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Predict Airbnb nightly price

Authors: Tomas Beuzen, Florencia D'Andrea, Mds Octocat and Tiffany Timbers

About

Here we attempt to build a regression model, using the k-nearest neighbors algorithm, which uses characteristics of an Airbnb property and host (price, bedrooms, host response rate, etc.) to predict the nightly price of the property. This model could help prospective and exisiting hosts determine how to price their new or existing property. Our current model has a prediction error, as measured by root mean square prediction error (RMSPE), of about $70. This model, is useful, however it could use some improvement given that the nightly prices in our data set range from $0-$1000, and the median nightly price is $119.

The data we used to build our model contains all the active Airbnb listings for Vancouver, Canada from April 2021. The data was collected from http://insideairbnb.com/.

Report

The analysis report can be found here.

Usage

We use a Docker container image to make the computational environment for this project reproducible. There are two ways we document how to do this. The first, which is ideal for those wishing to reproduce our results, is how to reproducibly execute the project non-interactively. The second, which is ideal for project developers and collaborators, is how to interactively run, edit and explore the project in Jupyter Lab.

1. How to reproducibly execute the project non-interactively

First, clone this GitHub repository and in in the terminal, navigate to the root of this project. Next run the following in your terminal:

docker run --rm \
  -p 8888:8888 \
  -v /$(pwd):/opt/notebooks/predict-airbnb-nightly-price \
  ubcdsci/predict-airbnb:v0.1.0 \
  jupyter nbconvert --to notebook --execute predict-airbnb-nightly-price/notebooks/airbnb_analysis.ipynb

2. How to interactively run and explore the project in Jupyter Lab

To interactively run Jupyter lab inside the ubcdsci/predict-airbnb Docker container (which is useful for project developers and collaborators):

  • in terminal, navigate to the root of this project repository

  • type the following in terminal:

    docker-compose up -d

  • once the container has launched, users need to copy the URL that looks like http://127.0.0.1:8888/lab?token=d9704724bf0267d3d9262698ffbb88123633f8c8f4b1a305 into their web browser to access Jupyter Lab

  • Next, in Jupyter lab, navigate to, and open notebooks/airbnb_analysis.ipynb and click Kernel > Restart and runall to run the entire analysis.

  • when done working, type docker-compose down to remove the dangling container.

Dependencies:

R version 4.1.1, Jupyter and R packages listed in environment.yml.

License Information

This project is offered under the Attribution 4.0 International (CC BY 4.0) License. The software provided in this project is offered under the MIT open source license. See the license file for more information.