The Rapid Growth Of Social Media And Online Communication Platforms Has Led To An Increase In Cyberbullying Incidents, Negatively Impacting The Mental Health And Well-being Of Individuals, Especially Adolescents. Detecting Cyberbullying Manually Is Challenging Due To The Massive Volume Of User-generated Content And The Subtlety Of Abusive Language. This Project Proposes An Automated System For Cyberbullying Detection Using Machine Learning Integrated With The Flask Framework For Web-based Deployment. The System Leverages Natural Language Processing (NLP) Techniques To Preprocess Textual Data, Including Tokenization, Stopword Removal, And Vectorization, And Applies Machine Learning Algorithms Such As Logistic Regression, Support Vector Machines, And Random Forest To Classify Content As Bullying Or Non-bullying. The Flask Framework Provides A User-friendly Web Interface For Real-time Input And Detection, Enabling Users To Monitor Social Media Posts Or Messages Efficiently. Experimental Results Demonstrate That The Proposed System Can Accurately Identify Cyberbullying Content, Offering A Scalable And Practical Solution To Mitigate Online Harassment And Promote Safer Digital Interactions.
.jpg "DETECTION OF CYBERBULLYING IN FLASK FRAMEWORK USING MACHINE LEARNING")