This is a Data Science project for the Udacity's Data Scientist Nanodegree. In this project, we dive into the Boston AirBnb Dataset, in order to answer 3 business questions:
- What was the housestay that earned the most? Is it in the most expensive street?
- What are the busiest times of the year to visit Boston? By how much do prices spike?
- Is it possible to predict the prices using the other informations?
The dataset covers the AirBnb listings from September/2016 till September/2017, and is available here.
We answer these questions by using statistical analysis and machine learning techniques, following the CRISP-DM methodology. Enjoy the insights!
- matplotlib 3.4.2
- numpy 1.20.3
- scikit-learn 0.24.2
- seaborn 0.11.1
The .csv and the archive.zip files contain AirBNB Boston data and are available here.
The nb1.ipynb file is the jupyter notebook with all the project's code.
The analysis conclusion was posted on Medium.
All the code in this project was written by the project's author. No code from others was used in this project.