kaggle airbnb

Data Files

File Name Available Formats
countries.csv .zip (546 b)
age_gender_bkts.csv .zip (2.46 kb)
test_users.csv .zip (1.05 mb)
sessions.csv .zip (59.14 mb)
sample_submission_NDF.csv .zip (478.27 kb)
train_users_2.csv .zip (4.07 mb)

In this challenge, you are given a list of users along with their demographics, web session records, and some summary statistics. You are asked to predict which country a new user’s first booking destination will be. All the users in this dataset are from the USA.

There are 12 possible outcomes of the destination country: ‘US’, ‘FR’, ‘CA’, ‘GB’, ‘ES’, ‘IT’, ‘PT’, ‘NL’,’DE’, ‘AU’, ‘NDF’ (no destination found), and ‘other’. Please note that ‘NDF’ is different from ‘other’ because ‘other’ means there was a booking, but is to a country not included in the list, while ‘NDF’ means there wasn’t a booking.

The training and test sets are split by dates. In the test set, you will predict all the new users with first activities after 7/1/2014 (note: this is updated on 12/5/15 when the competition restarted). In the sessions dataset, the data only dates back to 1/1/2014, while the users dataset dates back to 2010.

File descriptions

  • train_users.csv – the training set of users
  • test_users.csv – the test set of users
    • id: user id
    • date_account_created: the date of account creation
    • timestamp_first_active: timestamp of the first activity, note that it can be earlier than date_account_created or date_first_booking because a user can search before signing up
    • date_first_booking: date of first booking
    • gender
    • age
    • signup_method
    • signup_flow: the page a user came to signup up from
    • language: international language preference
    • affiliate_channel: what kind of paid marketing
    • affiliate_provider: where the marketing is e.g. google, craigslist, other
    • first_affiliate_tracked: whats the first marketing the user interacted with before the signing up
    • signup_app
    • first_device_type
    • first_browser
    • country_destination: this is the target variable you are to predict
  • sessions.csv – web sessions log for users
    • user_id: to be joined with the column ‘id’ in users table
    • action
    • action_type
    • action_detail
    • device_type
    • secs_elapsed
  • countries.csv – summary statistics of destination countries in this dataset and their locations
  • age_gender_bkts.csv – summary statistics of users’ age group, gender, country of destination
  • sample_submission.csv – correct format for submitting your predictions

作者: eugene123tw

Some notes



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