Intention recognition entails the process of becoming aware of another agent’s intention by inferring it through its actions and their effects on the environment. It allows agents to prevail when interacting with others in both cooperative and hostile environments. One of the main challenges in intention recognition is generating and collecting large amounts of data, and then being able to infer and recognise strategies. To this aim, in the context of repeated interactions, we generate diverse datasets, characterised by various noise levels and complexities. We propose an approach using different popular machine learning methods to classify strategies represented by sequences of actions in the presence of noise. Experiments have been conducted by varying the noise level and the number of generated strategies in the input data. Results show that the adopted methods are able to recognise strategies with high accuracy. Our findings and approach open up a novel research direction, consisting of combining machine learning and game theory in generating large and complex datasets and making inferences. This can allow us to explore and quantify human behaviours based on data-driven and generative models.