iRenter: Taipei City rental value searching & prediction engine
Introduction
Two team members and I built a Taipei City rental value searching & prediction engine (iRenter) for the CSX 4001 final project. We demonstrated iRenter in an R Shiny app. In the team, I was responsible for data collection, preprocessing, and visualization. Below is the iRenter R Shiny app.
>> iRenter Website Entry <<Following are the open datasets that we collected and used:
- Actual Selling Price of Real estate
- Locations of Mass Rapid Transit Stations
- Locations of parks
- Locations of bus stops
- Locations of convenient stores
Following are the machine learning model that we tried:
- Non-linear SVM
- ExtraTree
- RandomForest