This project aims to predict the legality of Airbnb's in Vancouver. Using Airbnb data collected from InsideAirbnb (data/yvr_listing_data.csv).
Through a multiple logistic regression model, we are able to predict the legality of an Airbnb with an accuracy of 0.90.
The repository has the following main jupyter notebooks:
-
data_cleaning.ipynb: This notebook is used to clean the data and prepare it for analysis. The cleaned data is saved as yvr_listing_data_cleaned.csv. -
Logistic_Regression_Assumptions.ipynb: This notebook is used to check the assumptions of the logistic regression model. Modifications from checking the assumptions are saved to yvr_listing_data_cleaned_fixed.csv. -
sm_model.ipynb&sklearn_model.ipynb: These notebooks contain the code for the models used to predict the legality of the Airbnb's. -
EDA.ipynb: This notebook contains the code for the exploratory data analysis on the key variables discovered by our logistic regression model.
The repository also contains a src.py file which contains the functions used in the notebooks.
All data is stored in the 'data' folder.
