kavinmugil2006

kavinmugil2006 / fraud-job-detector

Public

This project uses NLP and Machine Learning to detect fake job postings. It analyzes job descriptions using TF IDF and DistilBERT models and provides instant predictions through a Streamlit web application.

27
0
69% credibility
Found Feb 02, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A web application that lets users paste job descriptions to check if they are likely fraudulent using trained detection models.

How It Works

1
📰 Spot a Suspicious Job Ad

You see an online job posting that feels off and want to check if it's a scam.

2
💻 Get the Free Detector Tool

Download the simple tool from the sharing site and follow easy steps to set it up on your computer.

3
🚀 Start the Checker App

Open the tool and it launches a friendly web page right on your screen.

4
📋 Paste the Job Details

Copy the job description text and drop it into the app's input box.

5
🔍 Hit Analyze

Click the button and watch as it scans for scam signs instantly.

✅ Get Your Safety Verdict

See clear results like 'Likely Genuine' or 'Likely Scam' with percentages, so you can apply confidently or walk away safely.

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Star Growth

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AI-Generated Review

What is fraud-job-detector?

This Python project github repo builds a fraud detector job tool that analyzes job descriptions to flag scams using NLP and machine learning. Users paste text into a Streamlit web app for instant predictions on whether a posting is genuine or fraudulent, complete with probability scores. It solves the pain of spotting fake job ads that prey on desperate seekers, offering a quick check via TF-IDF models and DistilBERT options.

Why is it gaining traction?

Its dead-simple Streamlit interface delivers real-time results without setup hassle, standing out as a project github python example for ML demos. Developers grab it for the plug-and-play prediction endpoint feel, plus easy local training on job datasets. Low barrier hooks tinkerers wanting fraud analysis without building from scratch.

Who should use this?

Data science students prototyping NLP classifiers for text fraud detection. Indie job board devs adding quick scam filters to user submissions. Python scripters automating personal job hunts with a Streamlit dashboard.

Verdict

Grab this as a solid starter for fraud detector job experiments—23 stars and basic docs show it's early-stage, but the 0.7% credibility score flags missing pieces like pre-trained models. Fork and mature it for production use.

(178 words)

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