  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.
