# 27th place, +720 place shake up with NN model

Thank you Amex for hosting the amazing competition. Thank you to the whole Kaggle community and Kagglers who have attributed to this competition and provided the amazing opportunity to compete and learn!!
Abstract
I, fortunately, got the 30th place with 720 places shake-up. I think it is an interesting case since no one in the upper rankers has got the shake-up as much as I did. I'd like to share the experience of that for the record, and please comment if you guys have any ideas why this huge shake-up happened. Also, I'm looking for a team to join the Kaggle competitions. If there's anyone who considers it, feel free to contact me.
720 places Shake-up
I couldn't believe it when I woke up and saw on the private leaderboard that my submission was in 30th place. Last night when I checked it, the public leader board was 750th place😨. For your information, I am a university student in japan and there's 20 hours difference😃. I thought that the ensemble of NN + LGBM was the most likely solution, therefore I didn't really care about the public leader board though it struck me with a major surprise. It proves that we don't know what's gonna happen in the private leader board, and most of the participants were overfitting for the public leader board.
Solution overview
My solution was similar to his. The main idea was the 50%/50% ensemble of LGBM and NN Transformer.
[Mr. Chris Deotte's solution](https://www.kaggle.com/competitions/amex-default-prediction/discussion/347641)
In my case, I'm a broke university student, and I couldn't afford the computational resource. I used the free quote of Kaggle GPU and the resource was very limited. I attached Efficientnet with a transformer, and I could only use 60% of the competition data for the training session. That's one thing I regret. For LGBM models, I simply used the public LGBM notebook for the LGBM ensemble. I think the reason why I got this place was the NN model part. Although I had poor resources and couldn't do my best, I think that the transformer+efficientnet structure was the reason for my rank. That shows the infinite possibility of the NN!!
What I learned
I myself cannot believe that I got the top 0.6% place in this competition with this poor resource. Even though I could only use 60% of training data for NN, and public notebooks for the LGBM model, the private score was good. I think it means that this solution was one of the best answers for this competition. I'd love to see what happens if I could have used the full 100% of the training data for the NN models and properly train the LGBM models. Could I win the gold medal, maybe?
Closure
Thanks again to everyone who contributed to this competition. And again, I am desperately looking for a team. I'd love to learn from the team and I believe I can be helpful to the whole team. Please contact me if you can let me join your team😊