This repository encompasses the Master of Science in Data Science's DATS6101 subject's endterm project for Semester 1 in Fall 2022. Our project sheds light on the issue of adolescents and their likeliness of engaging in risky behaviours through extensive yet relevant exploratory data analysis and predicts the possibility of exhibition of such behaviour using machine learning algorithms.

We have plotted the entirety of the data and streamlined the data through relevant and sensible graphical representations, which help any human being make sense of the plethora of information by means of plotted graphs and narrowed down numerical values predicted by the algorithm.

To perform the analysis and present the analyzed data, the following languages, tools, and formats were used: R language, R studio, R Markdown, Linear Regression, Logistic Regression, Decision Tree and Random Forest and lastly Microsoft Power Point.

Please navigate to the root of the project directory in order to dive deeper into the finer details of the complete implementation.