This is my first Kaggle competition. The best model is in the first top 7%. You can read the high-level explanation in Thai here (http://kittinaradorn.com/first_kaggle_competition/).

Instruction:

1. Download dataset from https://www.kaggle.com/c/santander-product-recommendation/data, unzip and put them into input folder.

2. Create preprocessed data by running all cells in the following notebooks

• MakeJuneExtraData.ipynb
• MakeJuneExtraDataMulticlass.ipynb
• MakeDataMulticlass2.ipynb
• MakeTestDatawithpast3.ipynb

3. Choose model to create prediction from the following notebooks:

• Baseline model: run MostProbableProductRecent2.ipynb
• Basic logistic regression: run CollaborativeFiltering.ipynb
• A bit more advanced logistic regression: run LogisticRegression2.ipynb
• Simple XGBoost model: run XGBmulticlass.ipynb
• XGBoost with feature engineering: run XGBmulticlass_withpast5.ipynb
• Basic Neural Network: run Keras1.ipynb
• Ensemble model: run Ensemble6_Keras1_XGB5_popular.ipynb

Special thanks to anokas (for starter script), BreakfastPirate (for contributing important insight to the community) and other people in the kaggle forum!