EDA and Modelling on the old Airbnb Dataset
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.
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
A prelimnary analysis and broad eyeballing of the Airbnb data. The first file includes preprocessing and basic univariate analyses. The second one has a bit more indepth multivariate analyses. This is by no means exhaustive and I plan to upload more files which are of a more deep-dive types.
A single file (for now) which has your run-ofthe-mill XGBoost models, with an accuracy of about 86.87%.