I’m a 2nd Year B.Tech (AI) student focused on Data Visualization & Analytics (DVA). I work with SQL and Python (Pandas, NumPy) to transform raw data into clear, actionable insights through KPI-driven analysis and intuitive visualizations.

Alongside this, I have experience in MERN stack development and building AI/ML-integrated applications, combining data analysis with scalable full-stack solutions.

I also have exposure to quantitative research (WorldQuant Gold Level) and enjoy solving real-world problems using data.

Role: Data Analyst & Dashboard Developer Duration: Feb 2026 Domain: Data Analytics | Retail Intelligence | Business Optimization

• Designed and developed a data-driven order prioritization dashboard using a structured retail transaction dataset (~12,600 records). Performed revenue concentration analysis to identify high-impact transactions and top revenue-contributing customers.
• Built customer value segmentation models to analyze spending behavior across 25 unique customers. Conducted category-wise performance analysis to evaluate revenue distribution and purchasing trends across 8 product categories.
• Analyzed pricing and quantity behavior to identify patterns influencing total transaction value.
• Evaluated channel and payment insights by comparing Online vs In-store performance and payment method trends.
• Implemented data cleaning pipelines including handling missing values, validating transaction consistency, and outlier detection.
• Defined and structured key KPIs such as AOV, High-Value Contribution %, Revenue Share, and operational metrics for decision-making.

• Data Processing: Python, Pandas, NumPy
• Visualization: Tableau / Matplotlib / Seaborn
• Tools: Excel, Google Sheets (Pivot Tables, KPI dashboards)
• Data Source: Kaggle – Retail Store Sales Dataset

• Enabled identification of high-value transactions, improving strategic order prioritization decisions.
• Provided clear revenue distribution insights, highlighting concentration across customers and categories.
• Improved business decision-making through KPI-driven analytics such as AOV and revenue share.
• Delivered a scalable framework for retail analytics, transforming raw transactional data into actionable insights.

Role: Data Analyst & Visualization Engineer
Duration: Jan 2026
Domain: Data Analytics | Exploratory Data Analysis | Market Intelligence

• Performed end-to-end exploratory data analysis (EDA) on a large-scale video game sales dataset (~16,000+ records) to uncover global market trends.
• Analyzed regional sales distribution (NA, EU, JP, Others) to identify geographic demand patterns and key revenue drivers.
• Built global sales metrics and derived features to evaluate overall game performance across platforms and genres.
• Conducted genre-wise and platform-based analysis to identify the most profitable segments in the gaming industry.
• Developed correlation analysis to understand relationships between regional sales and total global sales.
• Implemented data preprocessing pipelines including handling missing values, data transformation, and feature engineering.
• Created visualizations and statistical summaries to present insights in a structured and interpretable format.
• Designed filtering and ranking logic to extract top-performing games based on multiple criteria.

• Data Processing: Python, Pandas, NumPy
• Visualization: Matplotlib, Seaborn, Plotly
• Environment: Jupyter Notebook
• Dataset: Video Game Sales Dataset

• Provides data-driven insights into global gaming trends, identifying top-performing genres and platforms.
• Enables clear understanding of regional market dynamics and revenue distribution.
• Highlights high-performing games and categories for strategic analysis.
• Establishes a scalable framework for exploratory data analysis on real-world datasets.

Role: Full Stack Developer & Prompt Engineer
Duration: Nov 2025
Domain: Healthcare | Artificial Intelligence | Patient Triage

• Designed and implemented an AI-powered triage system using Google Gemini API to provide context-aware medical guidance and symptom analysis.
• Built a real-time health timeline to maintain a unified longitudinal record of patient history.
• Developed precision specialist routing algorithms to connect patients with the correct medical professionals based on AI analysis.
• Integrated an SOS safety feature with live location sharing for immediate emergency response.
• Engineered a responsive, user-centric interface to act as an intelligent middle layer between patients and doctors.

• Frontend: React (Vite), TypeScript, Tailwind CSS
• AI & State: Google Gemini API, Context API, Prompt Engineering
• Tools: ESLint, Git
• Deployment: Vercel

• Simplifies complex symptom understanding for users through intelligent AI conversation.
• Streamlines the discovery process for medical specialists, reducing patient wait times.
• Enhances patient safety through immediate emergency response integration.

Role: Full Stack Developer
Duration: Dec 2025
Domain: SaaS | Data Analytics | Form Management

• Engineered a dynamic form builder application that allows users to design and customize data collection forms from scratch.
• Integrated a comprehensive analytics dashboard to visualize response data and user engagement metrics.
• Implemented structured data tracking, enabling efficient storage and retrieval of form submissions.
• Designed a visual interface for managing custom forms, significantly reducing the complexity of data collection campaigns.
• Optimized database schemas to handle dynamic field generation and varying data structures.

• Frontend: React.js
• Backend: Node.js, Express.js
• Database: MongoDB
• Architecture: MERN Stack

• Streamlines the process of creating and deploying custom forms for data collection.
• Empowers users with actionable insights through real-time visual analytics.
• Enhances data organization by structuring unstructured form inputs effectively.