Comparative Analysis of Cyber Crime Breaches Using Random Forest Over Decision Tree to Improve Accuracy

Random Forest is preferred over Decision Tree in a comparative examination of cybercrime breaches in order to increase accuracy. A unique innovative RF-Classifier is employed to attain accuracy. A total of 20 samples are taken for the Random Algorithm (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{N} \boldsymbol{=} \mathbf{1 0}$</tex>) and Decision Tree Algorithm (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{N} \boldsymbol{=}$</tex> 10) calculations, which are done utilizing two groups. The Kaggle Library's cyber breaches dataset is used to test accuracy and loss. The Random Forest method yielded a 99.39% success rate and a 1.61% loss compared to the Decision Tree algorithm's 91.60% success rate and 9.49% loss. Ultimately, the significance value obtained by the Random Forest and Decision Tree algorithms with Independent Samples T-test is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{p}=\mathbf{0. 3 7 1}(p&gt;0.05)$</tex>, indicating that two groups are statistically not significant.