Paper Contents
Abstract
The study suggests a mechanised method utilising machine learning-based categorization algorithms to stop fake online job ads. These days, it's common practise for companies to publish their open positions online, where they may be easily accessed by prospective employees. However, con artists may be taking advantage of people looking for work by offering those jobs in exchange for payment. Many people fall for this scam, which causes them to lose a lot of money. By performing an exploratory data analysis on the data, we can distinguish between legitimate and fake job ads. A machine learning strategy is utilised to identify fake comments, which makes use of several classification techniques. Using historical examples of both fake and real job postings, the system would teach the model how to correctly categorise future job advertisements. To begin tackling the difficulty of identifying scammers on job advertisements, supervised learning algorithms as classification techniques can be investigated. It uses two or more machine learning algorithms to determine which is best at predicting whether or not a job ad's headline is authentic.
Copyright
Copyright © 2023 K. SWAPNA. This is an open access article distributed under the Creative Commons Attribution License.
