• PRAJAKTA SANTOSH RANE
• PRACHI PANDIT PADWAL
• SHRAVANI SANJAY BAGADE
• PRAJAKTA RAMCHANDRA KADALAGEKAR
• SANIKA PANDURANG KUMBHAR

With economic development and the aging trend, the use of cosmetic products has expanded rapidly. In an ever-expanding skincare market, facial skin care product was the most popular product of skin care products. However, thousands of skin care products are available in the market. With endless options, shoppers are confronted confused, and tired. Because everyone's skin condition is different, using unsuitable skin care products can damage the skin. Frequent problems with face skin are wrinkles, spots, acne vulgaris, pores, etc. The causes of facial lines, such as dryness, facial expressions, aging, etc., are caused by different shades and different types of wrinkles. Therefore, knowing your skin quality and using skin care products correctly is very important. According to the application of different levels of image processing, it can be divided into image classification, positioning, object detection, and object segmentation in the field of image vision. skin condition classification uses the image processing algorithm to preprocess automatically remove, reduce noise, enhance, normalize, and extract features to obtain the feature vectors of the sub-images for training the multi-label classification model. The prediction results of machine learning can provide suitable maintenance knowledge and product recommendations for users to recommend suitable skin care products and maintenance ingredients for the user’s skin condition.

Machine Learning, Dermatology

"AI in Dermatology", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.c132-c139, November-2024, Available :http://www.jetir.org/papers/JETIR2411216.pdf

"AI in Dermatology", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppc132-c139, November-2024, Available at : http://www.jetir.org/papers/JETIR2411216.pdf