Karpiu is a package designed for marketing mix modeling by calling Orbit from the backend. Karpiu is still in its beta version. Please use it at your own risk.

✈️ AI-powered travel planning system with budget optimization, multi-source search, and personalized recommendations built on Supabase + PGVector, LangGraph Agents, Dragon DB, and NextJS 15, TailwindCSS

Marketing Mixed Modelling using PyMC3-Marketing

🧳 A state-of-the-art multi-agent travel planning system powered by OpenAI Agents SDK and LangGraph orchestration. Leverages Stagehand/Playwright for browser automation, Supabase for data persistence, and Firecrawl/Tavily for intelligent research.

This repository contains the links to Medium Articles written by me on topics like Digital Marketing Analytics, Data Science & Deep Learning

🌍🔍 Comprehensive GeoSpatial environmental data analysis, focusing on dissolved carbon & greenhouse gases. Includes advanced data processing, analysis, and budget optimization.

Comprehensive performance marketing optimization strategies covering modern paid advertising, ROI maximization, and campaign optimization methodologies. Maximize marketing performance and profitability.

Modular Marketing Mix Modeling tool for optimizing marketing budget across channels.

A Marketing Mix Modeling (MMM) project using Python to analyze channel performance, calculate ROI, and simulate marketing budget changes for better business decisions. Includes a trained Linear Regression model, ROI analytics, and a Flask API for revenue prediction.

MOCA-Net: Novel neural architecture with sparse MoE, external memory, and budget-aware computation. Real Stanford SST-2 integration, O(L) complexity, 96.40% accuracy. Built for efficient sequence modeling.

🧳 Automate travel planning with AI-driven multi-agent technology for personalized trips, optimized budgets, and real-time data insights.