Animal Health Danger Prediction

• Tech Stack: Python, scikit-learn, Pandas, NumPy, Matplotlib, Flask
• GitHub URL: Project Link

Developed a classifier to predict livestock health issues using vital signs and environmental data. Performed feature engineering, handled missing values, and tested Random Forest, SVM, and XGBoost models.

Achieved 92% accuracy with XGBoost after hyperparameter tuning. Deployed as a Flask REST API within a Docker container.

Created a lightweight HTML/JS dashboard to display real-time sensor inputs and model predictions.