Skip to content
View achalnm's full-sized avatar

Block or report achalnm

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don't include any personal information such as legal names or email addresses. Markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
achalnm/README.md

MSc Computing (Data Analytics) Student | Dublin City University, Ireland

Data enthusiast, AI/ML practitioner, and cloud computing explorer with hands-on experience in real-time edge AI deployment, full-stack development, and data analytics. Skilled in Python, SQL, C++, and JavaScript, with practical expertise in TensorFlow Lite, Scikit-learn, React, Django, and AWS/Azure/IBM Cloud. Passionate about building impactful solutions, delivering robust applications, and leveraging data to drive informed decisions. Published researcher with real-world project experience, continuously upskilling across emerging technologies.


🌐 Connect with Me:

Email LinkedIn Resume


💻 Tech Stack:

Python R C++ JavaScript Java

TensorFlow Scikit-learn XGBoost OpenCV

AWS Azure IBM Cloud Docker

React Django HTML5 CSS3

MySQL PostgreSQL MongoDB

Unreal Engine 5 Figma Canva


📌 Highlights:

  • Published ML research: Developed Currency Detection for the Visually Impaired using MobileNetV2/TFLite on Raspberry Pi, achieving 98.35% accuracy on 3,000+ test images with sub-second end-to-end recognition and voice output.
  • Deployed real-time AI/ML edge models on Raspberry Pi with robust performance under constrained hardware, demonstrating practical IoT/AI deployment skills.
  • Built full-stack applications using Django/React, integrating SQL/PostgreSQL databases with 100+ concurrent users, scalable cloud deployment on AWS, Azure, and IBM Cloud.
  • Developed AI Hand Gesture Recognition System, trained on 2,000+ custom images with ~95% recognition accuracy, enabling real-time gesture-controlled accessibility tools.
  • Prototyped SupFoodie AI WhatsApp chatbot and Munch Maps food locator, supporting 500+ unique queries and 75+ eateries, optimizing personalized search under 1 second.
  • Google Maps Local Guide Level 10 contributor with 82 million views, actively reviewing and mapping locations to improve user experience.
  • Completed 50+ certifications in Python, Data Science, ML, Cloud Computing, and Web Development, demonstrating continuous upskilling and versatility across technologies.

Pinned Loading

  1. Currency-Recognition-RPi Currency-Recognition-RPi Public

    Portable offline AI-powered currency recognition system for visually impaired users using Raspberry Pi and MobileNetV2 CNN

    Python

  2. HandGesture-VolumeControl HandGesture-VolumeControl Public

    This project uses a webcam to control PC volume based on thumb direction. The model recognizes hand gestures, increasing volume with an upward thumb and decreasing it with a downward thumb. Impleme…

    Python

  3. MunchMaps MunchMaps Public

    A database-driven food locator application utilizing geospatial search and personalized filters. Built with MySQL and Python, it delivers optimized, relevant results, offering efficient and user-fr…

    JavaScript

  4. EventEcho-Xampp-Apache-and-MySQL EventEcho-Xampp-Apache-and-MySQL Public

    An event management website using XAMPP, Apache, PHP, and MySQL. Features user registration, event management, and secure login. Easily set up with provided instructions and sample data.

    PHP

  5. DSV-project DSV-project Public

    A series of data science projects demonstrating statistical analysis, machine learning model evaluation, data cleaning, and visualization techniques on diverse datasets including Titanic, Iris, and…

    Python

  6. Eye-State-Detector Eye-State-Detector Public

    This project is an eye detection application using OpenCV and Django. It allows users to upload images and detects whether eyes are open or closed.

    CSS