I am using Random Forest Regressor to predict inventory needs. The data I am using to train the model lists the total quantity picked for each product per date, but does not include rows where total quantity picked for a product on the specified date is 0. The model considers the features that allow it to take date into consideration. For details on the data being used see the example data below:
UPC day_ID month day_of_year day_of_week quantity_picked
0 0000000002554 7500.0 5 141 1 4.0
1 0000000002554 7503.0 5 144 4 2.0
2 0000000002554 7512.0 6 153 6 2.0
3 0000000002554 7527.0 6 168 9 2.0
4 0000000003082 7494.0 5 135 2 2.0
5 0000000003082 7495.0 5 136 3 2.0
6 0000000003082 7496.0 5 137 4 8.0
7 0000000003082 7497.0 5 138 5 4.0
8 0000000003082 7498.0 5 139 6 4.0
9 0000000003082 7499.0 5 140 0 9.0
10 0000000003082 7500.0 5 141 1 3.0
11 0000000003082 7501.0 5 142 2 5.0
12 0000000003082 7502.0 5 143 3 3.0
13 0000000003082 7503.0 5 144 4 8.0
14 0000000003082 7505.0 5 146 6 2.0
15 0000000003082 7506.0 5 147 3 7.0
Will the model be less accurate at predicting inventory needs because it is missing dates for items where quantity picked is 0? I have tried running the same model with the rows where quantity picked = 0 but the total number of rows changes from approximately 50k to 5 million and my computer literally can't handle it, it just freezes. Without the rows where quantity picked = 0, the model reports mean squared log error level of .39448 and runs successfully within 4 minutes 37 seconds.
Any guidance on if that data is necessary or not would be very much appreciated and/or advice on how to improve performance/accuracy of such a model.