Smart Fashion Recommendation System using Random Forest Algorithm

The fashion sector is expanding quickly in the digital age, and this means that more sophisticated recommendation systems that improve user experiences and expedite the online purchasing process are required. This work proposes a sophisticated and all-inclusive fashion recommendation system that uses machine learning techniques, specifically Random Forest, to enhance suggestion accuracy and efficiency. Users can choose their preferred fit and style using an interactive interface, which enables the system to offer clothes based on their individual profiles. Moreover, Context-based filtering involves personalizing recommendations to users based on a variety of contextual factors which may include user preferences, current fashion trends, occasion, style, body type and others. As a result of the dynamic recommendation process, the recommendations are constantly improved and grow more specific and well-rounded over time. The outcomes showcase the system's capacity to effectively combine various machine learning algorithms to evaluate a wide range of data sources, including user preferences, item features, and contextual information, to provide individualized recommendations.