https://github.com/davidgasquez/kaggle-airbnb

:earth_africa: Where will a new guest book their first travel experience?
https://github.com/davidgasquez/kaggle-airbnb

jupyter kaggle machine-learning

Last synced: 12 months ago
JSON representation

:earth_africa: Where will a new guest book their first travel experience?

• Host: GitHub
• URL: https://github.com/davidgasquez/kaggle-airbnb
• Owner: davidgasquez
• License: mit
• Created: 2015-12-09T19:45:55.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2017-05-04T07:22:14.000Z (about 9 years ago)
• Last Synced: 2025-04-12T03:52:54.480Z (about 1 year ago)
• Topics: jupyter, kaggle, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 4.04 MB
• Stars: 53
• Watchers: 5
• Forks: 26
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Airbnb Kaggle Competition: *New User Bookings*

[![Code Health](https://landscape.io/github/davidgasquez/kaggle-airbnb/master/landscape.svg?style=flat)](https://landscape.io/github/davidgasquez/kaggle-airbnb/master)

This repository contains the code developed for the [Airbnb's Kaggle

competition][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.

[competition]: https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings

## 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*][rules], the data sets can not be shared. If

you want to take a look at the data, head over the [competition][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`.

[rules]: https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings/rules

## 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](http://www.numpy.org/)

- [Pandas](http://pandas.pydata.org/)

- [Jupyter](http://jupyter.org/)

- [SciKit-Learn](http://scikit-learn.org/stable/)

- [Matplotlib](http://matplotlib.org/)

- [Unbalanced Dataset](https://github.com/fmfn/UnbalancedDataset)

## Resources

- [XGBoost Documentation](https://xgboost.readthedocs.org) - A library designed

and optimized for boosted (tree) algorithms.

- [Pattern Classification](https://github.com/rasbt/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.