Prediction of Student Alcoholism and Academic Performance

This study examines the concerning rise of alcohol consumption among high school students and its negative consequences on academic achievement. We explore the influence of socio-educational factors, including socioeconomic background, peer relationships, and school environment, on alcohol use patterns. Additionally, we investigate the moderating role of parental engagement and family dynamics in the relationship between alcohol consumption and academic performance. Utilizing the Student Alcoholism and Academic Performance Dataset, we employ data preprocessing techniques, we address missing data, convert categorical variables into a suitable format for modelling, and normalize numerical features to prevent bias. Through exploratory data analysis, we gain insights into descriptive statistics, data distributions, and correlations, revealing the extent of alcohol's impact on academic outcomes. Dimensionality reduction via Principal Component Analysis (PCA) streamlines the dataset for further analysis. We implement the Random Forest Regression algorithm to develop predictive models for academic performance based on the identified socio-educational factors and alcohol consumption habits. Evaluating model performance using mean squared error calculations, we aim to provide actionable insights for designing targeted interventions. These interventions will focus on mitigating the detrimental effects of adolescent alcohol use on student success and promoting a healthier learning environment.