EECS E6893 Big Data Analytics final project
The number of international students in the United States is increasing these years. It is a little hard for new international students to find roommates and apartments which fit their needs. So, this project is going to build a roommates and apartments platform for new students. We designed our own frontend website and will use cluster algorithm like euclidean distance, cosine similarity and K-Means to get our recommendation, the result will show on the frontend back. Besides, we will compare algorithm we used to find which one is better for our platform.
Roommates: a dataset contains 13+ features and 3000+ rows including first name, last name, email address, uni, gender, hobbies, etc. Apartments: a dataset contains 10+ features and 40000+ rows including name, address, location, distance, etc.
You can see the demonstration of our project through this link: https://www.youtube.com/watch?v=ccGrH4LN8sA
./
├── .gitignore
├── README.md
├── data
│ ├── airbnb.csv
│ ├── countries.txt
│ ├── majors.txt
│ ├── roommates.csv
│ └── shcools.txt
├── docs
│ ├── 6893_progress_paper.doc
│ ├── figs
│ │ ├── fig1arch.png
│ │ ├── fig2flowchart.png
│ │ ├── fig3frontinfo.png
│ │ ├── fig4frontresult.png
│ │ └── flowchart.png
│ ├── progress_report.docx
│ └── ~$93_progress_paper.doc
├── final_BDA
│ ├── __init__.py
│ ├── __pycache__
│ │ ├── __init__.cpython-38.pyc
│ │ ├── search.cpython-38.pyc
│ │ ├── search2.cpython-38.pyc
│ │ ├── settings.cpython-38.pyc
│ │ ├── urls.cpython-38.pyc
│ │ ├── views.cpython-38.pyc
│ │ └── wsgi.cpython-38.pyc
│ ├── asgi.py
│ ├── search.py
│ ├── settings.py
│ ├── urls.py
│ └── wsgi.py
├── manage.py
├── src
│ ├── create_table_gbp.py
│ ├── knn+cosine_similarity.ipynb
│ ├── pull_from_gbq.py
│ └── push_to_gbq.py
├── static
│ ├── css
│ │ ├── postPatt.css
│ │ └── resultsPatt.css
│ ├── js
│ │ ├── button.js
│ │ └── display.js
│ └── picture
│ ├── Columbia.jpg
│ └── columbia_university.jpg
└── templates
├── post.html
└── results.html