I still have a bit of work to do, but I figured I was at a good point to share the progress I’ve made with my assignment in
Machine Learning Artificial Intelligence.
The tool works by reading a CSV file containing multiple columns of “Features” or variables you could say, ending with a final column designating if those variables resulted in a positive or negative end result. Finally this allows you to “Query” the dataset by entering your own set of variables and getting back the probability of a positive or negative end result.
Moreover, this is not simply pattern matching against a set of preconfigured categories, the tool both parses the “test cases” and generates a library for the data set at the same time, so you could swap out the commonly used “Weather=>Play-Tennis?” dataset for literally any spreadsheet based dataset you’d like. I am working with a collection of data from IMDb at the moment with just shy of 3,000,000 rows and 8 columns, which by design I was able to just plug in place of the “Weather=>Play-Tennis?” dataset without having to change anything.
Expect a follow up in the next few weeks specifically for this project!
For some more information on Bayesian Networks. I recommend checking out this article from Towards Data Science as it covers the topic much more fluidly than I could ever hope to achieve.