HealthGrade Solutions (HGS) is a fictiouse company set up this project. It aims to leverage predictive analytics based on the DOHMH New York City Restaurant Inspection Results dataset to address faulty restaurant compliance (low hygiene scores) with health and safety regulations. This model assists emerging restaurant owners and the Department of Health and Mental Hygiene (DOHMH) in improving compliance and ensuring public health and safety in New York City.
- Authors: This Project was done in a team as part of a collaborative project.
- DOHMH: Prevents foodborne illnesses, educates restaurant staff on hygiene, and assesses compliance.
- Restaurants: Achieving high sanitation scores enhances reputation and performance.
This project involves building a logistic regression model to predict restaurant compliance grades based on inspection data.
The dataset used is the DOHMH New York City Restaurant Inspection Results. It includes various features related to restaurant inspections, such as borough, cuisine type, inspection date, action taken, violation codes, critical flags, and grades.
The logistic regression model provides moderate accuracy and insight into restaurant compliance with health regulations. Further optimization and feature selection can improve model performance.