Today�s real word data posses class imbalanced datasets of which most of the instances are converged to a single class and few others instances assigned to smaller. To build classification models from imbalanced datasets is much harder and has a challenging task in solving Machine-learning algorithms. Traditional classification algorithms use similarity property in category minor and major classes. Real world imbalanced data can be classified accurately into class of major and poor class accuracy of minor by developing models which uses Machine learning algorithms to find the correct accuracy. We propose a framework evaluation model which classifies fraud detection on real-world imbalance data sets for imbalance dataset to achieve good majority class accuracy and poor minor accuracy. The evaluation of the framework is done using 7 Machine Learning Classification Algorithms with advanced balanced techniques. Many of the ideas are taken from the literature study. Most of our work is done using advanced balanced techniques using financial datasets of primary class to identify the accuracy and improve it using the entire algorithm and tested with results. The results obtained from shows that recall and precision are effectively used for measuring the developed model for imbalanced datasets. We also suggest use data mining techniques in simplifying balance datasets before, solving huge imbalance class problems using our framework

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