kaggle_ops
My best submission to the Kaggle competition "Online Product Sales", ranked 21th over 366 teams (score: 0.57885).
http://www.kaggle.com/c/online-sales/leaderboard
Requirements:
0) NumPy, scikit-learn
1) Pandas, http://pandas.pydata.org/ , just to create the initial dataset or to explore it.
2) joblib, http://packages.python.org/joblib/ , if you want to run blender_parallel.py , i.e. to use all your cores with GradientBoostingRegressor().
3) The "Online Product Sales" trainset/testset files to be put in the subdirectory "data/".
Usage:
"python explore.py" to have a look to the quantitative variables and to decided which of them to put in the logscale.
"python create_dataset.py" , creates and save the dataset from the initial trainset/testset files.
"python blender.py" computes the actual submission (simple blending of GradientBoosting).
"python blender_parallel.py" computes the actual submission splitting the computation on as many cores as you like.