title: Python : How to use Multinomial Logistic Regression using SKlearn
body: <p>I have a test dataset and train dataset as below. I have provided a sample data with min records, but my data has than 1000's of records. Here 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.</p>
  
  <p>Training Dataset:</p>
  
  <pre><code>A    B    C    D    E
  1    20   30   1    1
  2    22   12   33   2
  3    45   65   77   3
  12   43   55   65   4
  11   25   30   1    1
  22   23   19   31   2
  31   41   11   70   3
  1    48   23   60   4
  </code></pre>
  
  <p>Test Dataset:</p>
  
  <pre><code>A    B    C    D    E
  11   21   12   11
  1    2    3    4
  5    6    7    8 
  99   87   65   34 
  11   21   24   12
  </code></pre>
  
  <p>Since E has only 4 categories, I thought of predicting this using Multinomial Logistic Regression (1 vs Rest Logic). I am trying to implement it using python. </p>
  
  <p>I know the logic that we need to set these targets in a variable and use an algorithm to predict any of these values:</p>
  
  <pre><code>output = [1,2,3,4]
  </code></pre>
  
  <p>But I am stuck 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</p>