I have prepared some introductory sessions on machine learning for horse racing using Python and Scikit Learn. You do not need previous experience of either of these two tools but it would help if you are at least familiar with some basic programming concepts. For example it would help if you know what a FOR loop is, what an assignment statement is even if it is not in Python.
The main data file will be freely available until Tuesday 2nd February for those who showed an initial interest. After this it will be in the utilities section of the http://www.smartersig.com web site. A modest members fee will enable you to access it.
The instructions will be freely available to all at all times.
OK to get started you will need to have downloaded and installed Anaconda Python v3.4, see previous blog post Profitable Punting with Python Intro for details.
Once this has installed create a folder in your anaconda folder called horseracing.
All comments, questions and feedback should be posted to this blog post, that way they can act as a FAQ source.
First of all download the following zip file, double click on it to reveal all the contained files and copy them into your horseracing folder.
The next step is to download the following file into your horse racing folder. When you click the link it will probably display the contents in your web browser. Just right click the display and you will have the option to save to a file the screen data.
This file is now housed in the utilities section of the smartersig.com web site and is called aiplus12to14.csv
You now have the required files. To get started first open a msdos command window (the black box type)
Now navigate to your anaconda folder using cd command eg cd anaconda
Kick start Ipython Notebook by typing in ipython notebook and pressing return. (note on latest version this may now be jupyter notebook)
Once notebook is loaded up you will be presented with a directory screen of folders. Double click on the horseracing folder (that you created) to go into that folder.
Now double click on the file ProfitablePuntingWithPython1.ipynb
Follow the instructions within the displayed notebook.