Charles Crocicchia

Food Inspection Data Analysis and Modeling

Most health departments in the United States keep the record of food inspections for establishments on public record in some form, where all restaurant's prior violations are documented as well as all pertinent info regarding the what type of inspection occurred and how it resulted. But this information is stored in excel sheets or complex formats which are not easily interpretable and have virtually zero searchability. This project's goal is to visualise the inspection track record of an establishment to potential customers in a captivating, easy to digest manner so they can make a better informed decision on where to eat.

• Developed a web app enabling users to search restaurants, view inspection results, and analyze food safety trends.
• Built a logistic regression model to predict inspection outcomes, helping identify high-risk establishments.
• Engineered new features for the data set leveraging my domain knowledge.

App Demo
View on Github

Reddit Post Classification with Natural Language Processing

Analyzing language trends in Reddit forums, specifically r/philosophy and r/showerthoughts, to explore differences between self-made observations and those by established philosophers through exploratory data analysis and modeling. This project utilized the Reddit API and custom scripting to automate the creation of a data set in which I would use Natural Language Processing along with several types of classification models to predict from which subreddit a particular post came from. On the validation data, my best model achieved an accuracy of 90%.

View on Github

Data Analyst Intern

Analyzing multi-source data to deliver actionable insights. Creating compelling presentations that highlight business strengths and strategic opportunities, supporting decision-making for stakeholders and potential buyers.

Data Science Fellow

12 weeks of immersive training dedicated to acquiring industry best practices of data modelling, visual and statistical analysis, OOP, and Test-Driven Development with a Python approach into the world of Data Science.

• Focused on the ability to analyze, make sense of, convey data-driven facts from large data sets, and predict what happens next through predictive modeling and pattern recognition.
• Developed a portfolio of both individual and collaborative projects, including a deep dive into the statistics and predictive behavior of food inspections for restaurants, as well as modeling the socio-economic impacts of the agricultural industry in the United States.
• Utilized extensive EDA techniques, a wide variety of supervised and unsupervised learning methods such as Regression, Classification, Clustering, and Neural Networks, and visualization libraries such as Seaborn and Plotly to present my work in a captivating manner.

Sous Chef

Led and managed kitchen teams in a multicultural fine dining restaurant, fostering effective collaboration and communication across diverse crew members. Utilized Excel for inventory management and food preparation lists, ensuring accurate tracking, streamlined supplier orders, and efficient workflow. Strengthened team dynamics by tailoring leadership and feedback styles to individual team members, enhancing performance and morale.