Clustering Comparison of Customer Attrition Dataset using Machine Learning Algorithms

In the dynamic landscape of today's business environment, customer retention is a critical factor for sustainable growth and success. This project focuses on developing and comparing machine learning models for customer attrition and churn prediction using state-of-the-art algorithms such as Affinity, Birch, KMeans, and Agglomerative Clustering. The objective of this study is to evaluate the effectiveness of these clustering algorithms in identifying patterns and predicting customer churn. Using a dataset containing historical customer data, the project aims to create prediction models that can assist firms in proactively addressing possible churn concerns and implementing targeted retention efforts. The study is significant because it can give businesses predictive analytics capabilities to enhance their customer relationship management strategies, by figuring out which customers are likely to leave. In addition, the project intends to execute label selection by evaluating each feature individually according to its impurity score and to perform cluster classification to choose the optimal cluster according to its metrics. The study concentrates on the crucial machine learning methods for calculating client churn. This can include improving customer service, offering loyalty programs, or adjusting pricing strategies.