I’m a data science professional with a background in software development and a passion for solving real-world problems through data. I enjoy working on projects that turn messy, unstructured information into insights that support smarter decisions. Whether it’s cleaning data, writing code, or asking the right questions, I like building tools that make complexity easier to understand.
As part of my data science internship, I developed an AI-powered application that predicts cancer risk based on user-provided information. The tool demonstrates how structured health data and patient-reported inputs can be used to classify cancer risk through an interactive machine learning interface.
Users enter health-related inputs such as age, gender, lifestyle habits, and symptoms using natural language. The system translates this into structured features and uses a trained Random Forest model to classify risk levels as Low, Medium, or High. The front end is built with Streamlit to provide an interactive experience.
While this tool is based on publicly available data, it reflects the broader goal of my internship: to build an intelligent assistant that helps identify patterns in patient behavior, predict potential risks, and deliver insights in plain language using real-world healthcare data.
Tools used: Python, R, Streamlit, Scikit-learn, Pandas
Outside of data science, I care about focus, balance, and continuous growth. I take time to recharge through walks, journaling, and quiet reflection. These habits help me stay present and think clearly, especially when solving complex problems or working under pressure.
I’m also someone who enjoys trying new things, whether it’s exploring a new restaurant, discovering local events, or testing out unfamiliar tools and ideas. I like being the first to see what works and what could be better.
I believe that staying grounded and self-aware supports clear thinking, strong collaboration, and consistent results.
I believe that focus and creativity come from a calm, grounded mind.