Accuracy analysis for prediction of customer attrition using novel XGBoost over decision tree in big data platform

In order to accurately estimate customer attrition across a range of businesses, this work aims to apply Novel XGBoost (eXtreme Gradient Boosting) over Decision Tree. The study conducted a performance analysis utilizing the Novel XGBoost comparative Decision Tree method on a large data platform to predict customer attrition as accurately as possible. Each of the two algorithms’ groups has ten samples, and the statistical analysis was done using the SPSS software with an alpha value of 0.8 and a beta value of 0.2. The G-Power value of 87% was taken into consideration while predicting the dataset’s significance value using the graph value. Compared to Decision Trees (72.208%), XG- Boost Algorithms (90.456%) identify customer attrition with the highest accuracy. After that, an independent sample test was carried out with SPSS, and the results showed that a novel XGBoost is statistically significant compared to the current method. The significance value obtained was 0.0085 (p