This part of the project applies dimensionality reduction techniques to the RHMCD-20 dataset. The aim is to simplify the dataset by reducing its dimensions while retaining key information, enabling clearer visualization and interpretation of the underlying patterns.

This part project focuses on applying association rule mining to the RHMCD-20 data set, which consists of comprehensive survey data about depression and mental health. The data set captures various factors, such as stress levels, changes in habits, social interactions, and work-related dynamics, providing a rich source of information for analysis.

This project analyzes the RHMCD-20 dataset, focusing on mental health and factors like work stress, coping mechanisms, and social interactions. Using unsupervised learning techniques clustering, association rule mining, and dimensionality reduction it uncovers patterns, relationships, and insights within the data. The findings simplify complex mental health dynamics and offer actionable insights for targeted interventions. The analysis fosters awareness and provides a foundation for future research. Overall, it highlights the power of data-driven approaches in understanding and addressing mental health challenges.