DOI:10.20894/IJBI.
Periodicity: Bi Annual.
Impact Factor:
SJIF:5.065
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Now a days due to the, the announcement of new jobs has become problem in todays world. Therefore, the wrong predictions of such fraudulent jobs will be a big concern for everyone. Predicting misplacement poses many challenges. With advancements dangerous activations. Agencies and scammers use various methods to lure job seekers with resources from fake job sites. Our goal is to reduce the number of these false and fraudulent attempts. The goal research accurately detect and estimate fake emails. To design this system, we used different data mining techniques and classification algorithms such as gradient-boosting classifiers, random forest classifiers, and decision trees in a Python-based environment.
