Built an end-to-end modern Data Engineering pipeline on Microsoft Azure and Databricks for restaurant chain analytics, supporting both batch and real-time streaming data ingestion.Implemented a Medallion Architecture (Bronze → Silver → Gold) using Apache Spark, PySpark, Delta Lake, Unity Catalog, Azure Event Hubs, Azure SQL Database, and LakeFlow Connect, with transformations for orders, customers, restaurants, menu items, and reviews.Developed Gold-layer business-ready datasets and Databricks SQL / AI-BI dashboards for restaurant performance, sales, customer analytics, and reviews, incorporating CDC, change tracking, Structured Streaming, Spark Declarative Pipelines, workflow orchestration, and AI-powered sentiment analysis.

Built a production-oriented AI clinical triage and patient risk monitoring workflow using n8n, integrating Groq Llama-3.1, Webhooks, Gmail, Slack, and Google Sheets. The system validates and sanitizes incoming patient data, applies rate limiting and deduplication, enriches clinical context, and uses an LLM to generate structured risk assessments with confidence scores.Implemented confidence-based routing across LOW, MEDIUM, HIGH, and CRITICAL risk levels, with automated email/Slack alerts for high-priority cases and manual-review routing for low-confidence predictions. Added audit logging, Google Sheets persistence, retry handling, and global error monitoring to improve workflow reliability and traceability.Tech: n8n, Groq, Llama-3.1, LLMs, Prompt Engineering, Webhooks, Google Sheets, Gmail, Slack, JavaScript, Workflow Automation, API Integration.

Developed a machine learning-based sentiment analysis system to classify movie reviews as positive or negative using review text and associated movie metadata. Performed EDA, data cleaning, text preprocessing, vectorization, feature scaling, and model evaluation, followed by hyperparameter tuning across three classification algorithms: Logistic Regression (84.4%), Naive Bayes (82.3%), and Linear SVC (84.7%). Selected Linear SVC as the final model based on its highest evaluation score. Tech: Python, Scikit-learn, Pandas, NumPy, NLP, Jupyter Notebook, Linear SVC, Logistic Regression, Naive Bayes.

Built 2 interactive Tableau executive dashboards for Sales and Customer analytics, enabling year-over-year analysis of revenue, profit, quantity, customer behavior, and product performance. Developed 15+ visualizations and 10+ dynamic KPIs using Tableau calculated fields, parameters, filters, and dashboard actions to analyze sales trends, growth metrics, monthly performance, and customer segments. Implemented reusable BI calculations including Current Year vs Previous Year analysis, percentage growth, Top-10 customer ranking, and highest/lowest month analysis, transforming raw sales data into actionable insights for data-driven decision-making. Tech: Tableau, Business Intelligence, Data Visualization, Calculated Fields, Parameters, Dashboard Actions, KPI Analysis

Built 2 interactive Tableau executive dashboards for Sales and Customer analytics, enabling year-over-year analysis of revenue, profit, quantity, customer behavior, and product performance. Developed 15+ visualizations and 10+ dynamic KPIs using Tableau calculated fields, parameters, filters, and dashboard actions to analyze sales trends, growth metrics, monthly performance, and customer segments. Implemented reusable BI calculations including Current Year vs Previous Year analysis, percentage growth, Top-10 customer ranking, and highest/lowest month analysis, transforming raw sales data into actionable insights for data-driven decision-making. Tech: Tableau, Business Intelligence, Data Visualization, Calculated Fields, Parameters, Dashboard Actions, KPI Analysis

Built a comprehensive Customer Analytics solution by integrating Data Warehousing, ETL processing, EDA, and advanced analytics with interactive Tableau dashboarding. Processed and transformed large-scale ERP and CRM datasets into business-ready analytical models using PostgreSQL and SQL. Developed an interactive Tableau Customer Analytics Dashboard featuring 10+ KPIs and multiple visualizations focused on customer segmentation, revenue analysis, recency analysis, customer trends, top customers, and purchasing behavior. Generated actionable business insights to support customer retention strategies, sales optimization, and data-driven decision-making.

Designed and developed a modern end-to-end Data Warehousing solution using Medallion Architecture (Bronze, Silver, Gold layers) with PostgreSQL and SQL. Built robust ETL pipelines to ingest, clean, normalize, and transform ERP and CRM datasets into analytics-ready business models.Performed Exploratory Data Analysis (EDA) and advanced analytics on 29M+ sales records to uncover insights related to revenue trends, customer behavior, sales performance, and operational patterns. Applied analytical techniques for business reporting, trend analysis, recency analysis, and KPI generation, demonstrating practical expertise in Data Engineering, Data Modeling, and Business Analytics workflows.

Developed a complete Product Analytics solution by combining modern data warehousing, ETL development, EDA, advanced analytics, and Tableau dashboarding into a single business intelligence workflow. Designed analytics-ready data models using PostgreSQL and SQL for efficient reporting and product performance analysis. Created an interactive Tableau Product Analytics Dashboard with multiple KPIs and visualizations covering product revenue trends, category performance, top-selling products, pricing analysis, quantity trends, product segmentation, and recency analysis. The project enabled detailed product-level business insights and showcased expertise in Data Analytics, Business Intelligence, and Dashboard Development.

Analysis of Regional Sales Data , Finding Trends & KPI's using Python and Pandas. Designed a dynamic dashboard to track sales volumes, revenue, and customer behavior. Visualized key trends by region, gender, and day using clean charts and metrics. Built using Jupyter Notebook for fast insights and business decisions.

Analysis of Call Center Data , Finding Trends & KPI's using Microsoft Excel.Designed a dynamic dashboard to track call volumes, revenue, durations, and customer satisfaction. Visualized key trends by region, gender, and day using clean charts and metrics. Built using Power BI / Excel for fast insights and business decisions.

A powerful Research Assistant chatbot built with Google Gemini (Pro & Embeddings), LangChain, FAISS, and Streamlit. This tool extracts, processes, and answers queries based on information scraped from any public URLs.

AI Anime Recommender System using Groq , Langchain and Chroma DB as Vector Store

ClipNest is a micro-SaaS that lets users upload demo videos and instantly generate clean, shareable links for portfolios and project showcases. Built with a serverless-first approach, the app uses Cloudinary for video hosting and Vercel KV for lightweight URL mapping.

Celebrity Detector & QA using Kubernetes, CircleCI, Groq, Llama-4, OpenCV , Flask !!!

BCG (Boston Consulting Group) Generative AI Job Simulation on Forage. The simulation involved building an AI-powered financial chatbot capable of interpreting complex financial data from SEC filings (10-K and 10-Q reports).

An end to end LLM project based on Google Gemini and Langchain. A system that can talk to MySQL database. User asks questions in a natural language and the system generates answers by converting those questions to an SQL query and then executing that query on MySQL database. A T-shirt store where they maintain their inventory, sales and discounts data in MySQL database.

A simple Recurrent Neural Network (RNN) built from scratch using TensorFlow/Keras, trained and evaluated on the IMDb movie reviews dataset for sentiment classification.

This project uses an LSTM-based deep learning model to predict the next word in a sequence using Shakespeare's Hamlet as the dataset. The text is preprocessed through tokenization and padding, then fed into a model with embedding and LSTM layers. After training with early stopping to prevent overfitting, the model is evaluated on sample inputs. Finally, a Streamlit web app was built to let users interactively predict the next word in real-time.

Trained Lasso, Ridge, ElasticNET Model on Algerian Forest Dataset - Solve Regression Problem. Trained model on Algerian Forest Dataset and finds FWI Index value!!

I conceptualized and developed the Himroo Heritage Shopify store to bring a centuries-old Indian textile art to a global audience. The site blends a luxurious, heritage-inspired design with a modern, seamless e-commerce experience. It features compelling storytelling about the craft's history alongside optimized product pages for shawls and stoles.

Creator of Happy Earth Studio, a premium Shopify destination for hand-painted terracotta and sustainable home décor, blending traditional Indian craftsmanship with modern e-commerce.