IJNRD Research Journal

WhatsApp
Click Here

WhatsApp editor@ijnrd.org
IJNRD
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.76

Issue per Year : 12

Volume Published : 9

Issue Published : 96

Article Submitted :

Article Published :

Total Authors :

Total Reviewer :

Total Countries :

Indexing Partner

Join RMS/Earn 300

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: AI-Driven Career Path Prediction: A Machine Learning Approach”
Authors Name: Akanksha Yashwant Patil , Sandhya Sanjay Admapure , Yash Sagar Bulbule , Atharva Ramesh Jadhav , Suyog P. Sawant
Download E-Certificate: Download
Author Reg. ID:
IJNRD_213871
Published Paper Id: IJNRD2403366
Published In: Volume 9 Issue 3, March-2024
DOI:
Abstract: Career choice has a pivotal role in college students’ life planning. In today’s world choosing the right career is the toughest decision. Today many students are confused about their future. They do possess some skills but they are not able to identify their abilities and a proper domain. Different people suggest different career options but at last, the student has to select their career. In this project, we have focused on this problem of the student using machine learning. With the help of machine learning, we will help the student to decide which is the best career option and domain for them using different machine learning techniques. The career is decided based on academic information filled by the student. This project will help the student to get directed towards a specific domain as per their skills.
Keywords: —Machine Learning, Web Development,Course Recommendation System, career Prediction
Cite Article: "AI-Driven Career Path Prediction: A Machine Learning Approach”", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.d491-d493, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403366.pdf
Downloads: 00026
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID:IJNRD2403366
Registration ID: 213871
Published In: Volume 9 Issue 3, March-2024
DOI (Digital Object Identifier):
Page No: d491-d493
Country: Sindhudurg, Maharashtra, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403366
Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403366
Share Article:
Share

Click Here to Download This Article

Article Preview
Click Here to Download This Article

Major Indexing from www.ijnrd.org
Semantic Scholar Microsaoft Academic ORCID Zenodo
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX PUBLON
DRJI SSRN Scribd DocStoc

ISSN Details

ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to Get DOI? DOI

Conference

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Social Media

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Join RMS/Earn 300

IJNRD