I have been doing work on this recently, I have many years of greyhound data from my Surepick business, particularly valuable is our proprietary info.


I use RStudio for the ML analysis. As Shaun notes, the main issue is understanding ML, statistics and what combinations of your data can be best used for predictions. It is critical to remove as much co-linearity as possible. this is where the trial and error comes in, looking for the most significant data when creating Training and Test sets.

To get into ML, I found the kaggle ML resource fantastic. You can build your skills working on their Titanic ML example, where you try to predict which passengers live or die based upon specific data factors.



Grant