AGE AND GENDER DETECTION

Age and gender detection is a valuable task across a range of domains such as recommendation systems, demographic studies, customer profiling, targeted advertising, and user analytics. While most age and gender detection systems rely on image processing and facial recognition, these approaches raise privacy concerns and require extensive computational resources. This project proposes an alternative, non-visual method using Machine Learning models trained on textual, behavioral, and metadata features. This system uses demographic data, user behavior attributes, social media activity data, or application usage logs to predict the probable age group and gender of a user without relying on images or visual data. The approach emphasizes privacy, scalability, and ease of implementation.