Artificially Increasing Training data I am trying to predict sales quantity based on attributes of the item.Sales are aggregated by week wise and prediction is also done by week wise.I am having large number records with zero sales quantity than compared to positive sales(for 20 positive sales 250 zero sales records are there).I want to increase my training data by adding small values to sales quantity and combining with total records.
At present I am using RANDOM FOREST and NEURAL NETWORK .I am not getting any good results
Please correct me if anything is not considerable
I have tried SMOTE for categorical response variable scenario but never tried on Continuous response variable but I think that should not matter. You can run oversampling or undersampling based on the predictor variables. If you are using R, you can use DMwR library and use the SMOTE function. In Python you got to use imblearn.over_sampling.SMOTE