Author: Rishab Sharma - MAIT, GGSIP University ,New Delhi - 110086
A machine Learning Driven app for snack suggestion
One of the main uses of computers is to help us solve problems quickly and effectively. And a problem we often run into is figuring out what to eat, or what to make. This problem is solvable using data and recommendation engines.
Recommendation engines work on two levels. The first level is on the personal level. Let's say you create a dataset of foods and rank how much you enjoy or dislike them, 1-10. Given an unseen food and its set of features (such as the inclusion of ingredients, or perhaps the percentage of that meal the ingredient takes up). A machine learning algorithm figure out if and how much you'd like it. The other way recommendation engines work is on the group level. A machine learning algorithm should be able to recommend new foods to you, given a set of people who share your similar food preferences.
The goal for this project is to build a system that allows you to identify and then recommend, recipes you're likely to enjoy.
I have used Flask Microframework to serve my app.
Enter your Habits
Multi - Algorithm Recommender
Support Vector Machine - Output
Random Forest - Output
Decision Tree - Output
Neural Network - Output
- Python Numpy, OS , Sys , Matplotlib