Shopping Application
- Developed a full-stack shopping website project using the MERN (MongoDB, Express.js, React.js, Node.js) stack.
- Implemented responsive and user-friendly front-end interfaces using React.js, HTML, CSS, and JavaScript.
- Utilized Express.js and Node.js to build a robust and scalable back-end server to handle user requests and manage database operations.
- Integrated MongoDB as the database to store and retrieve product information, user profiles, and order details.
- Implemented user authentication and authorization functionalities using JSON Web Tokens (JWT) for secure login and access control.
- Integrated third-party APIs for features like payment gateways, shipping services, and social media login.
- Utilized Redux for state management, ensuring seamless data flow across different components of the application.
- Implemented cart functionality, allowing users to add, update, and remove items from their shopping cart.
- Conducted thorough testing and debugging, ensuring high-quality and error-free code.
- Collaborated with a team of developers, designers, and project managers to deliver the project within the specified timeline.
Potato Disease Classification
- Developed a Potato disease classification project utilizing Python and Convolutional Neural Networks (CNN) for accurate disease identification in potato crops.
- Preprocessed and augmented a large dataset of potato disease images to enhance model training and performance.
- Implemented a CNN model architecture using popular deep learning framework TensorFlow.
- Fine-tuned the CNN model on the potato disease dataset to optimize its ability to accurately classify different types of diseases.
- Conducted extensive hyperparameter tuning to improve the model's performance in terms of accuracy and generalization.
- Integrated the trained CNN model into a user-friendly web or mobile application interface for convenient disease classification.
- The user can upload a picture of a potao leaf, rapidly determine whether the crop has been infected and also receive treatment suggestions.
- Implemented appropriate evaluation metrics and conducted rigorous testing to validate the model's performance and robustness.
- Documented the entire development process, including model architecture, data preprocessing techniques, and training methodologies, to facilitate future maintenance and scalability.