Homeowners and potential home buyers often rely on Zillow’s Zestimate to estimate the market price of houses. The Zestimate predicts a home’s value using publicly available housing data and a proprietary machine learning formula. It serves as a starting point for homeowners who are looking to sell their property and enables home buyers to gain a better understanding of the market.
Such a feature would be similarly useful to predict the market rental price of a short-term rental unit on Airbnb. A guest could compare the price of an Airbnb listing to its predicted price to know if they are getting a good deal. A host could easily determine a good price to list their unit.
- Clone the repo
- Create a new python environmet using the command:
conda env create zillowbnb - Activate zillowbnb by using the command:
conda activate zillowbnb - Install the required python packages using:
pip install –r requirements.txt
Zillowbnb/
|- data/
|- Seattle.joblib.dat
|- Seattle_low.joblib.dat
|- calendar_price_averages.csv
|- clean_listings.csv
|- clean_predicted.csv
|- reviews_sa_summarized.csv
|- seattle_merged.csv
|- docs/
|- Component_Specification.pdf
|- Functional_Specification.pdf
|- Technology Review.pdf
|- zillowbnb.jpg
|- examples/
|- User_Guide.pdf
|- zillowbnb/
|- submodule/
|- __init__.py
|- constants.py
|- convert_to_matrix.py
|- detect_outliers.py
|- example.py
|- get_calendar_summary.py
|- get_cleaned_listings.py
|- get_data.py
|- host_predict.py
|- price_prediction.py
|- sentiment.py
|- train_model.py
|- test/
|- __init__.py
|- submodule_path.py
|- test_convert_to_matrix.py
|- test_detect_outliers.py
|- test_get_calendar_summary.py
|- test_get_cleaned_listings.py
|- test_get_data.py
|- test_host_predict.py
|- test_price_prediction.py
|- test_sentiment.py
|- test_train_model.py
|- __init__.py
|- zillowbnb.py
|- .coveragerc
|- .travis.yml
|- LICENSE
|- README.md
|- requirements.txt
|- setup.py
Use the User Guide to get you started.