Data analysis of Airbnb website history for the city of Rio de Janeiro, Brazil. The goal of this project is to explain and show step by step a data preprocessing procedure with a real world problem.
You can also check the repo Medium Post.
All the required Python libraries to run the python code are in requirements.txt file. Run pip install -r requiremts.txt to install all the dependencies.
The code should run with no issues using Python versions 3.*
The goal of this project was to retrive the maximum amount of information from a real world dataset. The dataset contains information from January 2010 until January 2020.
The project was focused on answering quentions through data. Based on the information presented on each of the datasets, it was possible to better understand:
- How do prices change based on location (geographical pricing) ?
- Does the host response rate affect his scores ?
- Airbnb growth through the years in Rio de Janeiro
- How do prices change based on the number of accommodates ?
There is one notebook, Data_Analysis.ipynb, to showcase work related to the above questions. The notebook explains step by step each procedures that were used answer each one of the questions above.
The CSV folder contains both of the dataset that were used:
- Detailed Listings data for Rio de Janeiro
- Detailed Review Data for listings in Rio de Janeiro
The Images folder contains an image of the price vs location study, this image is also shown inside the jupyter notebook.
The dataset was downloaded from indesideairbnb. The data behind the project is sourced from publicly avaiable information from the Airbnb site.