The objective of this project is to predict the loans that will be charged-off/default. The dataset is taken from Lending Club with 52 descriptive features with loans over a period of 5 years from 2007-2011.
The dataset is imbalanced with fully paid(positive class) to charged off(negative class) ratio of 85:15. Three techniques are implemented to balance the data: Under-sampling, over-sampling and using weighted model.
Three algorithms are used to train the data: Random Forest, XGBoost and Neural network using Pytorch and CUDA. The XGBooost and Neural network are trained using GPU. The models are evaluated using AUC, F1 score and confusion matrix.
The best model for each of the technique are:
| Technique | Algorithm | AUC | F1 score | Confusion Matrix |
|---|---|---|---|---|
| Undersampling | XGBoost | 0.98 | Charged Off: 0.98 Fully paid: 0.98 |
TP:2156 FP:14 TN:365 FN:14 |
| Oversampling | Neural Network | 0.99 | Charged Off: 0.97 Fully paid: 1.00 |
TP:2158 FP:12 TN:371 FN:8 |
| Weighted Model | XGBoost | 0.98 | Charged Off: 0.99 Fully paid: 0.99 |
TP:120 FP:1 TN:130 FN:5 |
pandas, numpy, matplotlib, seaborn, chart_studio, sklearn, xgboost, torch, torchvision.
Using conda install orca to render static plots from plotly. Command to install:
$ conda install -c plotly plotly-orca