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When you have an imbalanced class in a classification problem–the model tends to disregard the lower class sample. This is a problem because the models accuracy can be high but the errors for a single class would be very high. A good metric to look at would be the f1-score. To fix an issue like this, you need to use SMOTE (synthetic minority oversampling ...


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If the dataset has imbalance class (i.e. class 1 has 90% and class 0 has 10%) try to add some techniques like Up sampling or Down sampling in pre-processing stage to predict the sophisticated customer selection for that particular deal.


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