• Savitha K
• Nandini K
• Dr.Chandra Sekar S

Cyber bullying detection is solved in this project as a binary classification problem where we are detecting two major form of Cyber bullying: hate speech on twitter and personal attacks on Wikipedia and classifying them as Cyber bullying or not. The system uses SVM (Support Vector Machine) for hate speech and Random Forest Classifier for personal attacks. It instead of simply looking for patterns, it goes beyond what’s happening in the past to predict future outcomes based on the prediction existing data. The system produces the output set labeled as either offensive or non offensive. SVM aims to minimize an error by generating optimal hyper plane in an iterative manner. The real step towards of a Machine learning model is collecting data, Data preparation, model selection, Feature extraction and Analyze and prediction. Finally saving the trained model module is implemented. This system shows us more accuracy for detection Cyber bullying content. It also helps people from the attacks of social media bullies.

Cyber bullying, Natural Language Processing, Machine Learning, SVM, Random Forest Classifier.

"DETECTION OF CYBERBULLYING ON SOCIAL MEDIA USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.371-378, May-2024, Available :http://www.jetir.org/papers/JETIRGG06059.pdf

"DETECTION OF CYBERBULLYING ON SOCIAL MEDIA USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pp371-378, May-2024, Available at : http://www.jetir.org/papers/JETIRGG06059.pdf