Project: Kaggle - BNP Paribas Cardiff Claims Management

Objective: This project attempts to automate personal insurance claim approval mechanism by means of using data intelligence. Given a set of completely anonymous attributes we were to model the probability of a claim to be safely approved without needing any further manual scrutiny. We made extensive use of Bayesian/ Likelihood encoding of different order feature interactions to improve the predictive power of our model. We ended up adopting a three-layer ensemble architechtural design for our final model.

Time Frame: Feb, 2016 - Apr, 2016

Evaluation Metric: Binary Crossentropy/ logarithmic loss

Team: Bishwarup Bhattacharjee - Daniel FG - Jeremy Walthers

Total Participating Teams: 2947

Final Standing: 2nd

Minimum Logloss achieved: 0.42079

Models used:

Xgboost (R/ Python)

NN (Keras)

Random Forest (R/ Sklearn)

Extreemly Randomized Trees (R/ Sklearn)

Support Vector Machines (R)

Vowpal Wabbit

Factorization Machine (C++)

Regularized Greedy Forest (C++)

h2o GBM/RF