Rental Clustering & Price Prediction Project (R)

Author

Kaizhong (Mike) Mu

Published

September 1, 2022

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Keywords

PCA, Hierarchical Clustering, Unsupervised Learning, Data Visualization, Variable Selection, Automated program.

Highlight

I designed an automated R program that evaluates neighborhood quality around a user’s desired lodging location using unsupervised learning techniques. The motivation came from a personal experience: I once booked a vacation rental online that looked perfect in photos, but upon arrival, I realized I had paid four-star hotel prices for a place in a poorly maintained and unsafe neighborhood. This program aims to help users avoid similar situations by providing a quality rating based on the approximate location of a property.

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Citation

BibTeX citation:
@online{(mike)_mu2022,
  author = {(Mike) Mu, Kaizhong},
  title = {Rental {Clustering} \& {Price} {Prediction} {Project} {(R)}},
  date = {2022-09-01},
  url = {https://kaizhongmu.github.io/project/dsp/4/},
  langid = {en}
}
For attribution, please cite this work as:
(Mike) Mu, Kaizhong. 2022. “Rental Clustering & Price Prediction Project (R).” September 1, 2022. https://kaizhongmu.github.io/project/dsp/4/.