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Articles Journal using Data Mining | GP

MIT license Generic badge Website cv.lbesson.qc.to Open Source Love

forthebadge

Contributors

Youssof Kamal | Abdelhady Eslam | Mahmoud Hamed | Abdelrahman Naser | Abdelrahman Salah | Mohamed Sayed

Under supervision of

Prof. Marghany Hassan

Motivation

People sometimes need recommendations or impressions about someone specific products, trends and more. They won't waste a lot of time reading all opinions

Objective

From this point of view and to overcome this problem, we have built a web app using some data mining algorithms that helps analyzing and exposing content of review or blog post written about some product or trend that helps someone deciding if that content suitable for him or not

Details

The platform holds these features:

  • Making profile
  • Follow profiles system
  • Writing posts
  • Like/Dislike and commenting
  • Notifications
  • Unique News Feedback for each user
  • Full-Text search (words/tags)

Software Process Model

We have used Prototype Model. The basic idea in Prototype model is that: Prototype model is a software development model that's developed based on the currently known requirements. A throwaway prototype is built to understand the requirements. By using this prototype, the client can get an "actual feel" of the system, since the interactions with prototype can enable client to better understand the requirements of the desired system. Prototyping is an attractive idea for complicated and large systems for which there is no manual process or existing system to help determine the requirements. Prototypes are usually not complete systems and many of the details are not builtin the prototype. The goal is to provide a system with overall functionality

Model Advantages Disadvantages

Tools and technologies
  • DataBases MySQL - MongoDB
  • BackEnd Django
  • FrontEnd HTML - CSS - Bootstrap - JS - JQuery
  • Classifications Algorithms Logistic Regression - KNN - SVC - Decision Tree - Random Forests
  • Python Packages re - Pandas - Stopwords from nltk.corpus - word_tokenize from nltk.tokenize - PorterStemmer from nltk.stem.porter

ERD

ERD

TODO

  • News Feed Generation (PageRank Algorithm -social networks-)

documentation

Dataset

Languages

  • Python 51.6%
  • HTML 21.0%
  • JavaScript 20.8%
  • CSS 6.6%