Data Analysis and prediction on Kaggle dataset: Credit Risk Dataset
This repo provides algorithms for data preprocessing and model training for Credit Risk problem
CatBoostClassifier is used as prediction model in this repo
Dataset: https://www.kaggle.com/datasets/laotse/credit-risk-dataset

Before running preprocess.py, please change the path of 'credit_risk_dataset.csv' to your local path
It provides data visualisation of the dataset, also including the preprocess functions before training the model

Before running train.py, please change the path of 'credit_risk_dataset.csv' to your local path
This repo used CatBoostClassifier as prediction model
Default device is GPU, please comment the line of 'task_type="GPU",' if using CPU

Most of the method in Kaggle are using full dataset for training and evaluation dataset
After considering the unbalance on ground truth may lead to bias on the model, This repo will resample and balance the groundtruth data where number of (groundtruth == 1) equals to number of (groundtruth == 0)
Currently the model can reach ~86.81 of accuracy in balanced dataset, while reach ~93% in full dataset