/Identification-of-users-with-high-credit-risk

Identification of users with high credit risk base on pca,Feature Engineering,Feature importance extraction based on classification decision tree,xgboost,bagging and Pearson correlation matrix.

Primary LanguageJupyter Notebook

Identification-of-users-with-high-credit-risk

Actions on the basis of the account according to the risks of information, information and transaction behavior, through data analysis, from the account features, equipment operation and transaction frequency, trade time, trade amount, regional distribution characteristics of dimensions such as mining, supervised machine learning model, effectively identify high-risk trading account.

data

train with label and test without label.

skill

PCA,Feature Engineering,Feature importance extraction based on classification decision tree,xgboost,bagging and Pearson correlation matrix.

Result

out-bagging.csv and out-xgboost.csv for the predict of credict risk for the users in test-without-label data.