京公网安备11010802038645号京ICP备20022436号-3京EDI证京B2-20220639区块链京网信备 1101082276223860002X号以下为卖家选择提供的数据验证报告：
数据描述
Berlin Airbnb Ratings and Reviews Overview
Quality Ratings of Properties and Experiences for Guests
By Andy Kriebel [source]
About this dataset
> This dataset contains information about more than 18,000 Airbnb listings in Berlin, covering ratings and reviews from guests. It includes data on listing URL, host name and URL, listing name and type, country code, location coordinates and exact address location details along with the amenities offered. Guests' comments are also recorded for each property. Detailed metric ratings are available for each individual property's overall score as well as its accuracy rating, cleanliness rating checkout process rating communication with host rating and value for money rating. In addition to the ratings provided by guests a lot of other relevant information like price per night number of bedrooms number of bathrooms square feet can be accessed through this dataset It also offers an indication of whether a given property is bookable without contacting the host (Instant Bookable) or if the Host is Superhost etc. This comprehensive dataset allows you to explore what factors make certain Airbnb properties in Berlin highly rated by guests!
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How to use the dataset
> This dataset provides an overview of ratings and reviews for Airbnb properties in Berlin, Germany. The data provided includes a range of metrics including review date, reviewer name, comments made by the reviewer, listing URL, listing name, host URL and host name, the date the host joined Airbnb and the response time to inquiries made by users. It also includes information on whether a property is listed as a Superhost property or not; neighbourhood and neighbourhood group; city; postal code; country code; latitude & longitude coordinates for each location; exactness of location details (if provided); room type(s) offered at each property; number of guests that can be accommodated; number of bathrooms & bedrooms available at each property (if known); number of beds (and size in square feet if known); price per night for rental rate at this particular Airbnb location;;number of reviews from previous guests;; first & last review received to-date;;overall rating on delivery expectations per guest experience;; accuracy rating given based off description ;;cleanliness rating given based on condition/cleaning experienced during stay ; check-in rating given based off ease/efficiency experienced regrading key collection or alternative access method specified by hosts prior to arrival point-in time;;communication ratings bestowed by guests with regard to pre-arrival communication systems. > > For forward planners , this dataset also indicates if Instant Bookable is possible providing convenience when booking stays . Lastly business travel ready gives greater insight as double verification that each unit is well equipped for professional travelers requiring specific set up conditions. > > To get started working with this data , you can use Excel or Tableau Hyper formats this allowing you analyze variables whether these would be through charts , pivot tables , graphs etc.. To get even more detail using Geojson file helps visualizing Neighbourhoods within Berlin enabling further exploration through GIS applications In conclusion whether personal holiday planning or analyzing regional trends inside Berlin’s housing market via AirBnb list --this dataset gives great insights into giving better rounded views upon potential options !
Research Ideas
> - Analyzing the reviews of each host to measure the success rate and customer satisfaction of Airbnb properties in Berlin. > - Creating a map of Airbnb properties in Berlin based on location, price, ratings and amenities to help potential guests make an informed decision about where to stay. > - Utilizing the data collected from each listing to create predictive models for prices and ratings of future Airbnb listings in different neighborhoods or cities. This can help hosts set appropriate rates for their listings and optimize their success rate on hosting platforms like Airbnb
Acknowledgements
> If you use this dataset in your research, please credit the original authors. > Data Source > >
License
> > > License: Dataset copyright by authors > - You are free to: > - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. > - Adapt - remix, transform, and build upon the material for any purpose, even commercially. > - You must: > - Give appropriate credit - Provide a link to the license, and indicate if changes were made. > - ShareAlike - You must distribute your contributions under the same license as the original. > - Keep intact - all notices that refer to this license, including copyright notices.
Columns
File: Airbnb Berlin.csv
| Column name | Description |
|---|---|
| review_date | Date of the review. (Date) |
| Reviewer Name | Name of the reviewer. (String) |
| Comments | Comments made by the reviewer. (String) |
| Listing URL | URL of the listing. (URL) |
| Listing Name | Name of the listing. (String) |
| Host URL | URL of the host. (URL) |
| Host Name | Name of the host. (String) |
| Host Since | Date the host joined Airbnb. (Date) |
| Host Response Time | Time it takes for the host to respond to inquiries. (String) |
| Host Response Rate | Percentage of inquiries the host responds to. (Number) |
| Is Superhost | Whether or not the host is a Superhost. (Boolean) |
| neighbourhood | Neighborhood of the listing. (String) |
| Neighborhood Group | Group of neighborhoods the listing is in. (String) |
| City | City the listing is located in. (String) |
| Postal Code | Postal code of the listing. (String) |
| Country Code | Country code of the listing. (String) |
| Latitude | Latitude of the listing. (Number) |
| Longitude | Longitude of the listing. (Number) |
| Is Exact Location | Whether or not the location is exact. (Boolean) |
| Property Type | Type of property (e.g. apartment, house, etc). (String) |
| Room Type | Type of room (e.g. private, shared, etc). (String) |
| Accomodates | Number of people the property can accommodate. (Number) |
| Bathrooms | Number of bathrooms in the property. (Number) |
| Bedrooms | Number of bedrooms in the property. (Number) |
| Beds | Number of beds in the property. (Number) |
| Square Feet | Size of the property in square feet. (Number) |
| Price | Price of the property per night. (Number) |
| Guests Included | Number of guests included in the price. (Number) |
| Min Nights | Minimum number of nights required for booking. (Number) |
| Reviews | Number of reviews for the property. (Number) |
| First Review | Date of the first review. (Date) |
| Last Review | Date of the last review. (Date) |
| Overall Rating | Overall rating of the property. (Number) |
| Accuracy Rating | Rating of the accuracy of the listing description. (Number) |
| Cleanliness Rating | Rating of the cleanliness of the property. (Number) |
| Checkin Rating | Rating of the check-in process. (Number) |
| Communication Rating | Rating of the communication with the host. (Number) |
| Location Rating | Rating of the location of the property. (Number) |
| Value Rating | Rating of the value for money offered. (Number) |
| Instant Bookable | Whether or not the property is instantly bookable. (Boolean) |
| Business Travel Ready | Whether or not the property is suitable for business travel. (Boolean) |
Acknowledgements
> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit Andy Kriebel.
Berlin Airbnb Ratings and Reviews Overview
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