Implementation of a Meal Recommendation Technology using Dato and Text Clustering with SciKit

Meal Recommendation with Dato:

People are always trying to find the right kind of meal or diet which can help them reach their particular health goals. These health goals include but are not limited to gaining muscles, losing weight or keeping fit. Different types of meal lead to different results. Which meal will suit a particular person is determined by several factors such as ethnicity of the person, food habits of the person involved, health goals and availability of food in a particular geography. The present invention provides a computerized method (machine learning model) for people to get personalized meal recommendations from a computer system delivered through either a mobile, computer, laptop or person.   


Text Clustering with SciKit:

The current invention is aimed at simplifying the categorization of social media posts that are either (a) Published by individual companies/brands, or (b) Represent a compilation of posts from several companies/brands/thought leaders operating in the same industry or industry-subset, or (c) Represent results of search for a particular phrase or hashtag into several distinct themes as well as providing an effective way to evaluate which of the themes are popular. It can also help in predicting how popular a new post is likely to be on various social media channels.

Ashu is a product guy, hacker, and data scientist rolled into one. He is the co-founder of 12 Labs’ Applause - a data science based weight loss application. At 12 Labs, Ashu munches data to help Applause user’s loose weight smartly and scientifically.
He loves hiking and running. He has an MBA from UCLA and B.Tech from Indian School of Mines, Dhanbad. He brings over five years of product development experience