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Abstracted NLP-driven spatial exploration and understanding of crowd-engendered particular styles of the districts of Seattle.

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Seattle Senses: Taking a walk around the city, through data.

Seattle Senses aims to understand the crowd-engendered particular styles of each district of Seattle, making use of Natural Language Processing and Text-Mining techniques applied on Airbnb listings data.

Overview

Visiting new cities during COVID-19 times can result in an impossible task, but nothing restricts you from virtually having a taste of Seattle by making use of data visualisation and statistics. This project employs the Seattle Airbnb Open Data in order to have a sneak peek into the Airbnb activity in Seattle.

Undertaking

Different things have been uncovered in this project, such as the characteristic words for each neighbourhood of Seattle and how different adjectivised descriptions influence a district's mean listing price.

Contributing

Feel free to fork this repository and use the research of this project to build upon. If you have any question, please open an issue to discuss your inquiry.

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MIT

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Abstracted NLP-driven spatial exploration and understanding of crowd-engendered particular styles of the districts of Seattle.

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