Mental Health Dataset Analysis

Project Overview

This project involves analyzing the Mental Health Dataset using SQL for in-depth data analysis and Tableau for data visualization. The goal is to uncover insights into mental health trends, factors, and patterns to better understand the impact on various demographics.

Project Objectives

  • Utilize SQL for data cleaning, analysis, and extracting meaningful insights.
  • Leverage Tableau to create visualizations that effectively communicate findings.
  • Identify key factors affecting mental health across different groups and analyze trends over time.

Why I Chose This Project

Mental health is a critical yet often overlooked aspect of well-being, especially in today's fast-paced world. By exploring this dataset, I aim to highlight important trends and provide actionable insights that can contribute to a better understanding of mental health issues. This project allows me to apply my SQL and Tableau skills to a topic that has significant societal relevance.

Key Learnings

Through this project, I gained valuable experience in:

SQL Proficiency

  • Performing complex queries and analysis on real-world data.
  • Cleaning and preprocessing datasets to make them analysis-ready.

Tableau Visualization

  • Building interactive and insightful visualizations.
  • Effectively communicating data-driven insights through visual storytelling.

Data Challenges

  • Handling missing values and deriving meaningful insights from raw data.

Dataset

The dataset used in this project can be found on Kaggle:

How the Dashboard Looks

Trends Over Time

Interactive Dashboard

Explore the full interactive Mental Health Analysis dashboard on Tableau Public:

GitHub Analysis

For a detailed look at the code and analysis process, visit my GitHub repository:

In the repository, you'll find:

  • SQL Scripts: For data cleaning, transformation, and analysis.
  • Tableau Workbook: Containing the visualizations.
  • Documentation: Detailed explanations of the methodology and findings.

Tools and Technologies

  • SQL: For data cleaning, analysis, and deriving insights.
  • Tableau: For visualization and storytelling.
  • PostgreSQL: For database management and query execution.

Conclusion

By the end of this project, I gained a comprehensive understanding of the mental health landscape and developed actionable recommendations that can be used by policymakers, healthcare professionals, or organizations to address mental health challenges more effectively.