Introduction
Med Cabinet is an application for new cannabis consumers who want to use cannabis as a means to battle medical conditions and ailments. In addition, Med Cabinet helps patients find the right strain, dosing, intake methods and scheduale. This application is built using Flask, along with a PostgreSQL database hosted database. The API is deployed to Heroku at which point a front-end team is tasked to build a user interface. This is a cross functional project during which my team was tasked to build a Flask app and PostgreSQL database. An additional data science team was responisble to the model used for this app as well as choosing the metrics to measure our models success.
Model and Metric
Our model leverages k-Nearest Neibors assuming TF-IDF vectorization and achieves a validation accuracy of 73.2%. In addition, on this project my team was tasked to intergrate the model into our Flask app, host the data using PostgreSQL and deploy the API to Heroku, at which point the Front End team would connect to the endpoint. Finally, model output is in the form of a Json object using the following categories:
- id
- name
- strain
- flavors
- negative
- positve
- medical
- rating
- description
Presentation
