End-to-end Machine Learning project for Titanic survival prediction using ensemble models (Logistic Regression, Random Forest, KNN, XGBoost) deployed with Streamlit.
Modelo predictivo XGBoost para estimar el IPM continuo usando variables del hogar. Incluye análisis SHAP para interpretabilidad.
This repository serves as a collection of my work and learning in machine learning while my internship in Cellual-Technologies, including algorithm explanations, data preprocessing workflows, and two projects.
AI‑powered web app to forecast UK voter behaviour and spark transparent, non‑prescriptive political reflection.
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