Predict NA (missing values) with machine learning
<p>I have a huge data set and want to predict (not replace) missing values with a machine learning algorithm like svm or random forest in python.  </p>

<p>My data set looks like this:  </p>

<pre><code>ID i0   i1    i2    i3    i4   i5     j0    j1   j2   j3    j4    j5    

0  0.19 -0.02 -0.20 0.07 -0.06 -0.06  -0.06 1.48 0.33 -0.46 -0.37 -0.11
1 -0.61 -0.19 -0.10 -0.1 -0.21  0.63   NA    NA   NA   NA    NA    NA
2 -0.31 -0.14 -0.64 -0.5 -0.20 -0.30  -0.08 1.56 -0.2 -0.33  0.81 -0.03
.
.
</code></pre>

<p>What I want to do:<br>
On the basis of ID 0 and 2 I want to train the values of j0 to j5 with i0 to i5. Subsequent there should be a prediction of the NA's from j0-j5 for ID 1.</p>

<p>Question:<br>
As the data is not continuous (the time steps end at i5 and start again at j0), is it possible to use some kind of regression?</p>

<p>How should the X and the y for the .fit(X, y) and .predict(X) function look like in this example?</p>