主要分为两个部分：特征选择和模型训练
- correlation_calculate.py完成特征相关性的计算，主要看与y相关性大小，以及不同特征之间的相关性。
- 选择与y相关性大的属性。
- 当两个非目标属性相关性大时。选择其一，防止模型过拟合。
- 主要选择决策树和随机森林模型。决策树可解释性更好，随机森林效果更好
- MCC作为得分指标，指标约接近1越好
- 在特征选择时，是比较灵活的。因此选择不同的属性进行实验，最后选择效果更好的属性组合
bank_data_analysis's Introduction
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