• Peethala Bharath
• Kothala Naga Rama Manikanta
• Marumudi Raghu Kiran
• Mullagiri Sasi Kumar

In India, finding a job is an extremely challenging endeavour, and choosing a professional path is an even bigger issue. The lives of job candidates are not well planned. Job seekers do not receive enough current information about employment prospects across industries. There is a need for an appropriate medium via which job seekers might obtain information on the anticipated number of openings, new hires, salaries, etc., in each industry. With the help these forecasts, job seekers may plan their career paths and get the qualifications needed for the position in the ensuing years. In order to forecast career prospects, our solution analyses user capabilities and proposes upcoming technology. We are using a hybrid strategy in order to increase accuracy. A mixed method approach was used to perform the investigation, including both statistical and predictive techniques. Some of the machine learning algorithms taken into consideration in this study are Decision Tree, Random Forest, and XG Boost. The main results from this study's use of these methods are also shown.

Job Role Prediction, Decision Tree, Random Forest, XG Boost, React JS, Node.js, and MongoDB

"JOB PREDICTIONS AND SKILLS SUGGESTIONS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.f641-f645, April-2023, Available :http://www.jetir.org/papers/JETIR2304584.pdf

"JOB PREDICTIONS AND SKILLS SUGGESTIONS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppf641-f645, April-2023, Available at : http://www.jetir.org/papers/JETIR2304584.pdf