Posts Tagged “Air-B&B”

Predicting Air B&B User Travel

Table of Contents

Competition Description

I decided to have a go at the Kaggle competition for predicting Air B&B user’s future travel.

The Data

The data description can be found here. Below is a description in my own words.

It may be beneficial later to combine the test data and the training data, then randomly create a train, test, and validation dataset from the total sample.

Training Data

train_users.csv - the training set of users

The Sample Data

The sample data consists of three segmented data sets

Test Data

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

User Data

  • 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

Population Data

  • age_gender_bkts.csv - summary statistics of users’ age group, gender, country of destination

Country Data

  • countries.csv - summary statistics of destination countries in this dataset and their locations

First Examination of the Data

Predictions of the Data

We should convert the labels into dummy variables and individually predict for each country. Then join the results together.

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