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This project forked from palevas/santander-product-recommendation-8th-place
License: MIT License
Python 99.81%
Shell 0.19%
santander-product-recommendation-8th-place's Introduction
Santander Product Recommendation - 8th place
- make_data() step in main.py needs 30GB of memory but it can be optimized.
This code produces 3 submissions
- xgboost - 0.03061 public LB
- lightgbm - 0.03059 public LB
- xgboost+lightgbm - 0.03063 public LB
- place train_ver2.csv, test_ver2.csv to ../input/
- install pandas, scikit-learn, numpy, xgboost, lightgbm (or comment out lightgbm part) libs for python3
- set proper number of threads in engines.py
- ./run.sh
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