Research on earnings' drivers for AirBNB-listed properties in Florence, Italy (until 2019). You can find here the final report. The project follows the guidelines CRISP-DM.
We want to investigate how we could invest capital into a flat to rent for turists. To do so, we want to try to answer the following points:
- Understand Airbnb businesses in Florence: popular areas and highest occupancy, and mostly consolidated vs new listings over time.
- Relate the listing offer to our business orientation goal: listings are of any types, whereas we are interested in exploring small-to-medium-sized flats
- Investigate the characteristics of flats in relation to prices (how many guests can accept, utilities etc.)
The process is executed in a Jupyter Notebook, where you can find:
- Data preparation. Data is taken from Inside Airbnb, file: listings.csv.gz. Date 22 June 2019). Missing values and feature selection is processed to simplify the dataset and run an exploratory experiment.
- Data analysis. Prices and
- Data modelling
- Data visualization
The Notebook requires Python 3.7 installed on your machine to run. Dependencies are include in the Anaconda environment file requirements.yml