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.