This report presents a comprehensive analysis of cybersecurity data using various machine learning algorithms. The study begins with an executive summary, followed by an introduction to the problem and a literature review that explores the application of classification algorithms to text data, particularly from social media platforms. The report then delves into technical demonstrations, providing code examples and explanations for algorithms like Decision Tree, Logistic Regression, Random Forest, Naive Bayes, and K-Means. A detailed performance evaluation section compares the algorithms using metrics such as AUC, ROC, and confusion matrices. The report concludes with a summary of findings and insights into the effectiveness of each algorithm in classifying spammer and non-spammer tweets. The report also includes references to the literature that supports the analysis.
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