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@article{206777,
        author = {Nishanth and Sayeel Shetty and Nidhin Shetty and Varsha J and Spoorthi B},
        title = {Toxicity Analyzer of Cosmetic Products and Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {430-434},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206777},
        abstract = {Cosmetic products are widely used in daily life for skincare, haircare, and hygiene, yet many contain harmful ingredients that can cause health issues such as skin irritation, hormonal imbalance, or long-term diseases. Identifying these toxic components is challenging due to complex chemical names on product labels. To address this, the Toxicity Analyzer of Cosmetic Products and Recommendation System is developed as a machine learning–based solution that analyzes ingredients and evaluates their toxicity levels. Using TF-IDF vectorization, the system compares products and suggests safer alternatives. Implemented in Python with Pandas, NumPy, Scikit-learn, and Gradio, it provides an intelligent and user-friendly approach to help consumers make safer and more informed decisions.},
        keywords = {Cosmetic Products, Machine Learning, Recommendation System, TF-IDF, Toxicity Analysis},
        month = {July},
        }