I analyze data to uncover insights and build data-driven solutions. I specialize in: • Exploratory Data Analysis (EDA) on large datasets • Data cleaning, preprocessing, and feature engineering • Creating visualizations using Matplotlib, Excel, Power BI • Generating reports, dashboards, and decision-support insights • Training and evaluating simple ML models for trend prediction My internship at Shell strengthened my real-world analytics experience.

I build end-to-end machine learning solutions using Python and Scikit-learn. My experience includes: • Training and evaluating models such as XGBoost, Random Forest, SVM • Working with Pandas, NumPy, and real-world datasets • Deploying ML models with FastAPI for real-time predictions • Using SHAP for model explainability and insights • Building prediction systems like health-risk models and student performance predictors This role fits how I work: data → model → API → deployment.

I develop complete, production-ready web applications from frontend to backend. My strengths include: • Building interfaces using React, Next.js, JavaScript • Developing backends with FastAPI, Node.js, Express • Implementing authentication, APIs, and cloud integration • Working with Firebase, GitHub, and modern tooling • Shipping full apps like AI Resume Builder, MedPrompt+, and AutoFlow I'm comfortable with the entire development pipeline—from UX to API deployment.

I create AI-powered applications that use LLMs and automation. My experience includes: • Integrating GPT, Claude, Gemini into real products • Designing chatbots, assistants, and conversational workflows • Working with RAG pipelines, embeddings, and PDF/image parsing • Automating tasks using AI inside platforms like AutoFlow • Building AI-driven features such as resume scoring, health interpretation, and workflow automation This is one of my strongest areas, as I've built real AI systems used in full applications.