Analytical Prediction and Sentimental Analysis of virtual engagement tools in teens and youth of urban and rural areas in India

Chatting, educational project discussions, and attending live events via different digital platforms are among the ways that today's youth use technology. Although the utilization of virtual engagement tools by youngsters has been studied in other countries, less is known about the online behaviors of Indian teens and youth in both urban and rural India. So, I have carried out an analysis on the factors “Age, Gender, Area they live (Rural or Urban), familiarity and user satisfaction for educational project discussions platforms, instant messaging and informal chatting platforms, and platforms for attending live virtual events”. Based on these parameters, I created 3 prediction models which utilize supervised learning and involve Logistic Regression, Decision Tree, Random Forest, Linear Kernel SVM, and Polynomial Kernel SVM. Considering the data collected, it predicts “which platform to utilize taking into account the rural and urban India”. In all these three models, the highest accuracies were obtained by the Random Forest algorithm. The highest accuracy obtained by the model which predicts a platform for educational project discussions is ~79%. The highest accuracy obtained by the model which predicts a platform for informal chatting is ~90%. The highest accuracy obtained by the model which predicts a platform for attending live virtual events is ~85%. Sentimental Analysis of these platforms is performed based on the reviews of the respondents. This highlights and helps to gauge how to improve the user experience.