论文标题
从人工智能推出的技术应用于银行客户偿付能力的预测：采用购物车类型决策树（DT）的情况
The application of techniques derived from artificial intelligence to the prediction of the solvency of bank customers: case of the application of the cart type decision tree (dt)
论文作者
论文摘要
在这项研究中，我们将源自人工智能技术得出的Cart型决策树（DT-CART）方法应用于银行客户偿付能力的预测，为此，我们使用了银行客户的历史数据。 However we have adopted the process of Data Mining techniques, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values as well as rows with empty columns, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our使用SPSS工具的购物车决策树方法。在完成建立模型（AD-CART）的过程之后，我们开始了评估和测试模型性能的过程，通过该过程，我们发现模型的准确性和精度为71％，因此我们计算出误差率，我们发现误差率等于29％，这使我们能够在准确的范围内确定我们的模型水平，并且可以很好地预测班级，并可以很好地确定该索引，从而确定了我们的确定性。
In this study we applied the CART-type Decision Tree (DT-CART) method derived from artificial intelligence technique to the prediction of the solvency of bank customers, for this we used historical data of bank customers. However we have adopted the process of Data Mining techniques, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values as well as rows with empty columns, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our CART decision tree method using the SPSS tool. After completing our process of building our model (AD-CART), we started the process of evaluating and testing the performance of our model, by which we found that the accuracy and precision of our model is 71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to conclude that our model at a fairly good level in terms of precision, predictability and very precisely in predicting the solvency of our banking customers.
