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首页> 外文会议>International Conference on System, Computation, Automation and Networking >Customer Churn Prediction In Telecommunication Industry Using Random Forest Classifier
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Customer Churn Prediction In Telecommunication Industry Using Random Forest Classifier

机译：随机森林分类器的电信行业客户潮汐预测

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摘要

Nowadays data has become the important aspect in each and every field. In this the data about the telecommunication industry is collected and then the raw data is classified into churn and the non churn customers. The churn customers are one who periodically uses the same resource signals and non churn customers are one who utilizes the resources based on the services provided by the particular company. In existing system they uses the algorithm called LDT and UDT which train the system blindly with too many attributes which are not necessary for the computation. So it takes much time to train the system and the accuracy is not that much efficient and it achieve the performance about 84 percent. But this much of performance is not that much efficient for an organization to provide convincible services. So in order to resolve this problem in existing system we proposing the system with an efficient algorithms known as Random Forest Classifier and Support Vector Machine which selects the important attribute which increases the performance of the system and by implementing these two algorithms we can achieve the efficiency of about 95 percent. Because this efficiency in performance will ensure the company to provide the appropriate services to retain the non churn customer within the organization to sustain the Telecommunication industry.
机译：如今数据已成为每个领域的重要方面。在此，收集了关于电信行业的数据，然后将原始数据分为流失和不流搅拌客户。 Churn客户是定期使用相同资源信号的人，并且不流失客户是利用基于特定公司提供的服务的资源的客户。在现有系统中，他们使用称为LDT和UDT的算法，其中盲目地用太多属性训练系统，这些属性是不需要计算的。因此，培训系统需要很多时间，准确性并不高，达到大约84％的性能。但这种表现卓越的效率并不高，为组织提供令人信服的服务。因此，为了解决现有系统中的这个问题，我们提出了一种具有随机林分类器的有效算法的系统和支持向量机，它选择了增加系统性能的重要属性，并通过实现这两个算法，我们可以实现效率大约95％。由于这种性能效率将确保公司提供适当的服务，以保留本组织内的非流畅客户以维持电信行业。

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