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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

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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

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Avoid neural networks for weekly sales data. There simply is not enough data points to make it work.

The technique works great with image or video data (it can recognize numbers, or cat videos) but you need something in the neighborhood of 200,000 data points to train your network.

Imagine the cost and complexity of getting 200,000 weekly data points (that is something between 3,000 and 4,000 years of collecting).

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  • $\begingroup$ Thank you @Jindra Lacko Can you specify which model best suits for my data $\endgroup$ Aug 8, 2017 at 10:53
  • $\begingroup$ Suggest a model without seeing your data? :))) lm() is usually a good start, and a baseline to improve from. $\endgroup$ Aug 9, 2017 at 15:46

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