ARIMA plot airpassenger data

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.

his page presents an exploratory multivariate analysis of the SIRTUIN6 dataset using R. The analysis includes correlation matrix, variance–covariance matrix, and eigen decomposition to examine the structure of relationships and variance among numerical variables. Prepared as a student worksheet for Multivariate Analysis course.