Prediction of Cyberbulling Using Random Forest Classifier

The people who already know each other through any source like from schools or colleges can bully each other which is the pervasive as well as constant. In this type of bully, the person who is bullying the person will follow every time through internet and creates problem to him. The bully person can be very intelligent, he knows all the schemes to create problem to victim and can easily hides his identity. There are enormous types of applications developed and still developing by which the data can be easily generated. It gives rise to a term known as Data Mining. It is the process by which useful information and patterns are extracted from the large amount of data beings stored in the databases. Data Mining is also known as knowledge discovery process as knowledge is being extracted or patterns are analyzed which is very useful to collect data. Many classifiers are being used in the prediction of cyberbulling such as Naïve Bayes, SVM, KNN etc. In this work, Random Forest Classifier is implemented and results are analyzed in terms of accuracy, precision, f-measure and recall. Keywords: Cyberbulling, Data Mining, Naïve Bayes, Random Forest