Independent and dependent variables Machine Learning I want to start a regression model on lottery numbers, my database consist of the dates, and results(numbers). In order to do a regression model I believe you need independent and dependent variables, so my question is in my data what would be considered the dependent and independent variables. I think the results are dependent on the date but I am unsure, so someone else's opinion would be helpful. Below is the head of my data, thanks.
Data Head:

             Date  b1  b2  b3
11725  1984-09-01   9   4   8
11724  1984-09-03   1   5   3
11723  1984-09-04   8   6   6
11722  1984-09-05   5   6   9
11721  1984-09-06   2   1   9

Target is future outputs, Also would this data above need to be preprocessed.
 A: In a well designed lottery system, lottery numbers should be randomly generated and it should not depending on any variables.
So, if the range of the ball is from 1 to 32, I am expecting to see it is uniformly distributed, and have nothing todo with other variables such as date.
And if we really want to guess what number will be next, we have no choice but pick a random number from 1 to 32 for each ball.

Additional notes:


*

*I think this is a multi-class classification problem instead of a regression problem. Because the output is a discrete random variable.

*There is no "real random number generator exists", and we can argue the quality of the random number generator algorithm and random seeds. But in the lottery system, we usually will not have enough data to do the analysis to recover the random seeds) 

A: Short Answer: If a variable is completely independent, don't include it in your training!
More Detailed:
If a data column has no correlation with the target variable (in your case outcome of lottery), its  a good idea to take it out. You can look at correlation matrix to observe the relation between features and target variables (one easy way is using pandas.DataFrame.corr). If you are still not sure, then include it and try to see how your results are changing if you include that variable. You can also look at feature importance of that variable if you are using tree based models.
