Interactive Streamlit app for Latent Class Analysis (LCA) with Bayesian posterior probability computation, designed for diabetes comorbidity classification and outcome rate estimation from fitted model parameters.

This project aims to predict the obesity levels of individuals based on various features such as age, height, weight, and lifestyle factors. Multiple machine learning models are utilized, including Logistic Regression, Support Vector Machine (SVM), and ensemble methods like Bagging with SVM.

Fetal Health Classification