Drew GiffinStudent Habits and Stress Prediction Analysis
A machine learning project that examines the habits of students with the goal of gaining insight into how their daily routines may affect their stress levels. Habits such as studying, extracurricular involvement, sleep, socialization, and physical activity, as well as performance indicators like GPA, are analyzed to understand their correlation with stress.
Goal
The main goal of this project is to predict a student's stress level, categorized as low, moderate or high, depending on their times spent on different activities and academic performance. By providing clarity on the factors that most influence stress, the project aims to give students actionable context for developing less stressful lifestyles without compromising academic success.
Dataset
The dataset used in this project was sources from Kaggle, containing information about time spent on daily activities, academic performance, and reported stress level. Each entry has figures like time spent studying, sleeping, exersizing, socializing, and participating in extracurricular activities, as well as GPA.
The target variable is the stress level, indicated as low, moderate or high. These features will allow us to explore the correlation between lifestyle patterns, academic performance and stress. They will also help us build a predictive model capable of identifying which habits most strongly influence stress.