Skip to content

songgc/display-advertising-challenge

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Display Advertising Challenge

Description

This is the code was written for the Kaggle Criteo Competition of CTR prediction.

Since the data are highly sparse, the basic methodology is to use logistic regression with appropriate quadratic/polynomial feature generation and regularization to make sophisticated and over-fitting-tractable models. Vowpal Wabbit is the major machine learning software used for this project. Since the data size is challenging in terms of my personal workstation (a single quad-core CPU), the techniques of feature selection and model training are selected based on the trade off between performance and CPU/RAM resource limit.

Dependencies and requirements

Please note that the code was written for my personal learning and practice in new features of Java 8 and Python 3.4 in Ubuntu 14.04. The code cannot be run in early versions of these two languages or other OSs. Compatibility is not considered here.

  • Java 8
  • Python 3.4
  • Maven 3
  • Redis 2.8
  • Pandas 0.14
  • Vowpal Wabbit 7.7
  • Java-based open source projects: (Maven will install them automatically)
    • guava 17.0
    • jedis 2.5.1
    • commons-lang3 3.3.2

How to run

  • Copy train and test data file (train.csv, test.csv) to data folder
  • Compile the Java code by
$ cd display-ad-java
$ mvn package # or mvn install
  • Make sure a redis instance running at localhost:6379
  • Set the path of binary vw (VW_BIN) in run.sh, such as
export VW_BIN=/path/to/vw/binary
  • Finally,
$ cd work
$ ../run.sh

About

Criteo/Kaggle Competition of CTR prediction

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published