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NAKAMA PetFinder Adoption Prediction

This respository contains our code for competition in kaggle.

27th Place Solution for PetFinder Adoption Prediction

Team: Y.Nakama, currypurin, atfujita, copypaste

Public score: 0.484(6th)
Private score: 0.43455(27th)

nakama's feature

  • Features from json files and text are almost same as public kernels
  • Features of Malaysia - GDP, Area, Population, HDI(Human Development Index)
  • First image features extraction by Densenet121
  • Var aggregation on basis of RescuerID to tell the model that if the RescuerID-Group treat their pets in the same quality or not
  • New health features of how many 1(good) or 3(Not Sure) in ['Health', 'Vaccinated', 'Dewormed', 'Sterilized']
  • New age feature that expresses if the pet is younger or older in its RescuerID-Group or overall by using 'Age' and 'RescuerID_Age_var'

curry's feature

The following features have high importance

  • First image features extraction by Densenet121 and MobileNet
  • second later image features extraction by Densenet121
  • groupby RescuerID

atfujita's feature

  • pure_breed(x)
  • image features extraction & SVD
  • text data SVD
  • groupby RescuerID

copypaste's feature

  • text data SVD and NMF
    • different tokenization, with/without stemming
    • countvectorizer / tfidfvectorizer
  • image features extraction by Densenet121 and MobileNet
  • image quality features by blur and NIMA

Models

  • LightGBM
  • XGBoost
  • CatBoost

Ensemble method

  • We performed ridge regression stacking using 9models(all GBDT).

Blog

To check a part of our challenges, see this blog (written in Japanese).