Exercise 1 – malware detection

In this exercise, we will consider a dataset of executable files and attempt to find the malicious ones among them. We will achieve this by leveraging the Random Forest algorithm. Before delving into the code section by section, let’s elucidate our rationale for selecting the Random Forest algorithm. Random Forest, an ensemble learning algorithm, constructs numerous decision trees and amalgamates their predictions to enhance accuracy and mitigate overfitting. Renowned for its robustness in ML applications for malware detection, Random Forest handles extensive datasets—a critical attribute in ML implementation—while offering commendable generalization and resistance to overfitting. Its consistent performance across diverse datasets underscores its preferability over alternative algorithms in malware detection tasks. However, optimal algorithm selection for malware detection hinges on dataset characteristics, task-specific requirements...