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Generate Synthetic samples Machine Learning Models will have poor performance in case of labeled data from minority class for highly imbalanced data. For highly imbalanced data scenarios, over and under sampling techniques can be used with the aim to have more balanced training data and build a robust machine learning model. In case of undersampling the majority class, we can expect that useful information having impact on building our binary classification machine learning model can be lost. So in our example we will focus on oversampling techniques. A very common and widely used approach synthesizing new data examples is called -Synthetic Minority oversampling technique- or SMOTE, which creates artificial samples of the minority class through interpolation. This over sampling method does not rely on replacements, it is based on feature similarities between existing minority examples. This approach can balance finally the class distribution but does not provide any additional information to the model. SMOTE has various variances for different data types, the main package behind SMOTE is Imb Learn With our dataset we will use SMOTE with mixed continuous and categorical values, which is an extension of SMOTE to SMOTE N.C. SMOTE-N.C. is Synthetic Minority Over sampling Technique for Nominal and Continuous. With this unique library, the most common occurring category of nearest neighbours to a minority class point is taken. First, we define categorical features and resample the minority labelled class. And finally check the balanced distributed labelled synthetic data.