Data Mining is a promising field and is applied in multiple domains for its predictive capabilities. Data in the real world cannot be readily used for data mining as it suffers from the problems of multidimensionality, unbalance and missing values. It is difficult to use its predictive capabilities by novice users. It is difficult for a beginner to find the relevant set of attributes from a large pool of data available. The paper presents a Robust Prediction Model that finds a relevant set of attributes; resolves the problems of unbalanced and multidimensional real-life datasets and helps in finding patterns for informed decision making. Model is tested upon five different datasets in the domain of Health Sector, Education, Business and Fraud Detection. The results showcase the robust behaviour of the model and its applicability in various domains.

数据挖掘是一个有前景的领域，并且在多个领域中应用其预测能力。该文献提出了一种强大的预测模型，用于找到相关属性集，并解决了真实数据集的多维性和不平衡问题，帮助进行明智的决策。该模型在健康领域、教育、商业和欺诈检测领域的五个不同数据集上进行了测试，结果显示出模型的强大性和在各个领域的适应性。

多维度和不平衡数据集的稳健预测模型