Evaluation of a Job Suggestion Tool Using Machine Learning
Sharad Soni
, Prof. Sumit Sharma
Job Recommender Systems, Machine Learning , Businesses , Content Based Filtering , Gradient Boosting Regression Tree.
This study evaluates the effectiveness and performance of a job suggestion tool that utilizes machine learning techniques. The tool aims to provide personalized job recommendations to users based on their skills, experience, and preferences. The evaluation process involves collecting user data, training a machine learning model, and assessing the tool's accuracy and relevance in matching users with suitable job opportunities. The study also considers user experience factors such as usability, interface design, and overall satisfaction. The findings provide insights into the tool's strengths, limitations, and potential areas for improvement, offering valuable feedback for developers and stakeholders.
"Evaluation of a Job Suggestion Tool Using Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 7, page no.576 - 583, July-2023, Available :https://ijsdr.org/papers/IJSDR2307082.pdf
Volume 8
Issue 7,
July-2023
Pages : 576 - 583
Paper Reg. ID: IJSDR_207776
Published Paper Id: IJSDR2307082
Downloads: 000347202
Research Area: Engineering
Country: -, -, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
