Reference no: EM133751381 , Length: word count:2000

Data Analytics

Learning Outcome 1: Exhibit comprehension of the fundamental principles of data analysis, including theoretical frameworks and methodologies applicable to business and social contexts.

Learning Outcome 2: Exhibit a high level of expertise in assessing data analytics methods critically to solve real-world problems.

Learning Outcome 3: Exhibit the ability to critically draw from and evaluate research and data at an industry and organizational level to formulate effective strategies and plans.

Learning Outcome 4: Exhibit a high level of written and verbal communication skills relevant to the planning, design, and implementation of a technical solution.

Assignment - Individual Assignment

Overview
This assignment involves comprehensive research on the use of machine learning methods for predicting customer churn. Students are required to find various machine learning algorithms, explore their application in customer retention strategies, and evaluate their effectiveness using academic literature. The assignment concludes with a detailed report that combines all the research findings, organized in a clear way to demonstrate a thorough understanding of machine learning in customer churn prediction.

Problem Statement
This is an individual assessment task. Each student is required to submit a report of approximately 2000 words. This report should consist of:
Abstract: Summarizes your findings.

Introduction: Explains machine learning algorithms, customer churn prediction, why this topic is interesting and important for other researchers, and the history of the topic.

Literature Review: Surveys the latest techniques from academic research papers regarding customer churn prediction. The aim of this part of the report is to demonstrate a deep and thorough understanding of existing machine learning techniques for customer churn prediction.

Dataset Description: Describes one of the datasets that other researchers used for customer churn prediction, including the construction of datasets and the features identified for classification.

Methodology: Depicts the workflow of customer churn prediction, describing the process of conducting customer churn prediction.

Conclusion: Summarizes the key findings of your research and suggests potential future work.

This unit requires you to use APA system of referencing.