The use of machine learning is a deep rabbit hole to be sure. Estimation functions, activation functions, error functions, gradient descent, weights... then there are "hyper parameters" that can affect the performance, layering, hidden layers, noise, bias... stateful or stateless LSTMs. The learning will be continuing throughout the coding of the first iteration.

The desired output will be time series, one pick in each position for 7 days, since we know the dates. Why not just for the next day? Because we are working with sequence data, trying to find sequence patterns. This will run a great deal longer than any program I have written prior, hours rather than minutes (the 2nd layer).

Still one pick per draw. It can be modified to output a sequence of variable length, if the week length does not have a positive result, it could be extended to a month. It can be ran in several configurations at the same time.

Going to build the script to be modular, so it can handle any daily game, from pick 2 to pick 5. IF there are positive results, then it can be adapted to the red ball in PB or MM, but that is not an initial goal.

Generating a graph of the test phase IS a goal. 80% of the draw data will be used for training, the remaining 20% for testing and the final output being a series of 7 time increments into the future.

A graph of where the system picks right and where it misses will definitely help to set realistic expectations and possible error correction factors. Also going to keep a spreadsheet of the parameters to record changes made.

For all of the years I worked on lottery systems I do not believe I was asking the right questions... this could change all of that. Or not. The work will not be wasted, as machine learning has many applications, but wouldn't it be cool if there was some success!