Due to the pandemic, the world's dependence shifted to online platforms. It has made all age groups vulnerable to cyberbullying. Now more than ever, there is a need for online behavior monitoring. Existing algorithms tend to classify friendly banter as cyberbullying. They make use of binary classification by identifying offensive keywords. The lack of analysis of the context of data posted and the unavailability of public training data makes it challenging to train models accurately. Our models and research focus on the larger picture by making use of context as a significant parameter during the classification. The dataset chosen was such that its annotation was based on 5 parameters that considered the context of conversations happening online. This paper executes various machine learning algorithms, SVM, random forest, AdaBoost, and MLP algorithms, on a benchmark cyberbullying-representations dataset extracted from Twitter. We conducted randomized oversampling on the best-performing SVM model, which resulted in a significantly higher average F1 score outperforming the baseline score.