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)

Testing Hypothosisis