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Airbnb: New User Bookings | Kaggle Competition

My Solution to Airbnb: New User Bookings Competition on Kaggle

Introduction

What is Airbnb?

  • Airbnb is an online platform where people can list or rent properties for short-term use. Be it an entire home, a spare bedroom or even a sofa, anyone looking to earn some profit can promote their space on Airbnb.
  • New​ users​ on​ Airbnb​ can​ book​ a place​ to​ stay​ in​ 34,000+ cities​ across​ 190+​ countries.
  • The platform was launched in 2008.

Problem Category

Multiclass Classification

Problem Statement

Given a list of users along with their demographics, web session records, and some summary statistics, the objective is to predict the top 5 travel destinations in decreasing order of relevance for each of the new user.

Dataset

Stats

  • Observations:
    • Training Users: 213,451
    • Test Users: 62,096
    • Session Records: 10,567,737
  • Features:
    • users: 15
    • sessions: 6

Files Description

  • train_users_2.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.

Libraries Used

  • SciKit-Learn
  • NumPy
  • Pandas
  • SciPy
  • XGBoost
  • Visualization:
    • Matplotlib
    • Seaborn

Model Approach

  • XGBoost Classifier
  • Extended Hyperparameter Tuning
  • Cross Validation
  • NDCG Evaluation Metric

Model Performance

  • Training Score: 83.07%
  • Test Score: 88.13%

View the notebook on Kaggle.