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Scrapshut Project

Work for Scrapshut which was part of Design and Analysis of Software Systems Course at IIIT Hyderabad.

Client : Mounikesh Thadda, Founder of Scrapshut
Mentor TA : Mohit Chandra

Brief Problem Statement:

To develop a web platform for ScrapShut where users can come and rate different URLs and get reviews of other users on a particular URL. This web app aims to provide users with data on the credibility of the content provided by various websites, to curb fake news perpetration.

Project Overview:

We predict the genuineness of a website based on the majority of user ratings, and ML model trained using features like website’s scraped data, user's ratings/ reviews data from the website. Users further would be asked to tell which portions of the article made them think the article is fake and provide basis for their report . We aim to provide users with data on which websites are genuine and not genuine and protect the users malicious websites and fraud.

Development Environment

  • Editor - VS Code
  • Web framework - Django 3
  • Collaboration tools - Gitlab
  • Frontend - Angular.js
  • Documentation - Google Docs
  • Database – AWS, Heroku
  • Machine Learning - Keras, Tensorflow, Scipy
  • Web Scraping - BeautifulSoup
  • Language: Python○ Framework: Jupyter Notebook

Features Implemented:

  • Responsive WebApp
    • User login- Signup
    • Homepage
    • Dashboard
    • User Review form
    • Check URL verification
  • Scraping
    • Title, body and all associated links on that page scraped and stored.
  • ML model: for classification and prediction whether news is real/fake
  • LSTM
  • Passive Aggressive Classifier
  • CNN
  • Real time Classification
    • Prediction based on combination of User reviews for that URL and ML models predictions