A plant disease detection system was developed using a Vision Transformer (ViT) model, achieving 99% accuracy on a comprehensive testing dataset. The model demonstrated exceptional robustness, maintaining over 90% accuracy even with blurred and rotated images, making it highly effective for practical applications.

A text sentiment analysis model was designed and implemented to classify sentiments across diverse text datasets with high accuracy. The project harnessed advanced natural language processing (NLP) techniques to deliver reliable insights, enabling effective interpretation of sentiment patterns in real-world applications.