Mental Health and Technology Usage Analysis
Applied Multivariate Analysis
Exploring the Impact of Technology on Mental Health Using Machine Learning and Statistical Analysis
This project explores the relationship between technology usage and mental health using the Mental Health and Technology Usage Dataset from Kaggle. By employing statistical analysis and machine learning models, the study investigates key factors such as screen time, stress levels, physical activity, and access to support systems. The goal is to identify significant patterns and associations that highlight both risks and protective factors in digital habits, offering actionable insights to improve mental well-being in the context of increasing technological integration in daily life.
Methodologies Used and Results
Machine Learning Models
- Logistic Regression
- Decision Trees
- Random Forest
- K-Means Clustering
- Principal Component Analysis (PCA)
Statistical Methods
- ANOVA (Analysis of Variance)
- t-Test
- Chi-Squared Test
Results and Insights
The analysis revealed no significant relationship between screen time and stress levels, but access to support systems was strongly associated with better mental health outcomes. Machine learning models provided insights into behavioral patterns, identifying groups based on technology usage and support systems. These findings highlight the importance of exploring additional factors, such as socio-economic data, to better understand the complex relationship between technology usage and mental health.