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)

• 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'

The following features have high importance

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

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

• 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

• LightGBM
• XGBoost
• CatBoost

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

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