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The main problem is getting ready the info for Classification and Prediction. The classification rules might be applied to the new data tuples if the accuracy is considered acceptable. The classifier is constructed from the training set made up of database tuples and their related class labels. On this step, the classifier is used for classification. For instance, we can construct a classification mannequin to categorize bank loan purposes as both protected or dangerous, or a prediction model to foretell the expenditures in dollars of potential clients on computer equipment given their earnings and occupation. These labels are risky or safe for mortgage utility data and sure or no for advertising data. A bank mortgage officer wants to analyze the info with a purpose to know which customer (loan applicant) are dangerous or that are secure. With the assistance of the bank mortgage software that we have now mentioned above, let us perceive the working of classification. Here the check data is used to estimate the accuracy of classification rules

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