leveraging advanced technologies such as machine learning and generative AI, the platform offers a suite of tools tailored to address various aspects of the career journey. The "Placement Eligibility Predictor" employs machine learning models to assess a student's eligibility for job placements based on key metrics including age, internships, backlogs, CGPA, and gender. Additionally, the platform includes a "Job Description Generator" that utilizes generative AI to produce customized job descriptions based on provided job titles. Furthermore, a "Job Title Predictor" predicts suitable job titles based on user-provided skills, while a "Cover Letter Generator" creates personalized cover letters tailored to specific job titles. Lastly, an "Interview Questions Generator" generates interview questions customized to the job title provided by the user. Together, these tools aim to empower users with personalized guidance and resources to enhance their career prospects and job search success

Personalized Student Advisory System, Career Planning, Job Search Process, Web Application, Machine Learning, Generative AI, Placement Eligibility Predictor, Job Placements, Key Metrics

"A PERSONALIZED STUDENT ADVISORY SYSTEM USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.c376-c384, May-2024, Available :http://www.jetir.org/papers/JETIR2405245.pdf

"A PERSONALIZED STUDENT ADVISORY SYSTEM USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppc376-c384, May-2024, Available at : http://www.jetir.org/papers/JETIR2405245.pdf