This project implements an end-to-end MLOps pipeline for the UCI Bank Marketing Dataset. The goal is to predict whether a client will subscribe to a term deposit (y). Key Highlights: 1. Advanced EDA: Univariate & Bivariate analysis. 2. Imbalance Handling: Stratified sampling and evaluation using ROC-AUC (SMOTE prepared but disabled due to Windows compatibility). 3. Multi-Model Training: Comparing Decision Tree, Random Forest, Gradient Boosting, and XGBoost. 4. MLOps: Dockerized API serving and CI/CD pipelines.