Customer attrition is an increasingly pressing issue faced by many insurance providers today. Retaining customers who purchase life insurance policies is an even bigger challenge since the policy duration spans for more than twenty years. Companies are eager to reduce these attrition rates in the customer-base by analyzing operational data. Data mining techniques play an important role in facilitating these retention efforts. The objective of this study is to analyse customer attrition by classifying all policy holders who are likely to terminate their policies. These customers who are at high risk of attrition can then be targeted for promotions to reduce the rate of attrition. Data mining techniques such as Decision trees and Neural Networks are employed in this study. Models generated are evaluated using ROC curves and AUC values. Our research also adopts cost sensitive learning strategies to address issues such as imbalanced class labels and unequal misclassification costs.
This research uses association rule generation and classification techniques to support decision making, by considering a data set of diabetes type 1 & type 2 patients. There are advanced and reliable data mining techniques which leads to the discovery of unseen and useful information. The main focus of this research is to identify the yet undiscovered decision factors of diabetes which increases the possibility of the onset of diabetes, as well as to identify the undiscovered consequences of diabetes.Through the data mining analysis, gender female is identified as a major decision factor of high FBS (Fasting Blood Sugar) level. Furthermore wheezes and edema were identified as unknown side effects of diabetes.
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