Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

Installation

The code was tested using Python version 3.9. For other necessary libraries please use requirements.txt

pip install -r requirements.txt

Project Motivation

For this project, it was of interest to investigate Seattle Airbn data and to better understand:

1. How does the type of the property influenced the price ?
2. How does the neighborhood have impact on price ?
3. How well can we predict the price for premisses?

File Descriptions

There is one notebook available here to explore and analyse the questions above. The notebook contains all necessary information about the dataset, approaches as well as visualization and analysis to better follow up the results.

Since this analysis is limited to price, only listings.csv will be taken into consideration. The Data Set rights belongs to Airbnb and could be downloaded here

Results

The main findings of the code can be found at the post available here.

Licensing, Authors, Acknowledgements

Must give credit to Airbnb. You can find the Licensing for the data and more useful information at Airbnb here or at the Kaggle here. Great thanks to Udacity for their contribution during the process.

Copyright (C) 2021 June

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