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Code for a top 8% public and a top 11% private leaderboard ranking on the Kaggle HomeCredit competition. The goal was to predict whether a borrower would experience difficulties on repaying a specific loan.

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HomeCredit Kaggle Competition (top 8% public/11% private)

The top submission was the same on for the public and the private scores and consisted of a the arithmetic average of other submissions. It was created using blender.py (see ensemble folder).

The submission consisted of 5 runs created using run.py (see code folder) with different seeds and slight variations in the model parameters: learning rate, feature_fraction and regularization (L1 and L2).

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Code for a top 8% public and a top 11% private leaderboard ranking on the Kaggle HomeCredit competition. The goal was to predict whether a borrower would experience difficulties on repaying a specific loan.

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