• To avoid fraudulent post for job in the internet, an automated tool using machine learning based classification techniques is proposed in the project.
  • Different classifiers are used for checking fraudulent post in the web and the results of those classifiers are compared for identifying the best employment scam detection model.
  • It helps in detecting fake job posts from an enormous number of posts.
  • Two major types of classifiers, such as single classifier and ensemble classifiers are considered for fraudulent job posts detection.
  • However, experimental results indicate that ensemble classifiers are the best classification to detect scams over the single classifiers.


  • The Existing system are predicted using classifiers that have been learned.
  • When detecting fraudulent job postings, the following classifiers are used-
  • A. Naive Bayes
  • B. Support Vector Machine
  • C. Logistic Regression


  • This system’s main purpose is to identify whether a job posting is genuine or not.
  • Job seekers will be able to focus entirely on legitimate job openings if fake job postings are identified and deleted.
  • In this system, we plan to use a Kaggle dataset that contains information on the job, including attributes such as job id, title, location, and department.
  • Then there’s data preprocessing, which involves removing things like trivial spaces, null entries, stopwords, and so on.
  • The data is provided to the classifier for predictions after it has been preprocessed and cleaned to make it prediction ready.
  • The proposed method is entirely composed of Artificial Intelligence approaches, which is critical to accurately classify between the real and the fake, instead of using algorithms that are unable to mimic cognitive functions.
  •  The three-part method is a combination between Machine Learning algorithms that subdivide into supervised learning techniques, and natural language processing methods.
  • Although each of these approaches can be solely used to classify and detect fake Job, in order to increase the accuracy and be applicable to the social media domain, they have been combined into an integrated algorithm as a method for fake news detection

Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements

  • •Hard Disk: Greater than 500 GB
  • •RAM: Greater than 4 GB
  • •Processor: I3 and Above

Including Packages


  • * Base Paper
  • * Complete Source Code
  • * Complete Documentation
  • * Complete Presentation Slides
  • * Flow Diagram
  • * Database File
  • * Screenshots
  • * Execution Procedure
  • * Readme File
  • * Addons
  • * Video Tutorials
  • * Supporting Softwares

Specialization =======================

  • * 24/7 Support * Ticketing System
  • * Voice Conference
  • * Video On Demand 
  • * Remote Connectivity
  • * Code Customization
  • * Document Customization 
  • * Live Chat Support


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