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NTDS - TEAM 27 - Airbnb price prediction from an host point of view

Github repository of the final project of the class EE-558, A Network Tour of Data Science. This readme contains an abstract with our problem definition, the different librairies that are used for the project. The code can be found in the jupyter notebook AIRBNB.ipynb.

ABSTRACT

AirBnB has become an easy way to find accommodations for more or less long stays in big cities all around the world. With the number of tourists constantly increasing, more people than ever before are using the AirBnb system. Instead of focusing on the travellers, we found interesting to guide attention towards accomodation providers.

"How much money can I ask for my own flat ?"

This is the question we will try to answer while focusing on the New York City AirBnB Open Data dataset. AirBnb offers a lot of possibilities for the travellers to find the flat that suits their needs. Nevertheless, finding information about the price of your flat depending on a precise area might be more difficult. Providing information to the accomodation provider is the aim of the product we developped.

LIBRAIRIES USED IN THE PROJECT

Here is a list of the librairies used in the project :

• Pandas
• Numpy
• Scipy
• Networkx
• Sklearn
• Matplotlib
• utm
• pygsp

SOURCE OF THE DATA

Here is the link to download the dataset on Kaggle : https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data

LICENSE

MIT License

Copyright (c) 2020 Furter Samuel, Mosser Paul, Lugeon Sylvain, Hartmann Florian

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.