I have a test dataset and train dataset as below. I have provided a sample data with min records, but my data has more than 1000's of record. Here if you see E is my target variable which I need to predict using an algorithm. It has only four categories like 1,2,3,4. It can take only any of these values.Training Dataset:A    B    C    D    E1    20   30   1    12    22   12   33   23    45   65   77   312   43   55   65   411   25   30   1    122   23   19   31   231   41   11   70   31    48   23   60   4Test Dataset:A    B    C    D    E11   21   12   111    2    3    45    6    7    8 99   87   65   34 11   21   24   12Since E hass only 4 categories, I thought of predicting this using Multinomial Logistic Regression (1 vs Rest Logic). I am trying to implement it using python. I know the logic that we need to set these targets in a variable and use an algorithm to predict any of these values:output = [1,2,3,4]But I am truck at a point on how to use it using python (sklearn) to loop through these values and what algorithm should I use to predict the output values? Any help would be greatly appreciated
Python : How to use Multinomial Logistic Regression using SKlearn
python;data mining;logistic regression;scikit learn