Student depression risk analysis using SQL, Python, and Tableau to identify academic, lifestyle, and psychological factors associated with depression and to build a composite risk score for student populations.

An interactive Power BI dashboard analysing the relationship between sleep duration, quality, stress levels, BMI categories, and occupation using the Sleep Health and Lifestyle dataset. The project provides insights into lifestyle factors influencing sleep health, including correlations with stress and occupation,.

Visual exploration of obesity trends using the UCI Obesity Dataset. This project analyzes how demographics, physical activity, and dietary habits influence obesity levels, producing reproducible and accessible visualizations. Key insights highlight weight, activity, and sedentary behavior as primary contributors to obesity patterns.

A data-driven deep dive into the correlation between professional stress, lifestyle habits, and sleep quality. Features Exploratory Data Analysis (EDA) and health metric visualization using Python.

Machine Learning system to predict health risks based on lifestyle habits using various classification models.