The following code uses Kaggle's Boston Airbnb dataset to examine what features are most related to high Airbnb review scores and answer the following questions:
- Using random forest regression feature ranking, what are the top predictors of overall review scores?
- When we use a multilevel model, which of these features are statistically significant?
- Out of the statistically significant features, which have large effect sizes? What are the directions?
It first uses a random forest regressor to rank features on aggregated data. Based on these rankings it selects features with > mean importance, and runs multilevel models on them to look at significance as well as directionality and effect size.
Clone repository: git clone https://github.com/madkehl/Airbnb
If the repo is cloned, the Jupyter notebook should run smoothly from start to finish, and contains all necessary code.
- listings.csv and reviews.csv: These are taken directly from the above link
- TopScoringAirbnbs.ipynb This notebook standalone contains all the code necessary to run the project.
- txt_df: is a file that contains adjective/adverb count and word count for the text columns specified in Udacity-1. It is contained as a separate file because the code takes a long time to run, however the syntax exists in Udacity-1 to recreate it (cell 12).
pandas, numpy, seaborn, re, statistics, sklearn, nltk, plotly, statsmodels
The most important predictors of Airbnb scores tended to be amenities (WiFi, AC, Laptop-Friendly Workspace, Hair Dryer). Location in Boston might be important (no individual neighborhoods were extremely significant, however based on latitude and longitude, perhaps south west neighborhoods receive higher reviews. Hard to say with current info). Aside from this superhosts tended to receive higher reviews.
Madeline Kehl (mad.kehl@gmail.com)
- Kaggle
- Udacity Data Science Nanodegree
Copyright (c) 2020 Madeline Kehl
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