Airbnb Kaggle Competition: New User Bookings

This repository contains the code developed for the Airbnb's Kaggle competition. It's written in Python, some in the form of Jupyter Notebooks, and other in pure Python 3.

The code produces predictions with scores around 0.88090% in the public leader-board, enough to be in the best 5% participants(0.001% behind the best) and 0.88509% in the private leader-board(0.0018% behind the winner)

The entire run should not take more than 4 hours(thanks to the parallel preprocessing) in a modern/recent computer, though you may run into memory issues with less than 8GB RAM.

Feel free to contribute to the code or open an issue if you see something wrong.

Description

New users on Airbnb can book a place to stay in 34,000+ cities across 190+ countries. By accurately predicting where a new user will book their first travel experience, Airbnb can share more personalized content with their community, decrease the average time to first booking, and better forecast demand.

In this competition, the goal is to predict in which country a new user will make his or her first booking. There are 12 possible outcomes of the destination country and the datasets consist of a list of users with their demographics, web session records, and some summary statistics.

Data

Due to the Competition Rules, the data sets can not be shared. If you want to take a look at the data, head over the competition page and download it.

You need to download train_users_2.csv, test_users.csv and sessions.csv files and unzip them into the 'data' folder.

Note: Since the train users file is the one re-uploaded by the competition administrators, rename train_users_2.csv as train_users.csv.

Main Ideas

1. The provided datasets have lot of NaNs and some other random values, so, a good preprocessing is the primary key to get a good solution:

   • Replace -unknown- values with NaNs
   • Clean age values
   • Extract day, weekday, month, year from date_account_created and timestamp_first_active
   • Add number of missing values per user
   • General user session information:
     • Number of different values in action, action_type, action_detail and device_type

2. That kind of classification task works nicely with tree-based methods, I used xgboost library and the Gradient Boosting Classifier that provides along scikit-learn to make the probabilities predictions.

Requirements

To replicate the findings and execute the code in this repository you will need basically the next Python packages:

• NumPy
• Pandas
• Jupyter
• SciKit-Learn
• Matplotlib
• Unbalanced Dataset

Resources

• XGBoost Documentation - A library designed and optimized for boosted (tree) algorithms.
• Pattern Classification - Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.

License

Copyright © 2015 David Gasquez Licensed under the MIT license.