Introduction Google Slides Tableau About Us

🥇 NYC Restaurant Analysis 🥇

Health Inspection Grades and Neighborhood Income

New York City is a tapestry of different culture and cuisine. Our goal is to determine if there are any correlations between higher income neighborhoods
and the likelihood of receiving a better health inspection grade within these five boroughs.

Filter Search

Restaurant Street Income Level Borough Zipcode Cuisine Description Score Grade

Visualization of mean cuisine score by selected zip code using different plotly graph


Zip code:


Grading Information


What are these charts represents for?


Bar chart and Bubble chart was made to visualize if there is any correlation between high income neighborhood's by zip code and the mean of cuisine type scores.
Gauge chart was create based on the mean score by zip codes.


Tableau Analysis

Visualization of different Machine learning model's accuracy score

We tried different ML model to see which model give us the better accuracy score. Our best model is Random Forest Classifier with an accuracy score of 0.97.

Summary of Analysis


Accuracy score was obtained and visualize to compare which machine learning model performs best with our data.
Random Forest Classifier was the best model to predict grade from a specific zip code and income level.

About our team

We have five people in our team and each one response to their roles. In the whole project, we do help each other across different roles to solve the issue that we were faced.
Please see the roles listed below:
* Square Role: Yawen Liang - Responsibility: Github, Tableau, Dashboard
* Circle Role: Jessica Berrios - Responsibility: Database, Data Cleaning, Dashboard
* Triangle Role: Soha Tariq - Responsibility: Machine Learning
* X Role: Nishat Sultana - Responsibility: Visualization, Dashboard
* X Role: Joon Su Choi- Responsibility: Visualization, Tableau, Data Cleaning