使用机器学习预测客户流失

V. Agarwal, Shwetkranti Taware, S. Yadav, Durgaprasad Gangodkar, A. Rao, V. Srivastav
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引用次数: 0

摘要

随着时间的推移,客户数量的逐渐但持续的减少被称为“客户流失”,这是一个经常在商业和金融部门使用的词。如果一家公司能够识别出最有可能离开的客户,他们就更有可能采取预防措施来留住这些客户。了解哪些客户在理论上和实际上最有可能在相对较近的将来更换银行,对银行是有利的。本文解释了如何使用机器学习算法来识别可能正在考虑转换金融机构的银行客户。本文展示了逻辑回归(LR)和朴素贝叶斯(NB)等机器学习模型如何通过使用年龄、位置、性别、信用卡信息、余额等数据,有效地预测哪些客户最有可能在未来离开银行。本文还使用了年龄、位置、性别、信用卡信息、余额等数据。此外,本文还演示了可能使用机器学习模型(如逻辑回归(LR)和朴素贝叶斯(NB))生成的概率预测。本研究的结果最终指向NB优于LR的结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Customer - Churn Prediction Using Machine Learning
The gradual but consistent decrease in the number of customers retained over time is referred to as “customer churn,” and it is a word that is frequently used in the business and financial sectors. If a company can identify the customers who are most likely to leave, they are more likely to take preventative efforts to keep those customers as clients. It is to the bank's advantage to have knowledge about which customers are theoretically and practically most likely to switch banks in the relatively close future. This article explains how to use machine learning algorithms to identify banking customers who may be considering switching financial institutions. This article demonstrates how machine learning models such as Logistic Regression (LR) and Naive Bayes' (NB) can effectively forecast which customers are most likely to leave the bank in the future by using data such as age, location, gender, credit card information, balance, etc. The article also uses data such as age, location, gender, credit card information, balance, etc. In addition, this article demonstrates the probabilistic predictions that may be generated using machine learning models such as Logistic Regression (LR) and Naive Bayes (NB). The findings of this research ultimately point to the conclusion that NB is superior to LR.
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