GitHub repository for group 11
- José Domínguez Pérez
- Drago Be Lehn Jiménez
- Jaime José Monjo Rodríguez
- Patricia Ortiz Zamora
- Pablo Ángel Pérez Soto
We find Tourism domain an interesting field since we think it’s a growing economic sector and according to the experts, now is the best moment to research and invest on it because thanks to new technologies as mobile apps and to the webs of collaborative economy as airbnb is possible to change the way we interact with the information since now its is more specific, easy to access and finally is better for the users. Also we think traveling to other countries or even cities of your own country is a good way to know new cultures and traditions favoring the cultural enrichment of people.
- Hospitality Analytics
- SnapShot is a hospitality tool to analyze and process data from customers to hotels. This tool explain how to analyze the data and how to interpret them. Also this tool which helps to improve and make the data collection of guests to have better experience in hotels.
- The hunt for housing in NYC
- Unbox analíticas is a scrip developed by Erik Webb who is a data technology enthusiastic informatics. This scrip analyses data from different neighborhoods of NY helping people to find house in NY. It makes a comparative between prices and quality of the houses and flats, the user can find this info thought postal codes. Thanks to this study you can easily filter by number of rooms and the maximum price you want to pay.
- Airbnb vs Berlin
- This data driven project tries to answer whether sharing economy (such as airbnb) has contributed to a shortage in affordable housing in Berlin or not.
- Airbnb the Amsterdam story with interactive maps
- This data driven project gives us an overview of the type of airbnb flats that are available in Amsterdam; as well as some advice to the municipality of Amsterdam in order to find fraudulent hosting; and also gives the tourists some features about the flats such as the neighbourhood safety, the reviews, the avg price by date, etc. so that they can decide where they want to stay.
- Are there any different patterns when renting a shared flat depending on the country? (Not only in price, but in type of room, type of apartment, time of the year, etc)
- How is the location of a flat related to the price? How does the time of the year affects the disponibility and pricing of the airbnb flat?
- Is there a relationship between age and renting shared flats?
- Is the accomodation-sharing sector growing?
- How did the appearance of accomodation-sharing affected the hotel industry?
This dataset was gathered from Airbnb.com and the owner, a Drexel University’s student, Dgomonov (https://www.kaggle.com/dgomonov) shared it publicly on https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data. The dataset includes information about hosts, location and availability in New York for 2019. The data consists of one dataset (AB_NYC_2019.csv) which is structured in 49075 observations and 16 features. With this data set we can study the patterns when renting a shared flat depending on the country and whether the different features (such as location, time of the year, ...) affects disponibility and price.
This dataset was gathered from Airbnb.com and the owner Fuyutaru Suzuki (https://www.kaggle.com/fuyutaro) shared it publicly on https://www.kaggle.com/fuyutaro/tokyo-airbnb-detailed-open-data. He collected listings of apartments for the city of Tokyo recording the price depending on the date, the location, the number of rooms, the number of nights and even the reviews that users posted after the stay. The data consists of three datasets: calendar.csv which consists of the date and the price of the listings with 7,890,295 observations and 7 columns, listings.csv which contains all the information about the postings of the apartments with 21,617 observations and 106 features and reviews.csv which contains the data about the reviews user post on airbnb listings, this one has 576806 observations and 6 features. With this data set we can answer the first two questions.
The data was collected by Murray Cox and his inside Airbnb proyect, whose data is collected from publicly available information from the Airbnb site. The data can be downloaded from this link: https://www.kaggle.com/brittabettendorf/berlin-airbnb-data. The data has information about the apartments rented like the location, prizing, apartment type, and information about the hosts. The data set consists of 6 csv’s files. We will use the listings.csv that consists in 16 features and 22569 observations and has all the information mentioned above. With this data set we can answer questions about the different patterns when listing a airbnb flat depending on the country (by comparing this data set with one of the others), and questions about the different trends in each country like how the location affects the price, if the time of the year affects on the disponibility and pricing of an airbnb flat..