Leveraging Machine Learning to Generate Effective UX Personas for Low-Cost Digital Production

This research examines the use of machine learning for generating unbiased, contextually appropriate UX personas to enhance user experience design while reducing costs in low-budget digital production environments. The proposed framework utilizes demographic and behavioral data to train a predictive model capable of automatically creating personas, thus eliminating the biases inherent in manually crafted personas. By leveraging Python libraries such as scikit-learn and pandas, the system processes and analyzes input data, generating accurate personas based on product preferences, education, occupation, and other factors. The "persona.py" script further personalizes each persona by fetching gender-specific names from a publicly available dataset. This approach not only minimizes human bias but also offers a scalable and cost-effective solution for digital design teams, enabling more efficient persona generation without the need for extensive manual intervention.