Designed and developed a comprehensive database management system for a medical store. The system effectively stores, retrieves, and updates records of available stock, sales transactions, and supplier information. I implemented relational database design principles to create efficient data models, ensuring data integrity and facilitating quick queries for inventory tracking and management. Challenges included optimizing database queries for performance, implementing secure user authentication for different access levels (admin, staff), and creating a user-friendly web interface for managing complex data. This project showcases proficiency in full-stack web development fundamentals and practical database application.

Conceptualized and built an intelligent system for identifying plant leaf diseases using image analysis. I developed a Convolutional Neural Network (CNN) model in Python, trained on a diverse dataset of plant leaf images with various disease pathologies. The model achieves high accuracy in classifying common plant diseases. The neural network was then integrated into a Django web application, allowing users to upload images and receive instant diagnostic predictions. Key challenges involved sourcing and preprocessing a large, varied dataset, fine-tuning the CNN architecture for optimal performance, and deploying the model efficiently within a web framework. This project highlights expertise in machine learning model development, computer vision, and web application integration.