[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122335-en":3,"doc-seo-122335-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122335,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Dynamic Churn prediction using Machine Learning in Telecom Industries","Dynamic churn prediction for telecom industries using machine learning is developed through a structured thesis workflow: data collection, data cleaning, exploratory data analysis, and model building. The study defines churn management goals and problem scope, then applies multiple predictive approaches including logistic regression, neural networks, decision trees, and random forests. Model performance is evaluated with confusion matrix, ROC curves, and AUC to quantify classification quality and support selection of effective churn prediction methods for telecom datasets.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nDynamic Churn prediction using Machine Learning in Telecom Industries  \nA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science  \nBy  \nSai Krishna Pasbunat  \nMay 2023  \nCopyright by Sai Krishna Pasbunat 2023  \nThe thesis of Sai Krishna Pasbunat is approved:  \nMahdi Ebrahimi, PhD Date  \nKatya Mkrtchyan, PhD Date  \nRobert D McIlhenny, PhD, Chair Date  \nCalifornia State University, Northridge  \nAcknowledgments  \nI would like to express my gratitude and thank my thesis committee Chair, Prof. Robert DMcIlhenny, and committee members Prof. Mahdi Ebrahimi & Prof. Katya Mkrtchyan for their support and guidance throughout my work without which this thesis would have not been completed. I would like to sincerely thank my committee members for reviewing my thesis and certifying my work.  \nThis thesis would not be possible without help and knowledge from CSUN faculty who gave me academic and technical knowledge throughout my master’s degree.  \nLast but not least, I would like to express my sincere gratitude to my parents for always believing in me throughout academics and supporting me to excel in my career.  \nTable of Contents  \nCopyright..................................................................................................................................... ii  \nSignature Page ............................................................................................................................ iii  \nAcknowledgments ...................................................................................................................... iv  \nTable of Contents ........................................................................................................................ v  \nList of Figures ............................................................................................................................vii  \nList of Tables.............................................................................................................................. viii  \nAbstract………………………………………………………………………………………...ix  \nChapter 1: Introduction .............................................................................................................. 1  \n1.1 Overview and Issues ........................................................................................................ 1  \n1.2 Problem Definition .......................................................................................................... 1  \n1.3 Objectives ........................................................................................................................ 1  \n1.4 Proposed Solution ............................................................................................................ 2  \n1.5 Churn Management.......................................................................................................... 2  \n1.6 Data collection ................................................................................................................. 2  \n1.7 Clean data......................................................................................................................... 3  \n1.8 Prediction ......................................................................................................................... 3  \n1.9 Data Visualization............................................................................................................ 4  \nChapter 2: Literature Survey...................................................................................................... 5  \n2.1 Churn: Importance and Analysis ..................................................................................... 5  \n2.2 Techniques for cutting Churn .......................................................................................... 7  \n2.3 Methods of Churn Prediction ......................................................","cbCaiuWioli84Gps","https://ap.wps.com/l/cbCaiuWioli84Gps","pdf",779916,1,44,"English","en",105,"# Chapter 1: Introduction\n## Overview and Issues\n## Problem Definition\n## Objectives\n## Proposed Solution\n## Churn Management\n## Data collection\n## Clean data\n## Prediction\n## Data Visualization\n# Chapter 2: Literature Survey\n## Churn: Importance and Analysis\n## Techniques for cutting Churn\n## Methods of Churn Prediction\n## Problems in existing Solutions\n# Chapter 3: Methodology\n## Architectural Analysis\n## Telecom Dataset\n## Data cleaning/pre-processing\n## Exploratory Data Analysis (EDA)\n## Model Building\n## Logistic Regression\n## Neural Networks\n## Decision Tree\n## Random Forest\n## Evaluate and testing the Model\n## Confusion Matrix\n## ROC Chart\n## Area Under the Curve (AUC)","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis focuses on churn management in telecom industries by defining churn prediction as the core problem and outlining the objectives for predictive analytics.\"},{\"question\":\"Which machine learning models are used for churn prediction?\",\"answer\":\"The methodology builds models using logistic regression, neural networks, decision trees, and random forests.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Evaluation uses a confusion matrix for classification outcomes and ROC charts with the Area Under the Curve (AUC) to measure prediction quality.\"}]","Dynamic Churn prediction using Machine Learning in Telecom 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