DataMiningCase:基于真实业务上手数据挖掘（银行流失预警）
DataMiningCase
流失预警模型（二分类），代码原型为本人在某银行做的流失模型，AUC：83%、召回率（覆盖率）：19.4%，精确率：85%（数据是外部数据/代码已脱敏）
基于真实业务上手数据挖掘（银行流失预警）：数据的处理、LightGBM、sklearn包（里面含有：GridSearchCV寻找最优参、StratifiedKFold分层5折切分、train_test_split单次数据切分等）、stacking模型融合、画AUC图、画混淆矩阵图，并输出预测名单。
告诉你：是什么（WHAT）、怎么做(HOW)、为什么这么做(WHY)。
注释覆盖率为80%左右，旨在帮助快速入门，新手级
项目涉及的如下：
商业理解
数据理解
数据处理（数据准备）
特征工程（数据准备）
正负样本特征线性图
RFECV（特征五折递归消除）
Importan
文件下载
资源详情
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