If you want to predict something from your data, you need to put a strategy in place. I mean you need a way to measure how good your predictions are … and then try to make the best ones.
This is usually done by taking some data for which you already know the outcome and then measuring the difference from what your system predict and the actual outcome.
This difference is often referred to as the “cost function”. Once we have such a function our machine learning problem comes down to minimising our cost function.
One very simple way to find the minimum value(s) is called gradient descent. The basic idea is to make small steps along the gradient (the derivative of the function) until we reach a minimum.