stevenlol / otto

Code for Kaggle Otto Production Classification Challenge

Otto

Code for 85th place (out of 3514) in Kaggle Otto Production Classification Challenge (private leaderboard).

• Link: https://www.kaggle.com/c/otto-group-product-classification-challenge

Feature engineering (not all are used in final ensemble)

• Sum of all features for each row
• Variance of all features for each row
• Number of filled features for each row
• Operational features (+, -, *, /) created on top 20 features (does not work all the time)
• Transforming features with mean-standarization (new feature = original feature - column mean)

Models

• XGBoost
• Neural Networks (using Lasagna and H20; only Lasagna model was used for final ensemble)
• randomForest

Software

• R 3.1.3

• R packages:

  • doParallel
  • Caret
  • xgboost
  • party
  • glmnet
  • dplyr

• Python 2.7

• Python libraries:

  • Lasagna
  • numpy
  • scipy
  • theano