Analyze and Predicting with Data Mining on Customer Churn Dataset

In the ever-evolving landscape of machine learning technology, the exploration and advancement of novel models are of paramount importance. This paper delves into the realm of customer churn analysis, leveraging data mining techniques to discern patterns and predict future trends. The study employs three distinct data mining approaches, segregating data into training and testing sets for a comprehensive analysis. Notably, XGBoost emerges as a standout performer, boasting an impressive accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$97.75%$</tex> . In stark contrast, Logistic Regression exhibits an accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">85.91%</tex> , and the Decision Tree method follows closely with an accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">86.36%</tex> . Significantly, this research emphasizes the efficacy of harnessing sophisticated machine learning algorithms, particularly XGBoost, in forecasting customer churn. By doing so, it advocates for the integration of proactive business strategies informed by these predictive insights.