Kent Barter

Project Background

Airbnb is a booking service for vacation rentals in which vacationers can book from an available list of user offered vacation rental services. This platform is the most popular service of its type and is used in over 193 countries and features more than 2 million individual properties. This open market concept has given vacationers the opportunity to evaluate many options based on the individual’s wants and needs.

Each year Airbnb releases a series of datasets which contain information on vacation rental listings for several major cities around the world. In this case we will be evaluating the 10 June, 2024 listing report from Toronto. Our goal is to determine from this data what feature of a listing best contributes to its price and if we can use these features to properly predict the price of this dataset’s contained listings.

In this first intersection we will be introducing the dataset. This includes a description of its variables as well as an evaluation into the underlying structure of the data. We have done this in three stages description, data cleaning and outlier evaluation.

Goals:

* Investigate the features responsible for listing cost.

* Properly produce and evaluate a prediction model for listing cost.

Acknowledgement

This dataset is part of Airbnb Inside, and the original source can be found here. Date Compiled (10 June, 2024)

For dataset description, We have used Glossary of Terms from here as well as our understanding from airbnb site.# Airbnb-Toronto-Price-Prediction

Airbnb-Toronto-Price-Prediction