Predicting Health Score with Streamlit ML Model

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This presentation provides an overview of a project aimed at predicting health condition data using machine learning. It covers the objectives, data preparation steps including cleaning and scaling a health dataset with key features like age and BMI, as well as a comparison of logistic regression, random forest, and gradient boosting models. Key evaluation metrics, such as accuracy and ROC, are discussed, and the deployment of a user-friendly prediction interface using Streamlit is demonstrated.