.
how we applied it, we have used ‘imblearn’ lib for it: from imblearn.over_sampling import RandomOverSampler from sklearn.preprocessing import StandardScaler X = d[d.columns [0:20]] Y = d['labeldd'] # TRANSFORMING DATA scaler =StandardScaler() X = scaler.fit_transform(X) #APPLIED RANDOM OVER SAMPLER