Demo#

Jupyter Notebook

Overview#

Mental Health Regression Model is a Random Forest Regression model used to analyze the impact of sleep, work, and physical activity on mental health severity. To achieve this, I performed both univariate and multivariate exploratory data analysis (EDA) to understand the distribution of individual variables and the relationships between them. I cleaned the dataset by addressing missing values and outliers to ensure data integrity and reliability. Additionally, I performed feature engineering to create new meaningful variables and transform existing ones.

I chose Random Forest because it automatically detects complex patterns in the data without manual adjustments and handles messy data like outliers effectively. The model demonstrated strong performance, achieving a high training R2 and a moderate test R2. These results indicate the model effectively captures the variance in the training data but shows some overfitting.

Why#

Mental health is often an overlooked aspect of people’s lives, yet it plays a crucial role in overall well-being. Simple activities like getting good sleep, exercising regularly, and maintaining a healthy work-life balance can make a significant difference in improving mental health. This project explores the relationship between these everyday habits and mental health severity, highlighting the potential for small lifestyle changes to have a big impact. By analyzing this data, I hope to contribute meaningful insights that could help individuals take actionable steps toward better mental health.