Abstract: The problem this project is trying to solve is the ability to predict an outcome of a simplified poker game To solve this problem the following approach was taken – a neural network was trained to classify poker hands and the hand odds of the opposing player were calculated by finding all the possible hand combinations. These values were then generated as datasets for multiple poker games and used to train a new neural network to predict a win or a loss. Experimentation was performed on data that was downloaded from the UCI Machine Learning Repository as well as data that was generated by the project source code. The Code implementation of this project can be found in the following public GitHub repository https://github.com/armankocharyan/Honours-Project/tree/master/Data%20Generator Although the results were good, it stands that the neural network can be improved by training it with more valid data. The predictions can also be improved by implementing other AI methods and approaches such as decision trees and reinforcement learning.