Mintesnot Mengesha
Computer Engineer | AI/ML & Full-stack Developer
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Mintesnot is ready for their next project!
took a standard MERN stack food delivery platform and transformed it into an intelligent ecosystem by integrating an Action-Oriented AI Agent. This project bridges the gap between traditional e-commerce and the future of Agentic AI.
Key Innovations:
Dietary Personalization: The AI Agent analyzes user health data and dietary restrictions to provide custom meal recommendations.
Autonomous Execution: Unlike basic chatbots, this agent can execute orders and manage the checkout flow through natural language processing.
Full-stack Architecture: Built with the MERN stack (MongoDB, Express, React, Node.js), featuring real-time data sync and secure user authentication.
Intelligent Logic: Leveraged advanced LLM APIs to create a conversational interface that understands user intent and interacts directly with the database.
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Project Overview: Full-stack Food Delivery System
I developed this end-to-end food delivery platform using the MERN stack (MongoDB, Express, React, Node.js) to provide a seamless e-commerce experience.
Key Technical Features:
- Payment Integration: Integrated the CHAPA payment gateway to facilitate . secure and reliable online transactions for local users.
- User Management: Implemented secure JWT-based authentication and a dynamic cart management system.
- Real-time Logic: Developed robust REST APIs to handle restaurant browsing, order placement, and delivery tracking.
-Responsive UI: Created a modern, fast-loading frontend with React focused on high conversion and user engagement.
This project demonstrates my ability to build scalable, production-ready web applications with integrated financial logic.
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Ethio-MeditechScan: Medical Image Analysis with AI
I engineered this end-to-end medical diagnostic tool to improve the speed and accuracy of healthcare delivery in Ethiopia. The system uses Deep Learning (CNNs) to classify medical images and provide instant insights to healthcare professionals.
Key Technical Features:
AI Engine: Developed a high-performance Convolutional Neural Network (CNN) using TensorFlow and Keras for accurate image classification.
Full-stack Interface: Built a responsive React dashboard that allows doctors to upload scans (X-rays/MRIs) and view AI-driven diagnostic reports in real-time.
Data Processing: Performed extensive data cleaning and augmentation on medical datasets to ensure the model's robustness and reliability.
API Integration: Engineered a FastAPI/Flask backend to serve the model and handle image processing requests with low latency.
This project highlights my ability to bridge the gap between complex Artificial Intelligence research and practical, user-facing medical software.
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Global Superstore: Strategic Sales & Profitability Analysis
I performed a comprehensive analysis of the Global Superstore dataset to uncover hidden trends in sales performance, customer behavior, and regional profitability. This project demonstrates my ability to transform raw data into actionable business intelligence using modern data science techniques.
Key Technical Features:
Data Wrangling: Performed extensive data cleaning and preprocessing using Pandas to handle missing values and inconsistent formatting.
Exploratory Data Analysis (EDA): Conducted deep-dive analysis to identify top-performing product categories and high-growth geographic regions.
Visual Intelligence: Created advanced visualizations using Matplotlib and Seaborn to represent complex sales trends and profit margins clearly.
Business Impact: Developed a data-driven model to identify the key drivers of customer churn and profitability bottlenecks.
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MinTech Exchange: Real-time On-chain Trading Platform
I developed this end-to-end decentralized trading platform to facilitate secure, real-time asset exchange. Built with a modern tech stack consisting of Node.js, React, and Supabase, the platform provides a high-performance experience for tracking and trading on-chain assets.
Key Technical Features:
Real-time Data: Leveraged Supabase Realtime and WebSockets to provide instant price updates and live order book synchronization.
Secure Backend: Built a robust Node.js/Express API to handle transaction signatures and portfolio tracking.
Database Management: Utilized Supabase (PostgreSQL) for secure user data storage, leveraging Row Level Security (RLS) for maximum privacy.
Dynamic Frontend: Created a fast, responsive trading dashboard using React, featuring interactive charts and complex state management.
This project demonstrates my ability to build secure, data-intensive financial applications with a focus on real-time performance.
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