pritesh-shrivastava / kaggle-ieee-fraud-detection

Bronze Medal solution to Kaggle competition hosted by IEEE-CIS to detect fraud transactions

kaggle-iee-fraud-detection

This repo includes my Bronze Medal solution (Top 10% finish) to the Kaggle competition hosted by IEEE-CIS to detect fraud transactions.

Stacking & Blending Strategy :

A. Easiest - Weighted blending of top scoring public kernels - https://www.kaggle.com/priteshshrivastava/ieee-cis-blend

B. Linear stacking - https://www.kaggle.com/aharless/simple-linear-stacking-lb-9730

C. Stacking with a meta model :

• Create a pipeline of kernels starting from data loading & reducing memory usage
• Create a hold out validation set
  • should we split this by time?
  • https://www.kaggle.com/kyakovlev/ieee-cv-options
• Create single models reading the split data and predicting on val & test sets
  • A. Catboost - https://www.kaggle.com/priteshshrivastava/ieee-pipeline-2-a-model-a-catboost-feat-sel
    • Errors due to missing values / data type
  • B. Random Forest - https://www.kaggle.com/priteshshrivastava/ieee-pipeline-2-b-model-b-random-forest
  • C. XGBoost - https://www.kaggle.com/priteshshrivastava/ieee-pipeline-2-c-model-c-xgboost
• Create a meta model that trains on val preds and predicts on test set
  • https://www.kaggle.com/priteshshrivastava/ieee-pipeline-3-stacking-with-meta-model
• [Optional] Blend this output with other kernels (?)
• useful kernels : https://www.kaggle.com/yw6916/lgb-xgb-ensemble-stacking-based-on-fea-eng