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Code for a top 25% finish in Kaggle Springleaf competition
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Kaggle is a website that hosts online data science competitions. The goal of this competition is to identify consumers who are most interested in a direct mail offer from Springleaf. Participants mine anonymized customer information. For a full description click here.

I finished 445 out of 2225 participants. This was good for a "Top 25%" honor from Kaggle. In this competition I experimented with a few different machine learning algorithms. I found the gradient boosting to be the best.

I used the XGBoost R library and spent some time finding the best hyperparameters. The methodology I used was Random Search. I was convinced to use Random Search over grid search by this blog post on medium.

I separated the Kaggle training set into a smaller training set and a validation set. This ensured my results wouldn't overfit to the public leaderboard. In the end my final submission position was one place better than my public leadboard position.

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