Telenor Handset Model with Product-based Neural Networks

This repository explores a binary classification problem through...

1. feature selection
2. xgboost
3. wide&deep (Google)
4. complementary neural networks (cmtnn)
5. product-based neural networks (pnn) (main focus)

note 1: the relevant papers are included in the papers directory

note 2: the company data has not been included in this repository

For an overview, refer to the presentation directory.

The specific results obtained from different models are in the results directory.

label column: "TARGET_S_TO_S_APPLE"

• id=1: all features
  • dropped: ['Unnamed: 0', 'ID', 'MPP_NET_DISCOUNT_OTHER_FEE']
  • categorical and binary encoded as -1/1
  • standardized numerical

standardized numerical and categorical/binary encoded as 0/1:

• id=2: sklearn selectKBest w/ f_classif features
• id=3: all cat features
• id=4: all features selected in feature_selection directory
• id=5: f_classif features, cat vars without any encoding
• id=6: features from COLS_TERM (look in handset_model_current.py)

note: refer to features in the feature_selection directory

note: handset_model_original.py was written for python 2.7 while handset_model_current.py has been debugged (and further modified) for python 3.6