[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126723-en":3,"doc-seo-126723-105":29,"detail-sidebar-cat-0-en-105":94},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},126723,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","A Survey and Implementation of Machine Learning Algorithms for Customer Churn Prediction - Abstract","Customer churn prediction supports businesses in identifying customers likely to leave and implementing retention measures to protect revenue and satisfaction. The study builds a machine-learning model from historical customer data by performing data engineering, handling missing values, encoding categorical variables, and preprocessing before evaluation. Multiple metrics are used, including accuracy, precision, recall, F1 score, and ROC AUC. Feature significance analysis highlights monthly fees, customer tenure, contract type, and payment method, and results show Soft Voting Classifier achieves strong performance with ROC AUC and 0.78 accuracy.","A Survey and Implementation of Machine Learning Algorithms for Customer Churn Prediction  \nDr. Snehal Rathi1, Atharva Puranik2, Vaishnavi Pophale3, Prajwal Kutwal4 , Vibhav Kulkarni5, Shaantanu  \nPratham6, Prof. Vikas Maral7  \n1Department of Computer Engineering  \nVishwakarma Institute of Information Technology, Pune, India  \n[snehal.rathi@viit.ac.in](snehal.rathi@viit.ac.in)  \n2Department of Computer Engineering  \nVishwakarma Institute of Information Technology, Pune, India  \n[atharva.22010869@viit.ac.in](atharva.22010869@viit.ac.in)  \n3Department of Computer Engineering  \nVishwakarma Institute of Information Technology, Pune, India  \n[vaishnavi.22010473@viit.ac.in](vaishnavi.22010473@viit.ac.in)  \n4Department of Computer Engineering  \nVishwakarma Institute of Information Technology, Pune, India  \n[prajwal.22010845@viit.ac.in](prajwal.22010845@viit.ac.in)  \n5Department of Computer Engineering  \nVishwakarma Institute of Information Technology, Pune, India  \n[vibhav.22010011@viit.ac.in](vibhav.22010011@viit.ac.in)  \n6Department of Computer Engineering  \nVishwakarma Institute of Information Technology, Pune, India  \n[shaantanu.22010051@viit.ac.in](shaantanu.22010051@viit.ac.in)  \n7Department of Computer Engineering  \nVishwakarma Institute of Information Technology, Pune, India  \n[vikas.maral@viit.ac.in](vikas.maral@viit.ac.in)  \nAbstract—Estimating customer traffic is an important task for businesses because it helps them identify customers who are most likely to leave and take preventative measures to retain them by improving customer satisfaction and further increasing their own revenue. In this article, we focus on developing a machine-learning model for predicting customer churn using historical customer data We performed engineering operations on the data, addressed the missing digits, coded the categorical variables, and preprocessed the data before evaluating it using a variety of performance indicators, including accuracy, precision, recall, f1 score, and ROC AUC_Score. Our feature significance analysis revealed that monthly fees, customer tenure, contract type, and payment method are the factors that have the most impact on forecasting customer churn. Finally, we conclude the best-performing model, the Soft Voting Classifier, implemented on the four best-performing classifiers with a good accuracy of 0.78 and a relatively better ROC AUC_Score of 0.82. Keywords—Customer churn prediction, Machine learning, Feature importance analysis, Gradient boosting, Business revenue.  \nI. INTRODUCTION  \nCustomer churn prediction is a critical problem for companies across various industries. It refers to the task of identifying customers who are likely to discontinue using a company's products or services. From a business perspective, customer churn poses significant challenges and can have a substantial impact on a company's profitability and growth. Retaining existing customers is the most important task for the survival of the business, which has become common sense in the business world. [15] . Customer acquisition requires substantial marketing and promotional efforts while retaining loyal customers can lead to repeat business and increased customer lifetime value. Therefore, accurately predicting customer churn allows companies to proactively address the underlying issues  \nand take appropriate measures to retain valuable customers. Customer churn prediction relies on analysing historical customer data, such as demographic information, transactional records, service usage patterns, and customer interactions. Different data types have different analysis capabilities. It is necessary to determine the most appropriate data for the type of analysis performed. Different datasets provide better metrics for different problems and services. [11] In order to find trends and signs that assist in identifying high-risk customers, the organization employs sophisticated analytics as well as machine learning approaches.  \nSome popular betting m","cbCainEvYHMHJANo","https://ap.wps.com/l/cbCainEvYHMHJANo","pdf",750155,1,"English","en",105,"# I. Introduction\n# II. Motivation\n# Machine-Learning Algorithms\n# Survival Analysis\n# Neural Networks\n# Ensemble Methods\n# Data Preparation and Preprocessing","[{\"question\":\"What problem does the document address and why is it important?\",\"answer\":\"It addresses customer churn prediction, aiming to identify customers likely to discontinue services so companies can take preventive retention actions and reduce profitability and growth risks.\"},{\"question\":\"What preprocessing steps are performed before model evaluation?\",\"answer\":\"The work performs data engineering operations, addresses missing values, encodes categorical variables, and preprocesses the dataset prior to running model evaluations.\"},{\"question\":\"Which features are found to have the strongest impact on churn forecasting?\",\"answer\":\"Monthly fees, customer tenure, contract type, and payment method are identified as the most influential factors in forecasting churn.\"},{\"question\":\"Which model performs best and how is it evaluated?\",\"answer\":\"The Soft Voting Classifier provides the best results, evaluated using accuracy and ROC AUC, with reported accuracy of 0.78 and ROC AUC around 0.82.\"}]","A Survey and Implementation of Machine Learning Algorithms for Customer Churn Prediction - 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