Hello!

I'm Aneesh Kapole, data analyst and back-end engineer. Currently based in Detroit, Michigan.

Get in touch with me via email or LinkedIn

More About Me.

I'm currently studying Computer Science and Engineering at Michigan State University, and stuff I'm currently learning includes software engineering, database systems and the big data analysis. I recently helped code SwiftFlow at SpartaHack X.

I'm passionate about using the skills I'm learning to develop solutions to day-to-day problems. I'm a big believer in open-source and configurable software, and believe that each user should not be bound by the design ideals of the developer.

my github

my work so far ...

Next.js React PostgreSQL Stripe Vercel

Batter Up Bakery

Full Stack Developer · Dec 2025 - Present

Designed and deployed a full-stack web application using Next.js, integrating frontend components with backend API routes and third-party services. Implemented authentication workflows and secure Stripe-based payment processing for online orders, with database-backed customer management and order tracking. Deployed to production via Vercel with CI/CD.

React Flask Python SQL TailwindCSS PowerBI

Intelligent Network Security for High Risk Traffic

Data Engineer / Full Stack Intern · Sept 2025 - Dec 2025

Built an end-to-end enterprise web application at Michigan State University for McKesson to help IT teams analyze and manage firewall rules. Implemented an ETL pipeline joining 4+ relational tables, processing 100,000+ data points, and generating risk scores for 6,000+ rules — reducing processing time from 40+ minutes to 5 minutes. Developed interactive dashboards with PowerBI and integrated OpenAI APIs for automated security report summaries.

Python TensorFlow OpenCV YOLOv8 Roboflow Tkinter

SwiftFlow

Trained a visual ML model using Roboflow, TensorFlow, OpenCV, and YOLOv8 that recognized 5+ hand gestures and simulated 100+ keyboard shortcuts and precise mouse movements in real time. Built the application UI and backend using Tkinter and Django, with a database to store preset shortcut lists per application. Presented at SpartaHack 10, receiving praise for innovation and effectiveness.

Python MongoDB scikit-learn pandas BeautifulSoup

Super Bowl Prediction Model

Built a data ingestion pipeline scraping 1,000+ rows from 3+ web sources using BeautifulSoup, with automated cleaning and loading into MongoDB. Designed a database with 10+ seasons of structured offensive and defensive team data, then developed 5+ predictive models using apriori algorithms and decision trees, achieving 90% accuracy in identifying Super Bowl bound teams.