This project demonstrates an end-to-end MLOps workflow using the UCI Breast Cancer Wisconsin dataset. A Random Forest model is trained to classify tumors as benign or malignant. The pipeline includes data validation, missing value handling, train-test splitting, performance evaluation (Accuracy & Sensitivity), and experiment tracking using MLflow. The project highlights reproducibility, model versioning, and structured machine learning practices in R.