Development of a machine learning model for predictive customer churn analysis
Table Of Contents
<p>Certainly! Here's a detailed abstract and table of contents for project topics #84, #85, #86, #87, and #88.<br><br>**Project Topic #84: Design and implementation of a blockchain-based digital asset management system**<br>. Introduction<br> - Background and significance of customer churn analysis<br> - Objectives of the project<br>2. Literature Review<br> - Overview of customer churn prediction models<br> - State-of-the-art machine learning algorithms for predictive analysis<br>3. Data Collection and Preprocessing<br> - Sources of customer data<br> - Data preprocessing and feature engineering<br>4. Model Development and Training<br> - Selection of machine learning algorithms<br> - Model training and validation<br>5. Performance Evaluation<br> - Evaluation metrics and criteria<br> - Comparative analysis of predictive models<br></p>
Thesis Abstract
<p> This project aims to develop a machine learning model for predictive customer churn analysis, with the goal of identifying and predicting customer churn in various industries such as telecommunications, e-commerce, and subscription-based services. The project will leverage historical customer data and advanced machine learning algorithms to build a predictive model that can forecast customer churn, enabling businesses to proactively address customer retention and loyalty strategies. <br></p>Thesis Overview
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