Mental_Health_wellbeing

Designed interactive visualizations and dashboards to analyze mental health trends, involving data cleaning, transforma- tion, and visualization to extract actionable insights.

Comment

Mental Health and Student Wellbeing

🌟 Project Overview

This project delves into mental health patterns among students aged 18–32 using survey data. It provides actionable insights through data visualization and warehousing techniques to foster awareness, reduce stigma, and recommend strategies for enhanced mental well-being.

📊 Key Features

  • Comprehensive Survey Analysis: Unveils insights into anxiety, coping mechanisms, and emotional openness among students.
  • Interactive Dashboards: Visual storytelling with Power BI for better understanding of mental health trends.
  • System Architecture: Clear visualization of the system's design, including workflows and use cases.

⚙️ Technologies Used

  • Programming Languages: Python, VBA (Excel Macros)
  • Data Visualization: Power BI
  • Supporting Tools: Microsoft Excel, Google Forms

🗂 Project Structure

Mental_Health_Dashboard/
├── data/
│   ├── MentalHealth_Survey_Raw.xlsx      # Raw survey data
│   ├── MentalHealth_Cleaned.csv          # Cleaned dataset
│   └── dashboard.pdf                     # PDF containing dashboard visuals
├── code/
│   ├── Mental_Health_Python_Code.py      # Python script for data preprocessing
│   ├── Mental_Health_SQL_Queries.sql     # SQL queries for data analysis
│   ├── Mental_Health_VBA_Code.vba        # VBA macros for Excel processing
│   └── Star_Schema_Creation.sql          # SQL script for star schema creation
├── visuals/
│   ├── system_architecture_design.jpg    # System architecture diagram
│   ├── use_case_diagram.jpg              # Use case diagram
│   └── Project_planing.jpg               # Project planning timeline
├── MentalHealth_Dashboard.pbix           # Power BI dashboard file                          # Project overview and instructions

📐 Diagrams

System Architecture

Below is the visual representation of the system's architecture, highlighting key components and workflows:

System Architecture

Use Case Diagram

The use case diagram outlines the system's primary interactions and functionalities:

Use Case Diagram

Project Planning

Here’s a high-level overview of the project timeline and milestones:

Project Planning

How to Use This Repository

1. Prerequisites

  • Install Microsoft Excel and Power BI Desktop.
  • Set up a Google BigQuery project with the necessary permissions.

2. Run ETL Operations

  • Open the MentalHealth_Survey_Raw.xlsx file in Excel.
  • Use the RenameColumns.vba script to standardize column names.
  • Save the cleaned data as MentalHealth_Cleaned.csv.

3. Data Loading into BigQuery

  • Use the star_schema_creation.sql script to define the schema in BigQuery.
  • Import the MentalHealth_Cleaned.csv file into the appropriate fact and dimension tables.

4. Data Visualization in Power BI

  • Launch the MentalHealthDashboard.pbix file in Power BI Desktop.
  • Connect the dashboard to your BigQuery project.
  • Interact with the dashboard to uncover meaningful insights.

Key Results and Insights

  1. Higher Anxiety Levels in Younger Students: Students aged 18–25, particularly in high-stress academic fields, reported elevated anxiety levels.
  2. Positive Impact of Physical Activity and Family Support: Regular exercise and strong family support were strongly associated with better mental health.
  3. Workload and Stress Correlation: Heavy academic workloads were a major driver of anxiety and reduced mental well-being.
  4. The Role of Community and Peer Networks: Students with access to supportive peer networks and communities reported lower stress levels and better overall mental health.

Future Enhancements

  • Broadening the dataset to cover more diverse demographics, including variations in age, geography, and academic disciplines.
  • Implementing advanced machine learning techniques to predict mental health trends and patterns.
  • Automating data pipelines for real-time integration and analysis of incoming survey responses.

Acknowledgments

  • Survey Participants: Grateful appreciation to everyone who contributed to the survey for their valuable input.
  • Technologies and Platforms: Google Forms, Microsoft Excel, VBA, Google BigQuery, Power BI for enabling this project.

License

This project is distributed under the MIT License. For details, refer to the LICENSE file.

Built With

Share this project:

Updates