A Skincare Ingredient Analysis And Product Recommendation Platform Using Filtering And Ai

Abstract

This project introduces a web-based approach for the comprehensive analysis and filtration of comedogenic substances in skincare products, responding adeptly to the dynamic developments in this field. Comedogenic substances, infamous for blocking pores and worsening skin issues, pose a considerable challenge for those seeking effective skincare solutions. This groundbreaking system utilizes advanced algorithms to deeply analyze the ingredients in skincare products provided by users. It focuses particularly on identifying ingredients that are most likely to be comedogenic, addressing a critical concern in skincare. Central to this system is its advanced algorithm, meticulously crafted to detect and segregate potentially harmful ingredients, while concurrently providing user alerts and suggesting safer alternatives. Consequently, this methodology is firmly anchored in a thorough understanding of dermatological principles and consumer preferences, ensuring that the recommendations are not only safe but also specifically tailored to individual skin types. Furthermore, the system stands out due to its easy-to-use interface, making it much simpler to input ingredients and enabling quick analysis, thereby supporting users in making well-informed skincare decisions. The Skincare Ingredient Analysis and Product Recommendation Platform employs advanced machine learning techniques to enhance user experience and optimize product recommendations. By integrating Flask and the BERT model, the platform effectively semantically analyzes user queries and ingredient inputs, ensuring accurate and relevant search results. The BERT model, hosted on a Flask server, processes ingredient information to identify semantically similar terms, which are subsequently used to search for products through Elasticsearch. Furthermore, the platform incorporates the Google AI Studio - Gemini API, providing detailed information on ingredients, specifically identifying those that are comedogenic or harmful to pregnant women. Moreover, this project delves into the system's technical framework, encompassing its database architecture, algorithmic design, and user interface development. Therefore, the primary objective is to enhance the user experience in selecting skincare products by offering a trustworthy and effective tool for identifying comedogenic ingredients. Significantly, preliminary testing demonstrates a high degree of accuracy and user satisfaction, underscoring its prospective applicability in the skincare industry. Looking ahead, future enhancements for this system include the incorporation of an AI-driven 'smart feature'. This innovative tool is designed to evaluate the compatibility of skincare product ingredients with an individual's specific skin type and to recommend similar products that meet the user's skin requirements. Furthermore, this forthcoming enhancement will utilize artificial intelligence and machine learning methodologies to refine and personalize the filtering process even further. The database is also set to be expanded to encompass a more diverse range of products. Ultimately, this evolution represents a substantial breakthrough in personalized skincare technology, offering an efficient and sophisticated solution for navigating the complex skincare product landscape.