Fraudulent Job Posting Detection System

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Overview

Detecting fraudulent job postings is crucial in today's online job market. This web-based application leverages machine learning to assess the authenticity of job postings. By analyzing the job description, it predicts whether a listing is genuine or fraudulent, helping users make informed decisions.

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Key Features

• Multiple Machine Learning Models:
  • Predictions are generated using five different ML models: Naive Bayes, Support Vector Machine (SVM), Random Forest, XGBoost, and Logistic Regression.
• User-Friendly Interface:
  • A clean and responsive web design that allows users to easily input job descriptions and view results.
• Instant Results:
  • Predictions from all models are displayed in real time, with an aggregated final verdict.
• Keyword-Based Fraud Detection:
  • Identifies potentially fraudulent postings using specific keywords and patterns.

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Technology Stack

Front-End

• HTML & CSS – Provides structure and styling for a seamless user experience.

Back-End

• Flask – Manages web requests and integrates the machine learning models.
• Python – Handles data processing, model prediction, and logic implementation.

Machine Learning

• Algorithms Used: Naive Bayes, SVM, Random Forest, XGBoost, Logistic Regression.
• Libraries & Tools: Scikit-learn, XGBoost, Pandas, NumPy, Pickle (for model storage and loading).

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How It Works

1. The user inputs a job description into the web interface.
2. The system processes the text and runs it through five different machine learning models.
3. Individual model predictions are displayed alongside a final aggregated result.
4. If fraud-indicating keywords are detected, an alert is provided.

This project serves as a valuable tool for job seekers, recruiters, and organizations aiming to identify and prevent job fraud effectively. 🚀