ارائه مدلی مبتنی بر الگوریتم جنگل تصادفی و بهینه‌سازی جایا برای پیش‌بینی ریزش مشتریان بانکی | ||
| مدیریت مهندسی و رایانش نرم | ||
| مقاله 9، دوره 9، شماره 2 - شماره پیاپی 17، مهر 1402، صفحه 132-148 اصل مقاله (2.1 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2024.9541.1174 | ||
| نویسندگان | ||
| سپیده چهره* 1؛ علی سرآبادانی2 | ||
| 1دانشجوی دکترا رشته مهندسی فناوری اطلاعات گرایش سیستم‌های چند رسانه‌ای، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران. | ||
| 2دانشجوی دکترای مهندسی فناوری اطلاعات(IT) گرایش تجارت الکترونیک، گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه قم | ||
| چکیده | ||
| ریزش مشتری یک اصطلاح مالی است که به از دست دادن مشتری اشاره دارد؛ امروزه با توجه به تعداد زیاد بانک‌ها، ریزش مشتریان از یک بانک به بانک دیگر تبدیل به دغدغه جدی برای بانک‌های مختلف شده است. بنابراین در این مقاله که برای مشتریان یک بانک گردآوری شده است، می‌توان با توجه به رفتار و ویژگی‌های مشتریان قبل از وقوع ریزش، به شناسایی مشتریانی که احتمال ریزش بالایی دارند پرداخت و با ارائه پیشنهادهایی آن‌ها را حفظ نمود. در بازاریابی همه بر این امر توافق دارند که حفظ یک مشتری از جذب یک مشتری جدید بسیار کم هزینه‌تر است، از این رو این مقاله به معرفی فازهای مختلف رویکرد پیش‌بینی مشتری ریزشی با کمک یادگیری ماشین پرداخته است. روش پیشنهادی ترکیبی از الگوریتم‌های جنگل تصادفی و بهینه سازی جایا می‌باشد و ریزش مشتری را بر اساس ویژگی‌های مختلف مشتری مانند سن، جنسیت، جغرافیا و موارد دیگر پیش-بینی می‌کند. نتایج حاصل از مدل پیشنهادی در مقاله به ترتیب در معیارهای Precision ، Recall و Accuracy برابر مقادیر91.41 درصد، 95.66 درصدو 93.35 درصد می‌باشد. | ||
| کلیدواژه‌ها | ||
| الگوریتم جنگل تصادفی؛ بهینه سازی جایا؛ ریزش مشتری؛ یادگیری ماشینی | ||
| عنوان مقاله [English] | ||
| A model based on random forest algorithm and Jaya optimization to predict bank customer churn | ||
| نویسندگان [English] | ||
| Sepideh Chehreh1؛ Ali Sarabadani2 | ||
| 1Ph.D. Student in information technology engineering specializing in multimedia systems, Faculty of Engineering and Technology, Qom University, Qom, Iran. | ||
| 2Phd student of Information Technology (IT) Engineering, e-commerce , Department of Computer Engineering and Information Technology, Faculty of Technology and Engineering, Qom University | ||
| چکیده [English] | ||
| Customer churn is a financial term that refers to the loss of a customer; Today, due the large number of banks , the loss of customers from one bank to another has become a serious concern for different banks. Therefore, in this article, which has been compiled for the customers of a bank , it is possible to identify customers who have a high probability of falling by considering the behavior and characteristics of the customers before the fall occurs and to keep them by providing suggestions. In marketing, everyone agrees that keeping a customer is much less expensive than attracting a new customer, this article introduces the different phases of the approach of predicting customer churn with the help of machine learning. The proposed method is a combination of random forest algorithms and Jaya optimization, and customer dropout is based on different characteristics. Customer like age, Gender, graphs and cases It predicts more . The results of model in the article are 91.41%, 95.66% and 93.35% respectively in Precision , Recall and Accuracy criteria. | ||
| کلیدواژه‌ها [English] | ||
| customer churn, machine learning, random forest algorithm, site optimization | ||
| مراجع | ||
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