Abstract: Whenever I want to try something new, it is very difficult to choose. Sometimes it's scary because new things I've never tried end up giving me a skin problem. We know that the information we need is behind each product, but it is really difficult to define that ingredient list unless you are a pharmacist. You may relate to this situation. So instead of buying and trusting the best, why not use data science to help us predict what might be right? We will create a content-based recommendation system where the content will be the chemical components of cosmetics. Specifically, we will process a list of 1472 cosmetic ingredients in sephora using WORD EMBEDDING, and visualize the similarity of the ingredient using a machine learning method called t-SNE and a collaborative library called Bokeh. In a world focused on the need to sell a large growing number of goods, quality, visual appearance, texture, taste, taste etc. it becomes an important factor in supporting the development of the global economy. As a result, different types of food additives have been developed and are still being developed and used to achieve those purposes. Next, the purpose of this paper is to introduce the design and implementation of a new software package for embedded systems that can support the customer in the food purchasing system. The advanced software package works on the platform and uses the cloud optical character (NLP) MACHINE learning algorithm. The details related to the product you are interested in and provided on request to customers are: name and product ingredients code, risks associated with each food supplement, source of each food component, soap, limit of daily use. and side effects, dietary restrictions etc. Technology: NLP(Natural Language Processing)