SafeHerd Solutions

Summary; This project develops a machine learning-based web application aimed at distinguishing domestic animals from predators. Utilizing AI algorithms, it analyzes various animal characteristics to categorize them accurately. The platform is designed for educational and research purposes, allowing users to input animal data and receive instant classification. It features a user-friendly interface for data submission and displays results in a clear, concise manner. The application serves as a practical tool for understanding animal behaviors and characteristics, highlighting the potential of AI in wildlife study and education.

Technologies: The animal classification web application leverages a Python backend with TensorFlow for the machine learning model, assisting data analysis and prediction capabilities. The frontend is crafted using HTML, CSS, and JavaScript, providing a dynamic and responsive user interface for interaction and data visualization. Data exchange between the frontend and backend is facilitated through RESTful APIs, ensuring efficient communication. The machine learning model is trained on a dataset of animal characteristics, allowing for accurate classification. Documentation, including details of the model's architecture and training process, is maintained in the project's GitHub repository for easy reference.