LATFinders: ARA Project

Project Idea:

To support student’s decision to choose a course based on other student's reviews, so they can:

• Get overall and detailed view about courses.
• Compare courses from different universities and make it easier to choose a course.
• Predict the future trend of course overall ratings based on previous students’ ratings.

Youtube video shows our project: ARA Demo

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Project Architecture:

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Tools, Algorithms and Libraries used:

• Database:

  • Beautifulsoup to scrape Studycheck website
  • Render as Cloud application hosting
  • Postgres as Database

• Frontend Development:

  • Angular
  • Library Plotly for Data Visualization

• Backend Development:

  • Flask: REST API
  • Machine Learning Algorithm: SARIMA Model
  • Plotly Library for creating Data Visualization

• Version Control System:

  • Git

• Code Hosting Server:

  • Github

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Visualization: (Screenshot)

• Home Page:
• Course Analysis:
• Compare Courses:
• Trend Prediction:

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Installation

Install Python, NodeJS and Angular Use the pip package manager pip to install all the required dependencies.

• sqlalchemy
• flask_sqlalchemy
• flask
• python-dotenv
• Flask-Cors To install dependencies:

pip install -U flask-cors

Go to the folder of frontend and do

npm install

Set Database URI as Environment variable E.g in windows:

export SQLALCHEMY_DATABASE_URI_DEV="databaseURI..." 

Make sure that the port of backend in app.py is the same as the port that is configured in frontend. You can set it in frontend in the file: ./frontend/src/environments/environment.prod.ts

Run Backend:

cd src
python app.py

Run Frontend:

cd frontend
npm install
ng serve

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LATFinders members:

• Mahdyar Safarianbarmi
• William Kana Tsoplefack
• Nikolas Gur
• Yusra Abdulrahman
• Ghazal Abbasi

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