This project contains the work related to the first project from the second Term of Udacity's Data Science Nanodegree.
The aim of the project is to analyze the Seattle AirBnB dataset, that can be downloaded through this link.
The version of Python and the modules used in the analysis are the following:
- python: 3.7.3
- numpy: 1.16.2
- pandas: 0.24.2
- matplotlib: 3.0.3
- seaborn: 0.9.0
Based on Cross-Industry Standard Process of Data Mining (CRISP-DM), Seatle Airbnb dataset was analized. The aim of the analysis was to answer the following business questions:
- How does price varies during the year?
- Which neighbourhood of the city has better review ratings?
- Can we predict the price of a room based on its characteristics? If so, which of them are more important in order to predict the room price?
The files contained in the project are the following:
- A Jupyter notebook containing all the analysis performed on the datasets attached in the link above.