This tutorial provides a fully self-contained implementation and explanation of the DL2 composite indicator algorithm. It introduces the theoretical foundation of DL2 as a weighted Minkowski norm, outlines its connection with modern composite indicator methodology, and demonstrates how the method integrates polarity-based normalization, powered distances, Fréchet-type aggregation, and machine-learning-based weighting via MARS. The document guides the reader step-by-step through the improved DL2 workflow, including convex weight enforcement, adaptive smoothing, and iterative convergence assessment. A simulated dataset is used to illustrate each component of the algorithm, offering a practical and reproducible example suitable for methodological learning, benchmarking, and applied research.

The report utilizes the Global Cybersecurity Threats Dataset (2015-2024) that provides extensive data on cyberattacks, malware types, targeted industries, and affected countries. It is designed for threat intelligence analysis, cybersecurity trend forecasting, and machine learning model development to enhance global digital security. This report was developed using R Quarto for reproducible analytics. All visualizations were created using the R ggplot2 and plotly packages.

Supermarkets sales analysis

The map uses UFI dataset to show which city held which exhibition in 2024

instruction: classify new "test" documents using already classified "training" documents. A common example is using a corpus of labeled spam and ham (non-spam) e-mails to predict whether or not a new document is spam. For this project, you can start with a spam/ham dataset, then predict the class of new documents (either withheld from the training dataset or from another source such as your own spam folder)