My machine learning model for the See Click Predict Fix Kaggle competition
Python
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.ipynb_checkpoints
.gitignore
CV performance.txt
LICENSE
README.md
SeeClickFix.ipynb
estimator.py
requirements.txt
submission.txt

README.md

kaggle_seeclickfix

My model for the See Click Predict Fix Kaggle competition. Read the postmortem for a more detailed explanation of how it works.

Generating a submission

  1. Install dependencies pip install -r requirements.txt
  2. Put data in data/ directory
  3. Run model python estimator.py
  4. Upload submission.txt

Outcome

  • 147/533 on the public leaderboard
  • 151/533 on the private leaderboard

Puts me in the top 70%, which is not that great, but on the bright side, my public and private leaderboard scores being very close means I didn't horribly overfit during the competition.