This is an individual course project for STAT 456 : Multivariate Analysis

Professor : Dr. Wei-Yin Loh

In this project, to build a recommendation system, principal components analysis and clustering analysis will be used. Recommendation system is used by multiple IT companies to make personalized recommendation for their customers and help them to make a decision for their products. Lodging company, such as, Airbnb and Trivago are overlooking power of the recommendation system. To deal with this problem, the recommendation system will be built based on Airbnb data in New York city. Recommendation system will be powered by clustering analysis, including k-means clustering and model-based clustering will be performed on two different plots. One plot will be generated with coordinate data of the accomodation and other being generated with principal components of different datasets. While making plots based on principal components, analysis will be done to obtain deeper understanding of the dataset.

To go further with this project Deep Factorization machine will be used in order to improve the recommendation system built based on unsupervised learning methods.

The final report can be found at:

• Final Report

All .csv data in Data are from AirBnB research website

Codes for this project can be found at:

• Main
• Word cloud

All visualization that was created with R and python are in Image files

Written by alexdseo