I built a Telegram bot using Python and the python-telegram-bot library. The bot automates reminders, tracks contributions, and sends personalized updates to members. This project taught me how to structure modular code, handle user input, and deploy a real-time automation tool that supports community engagement.
I designed a data pipeline to collect, clean, and visualize pay rate data across regions. Using Python, Pandas, and Matplotlib, I created a dashboard that highlights disparities and trends. This project deepened my understanding of data wrangling, exploratory analysis, and ethical data storytelling.
I built a personal portfolio using Django, HTML/CSS, and Bootstrap. The site features modular templates, animated content, and a responsive layout. It serves as both a learning lab and a showcase of my evolving skills in web development, design systems, and user experience.
As part of my ongoing data science internship at Interncred, I completed Task 1: predicting survival on a synthetic Titanic dataset. I explored Random Forest, Gradient Boosting, and Logistic Regression, achieving 52% accuracy using Gradient Boosting on a simplified feature set. I learned to interpret feature importance, diagnose weak signals, and document the full workflow.