Abstract
This research paper delves into the intricate domain of cyberbullying detection on social media, addressing the pressing issue of online harassment and its implications. The study encompasses a comprehensive exploration of key aspects, including data collection and preprocessing, feature engineering, machine learning model selection and training, and the application of robust evaluation metrics. The paper underscores the pivotal role of feature engineering in enhancing model performance by extracting relevant information from raw data and constructing meaningful features. It highlights the versatility of supervised machine learning techniques such as Support Vector Machines, Naïve Bayes, Decision Trees, and others in the context of cyberbullying detection, emphasizing their ability to learn patterns and classify instances based on labeled data. Furthermore, it elucidates the significance of evaluation metrics like accuracy, precision, recall, F1-score, and AUC-ROC in quantitatively assessing model effectiveness, providing a comprehensive understanding of the model's performance across different classification tasks. By providing valuable insights and methodologies, this research contributes to the ongoing efforts to combat cyberbullying, ultimately promoting safer online environments and safeguarding individuals from the pernicious effects of online harassment.