The impact of social media on contemporary culture has been unprecedented, making it the most significant medium of our times. While it has had a positive effect on people's worldview, social media has also been linked to a rise in undesirable phenomena such as cyberbullying, cyberstalking, and cybercrime. Cyberbullying, in particular, can have a negative impact on individuals' mental health and has even been identified as the root cause of mental health issues in some cases. The proliferation of sexually explicit comments and the spread of rumors by multiple individuals are some of the negative influences that have been observed in the social media ecosystem. In recent years, academics have been increasingly concerned about the indicators of online harassment. Our goal is to develop a system that can detect instances of online abuse using Natural Language Processing (NLP) and Naïve Bayes, among other techniques. The cultural norms have shifted dramatically due to the rapid transmission of the COVID-19 virus, resulting in a rise in cyberbullying, especially among adolescents. The younger generation is more likely to engage in this practice, which has become more widespread with the stratospheric rise in popularity of various online engagement-promoting platforms. The COVID-19 pandemic has changed the way people interact online and has contributed to an increase in cyberbullying. As more people began working from home, bullying became a more significant concern. Our proposed system includes modules for data cleansing, text mining, word embedding, and regression analysis, among others. We utilize the Lemmatization technique for text mining, which enhances the model's precision. We also utilize the Vader emotion for feature extraction, which generates word vectors that are scattered numerical representations of word attributes. Additionally, Naive Bayes is used for data categorization to prevent overfitting in the proposed model. This would help in creating vectors that connect words with similar meanings

Accurately predicting the travel time between two destinations is an essential aspect of traffic monitoring and facilitating ridesharing services. However, this is a highly complex and challenging task, which involves a multitude of variables that cannot be resolved straightforwardly. Previous studies on travel time prediction have focused on evaluating the duration of individual road segments or specific sub-paths before integrating the necessary time for each sub-path. While this method may provide some insight, it may result in an incorrect or imprecise time estimate. To address this issue, this research aims to utilize machine learning techniques to predict the duration of trips in ride-sharing networks, by utilizing the Uber movement dataset. The proposed system employs Python programming to calculate the distance between the pickup and drop-off locations. Furthermore, the study explores the various factors that affect travel time in a descriptive analysis. This includes examining the impact of traffic congestion, weather conditions, and road construction on travel time. The suggested approach incorporates a robust regression model known as Huber regression to enhance the accuracy of trip duration prediction and increase the precision of the algorithm. The Huber regression model is robust to outliers, making it suitable for the Uber movement dataset, which may contain unexpected and extreme values. The dataset is processed using k-fold cross-validation, which splits the dataset into k subsets, with each subset used for validation once while the remaining subsets used for training the model. However, this approach presents several challenges that need to be addressed, including the difficulties with tracking variables, the need for extensive data transformation due to the diverse data types contained in the dataset, and the challenge of handling unlabeled places during the segmentation of geographical data. Additionally, outliers in the dataset can lead to substantial data differences and affect the model's accuracy. Data normalization is slow due to the time-consuming nature of reading duplicated information. To mitigate these issues, additional study is required to improve the model's layout and address the challenges of working with the Uber movement dataset.