Prasad Nayak

Motivated and job-ready web developer with hands-on experience in HTML, CSS, JavaScript, and Python. Built multiple responsive web projects through certification courses, demonstrating strong skills in clean, efficient coding, and modern web design. Eager to contribute to a dynamic team and grow under experienced mentorship in a full-time role.

Driver Drowsiness System:

• Aimed to reduce road accidents by detecting and mitigating driver fatigue and drowsiness through real-time monitoring and alert systems
• Performed initial data preprocessing by collecting and cleaning image data, including the removal of duplicate images to ensure dataset integrity and accuracy.
• Utilized OpenCV2 for real-time facial expression recognition to monitor driver alertness. Split the processed dataset into training and testing subsets to train the model and evaluate its accuracy.
• Built and trained a deep learning model using TensorFlow's Sequential API to identify patterns indicative of driver drowsiness. The model detects prolonged eye closure as a key feature signaling potential fatigue.

Custom Full Stack Ecommerce Website:

• designed and developed a fully customizable e-commerce platform with a focus on modularity, performance, and admin usability
• Developed backend APIs using Node.js and Express.js, with MongoDB as the primary NoSQL database for efficient data storage and retrieval
• Implemented two separate frontend applications using Next.js: a customer-facing interface for shopping and checkout, and an admin-facing dashboard for managing products, orders, and users
• Deployed the application on AWS Lightsail for scalable and cost-effective cloud hosting, and integrated Razorpay for secure online payment processing.

AI-Based Crop Identification Mobile Application:

• Developed a mobile app that employs a machine learning algorithm to identify crop types from images and deliver useful information about each crop
• Designed and developed the Android application using Android Studio, integrating machine learning, and AI models implemented with TensorFlow
• Utilized MobileNet architecture to build a lightweight, efficient model optimized for mobile and embedded vision applications, and employed Firebase for real-time storage and management of user-generated scan data.

• Programming with JavaScript, 2024
• Version Control by Meta, 2024
• Python Data Analysis, Coursera, 2020
• Python Data Visualization, Coursera, 2020
• Python Data Representation, Coursera, 2020
• Engineering Workshop on Android Game Development using Unity, 2019

• Volunteered in Rotaract.
• Part of the Association for Computing Machinery committee.
• Secured 2nd prize in the intra dance competition.