Remote Work and Mental Health: A MongoDB Database Project

Overview

This project explores the relationship between remote work and mental health using a dataset sourced from Kaggle. The dataset contains information about employees' work conditions, mental health status, and related factors, such as work-life balance, stress levels, and company support for remote work. The goal is to derive meaningful insights through MongoDB queries and showcase the results for a database systems course.

Project Objectives

• Understand the impact of remote work on mental health and related factors.
• Perform aggregation queries to analyze the dataset and derive insights.
• Store the query results as JSON files for further analysis or visualization.

Dataset Description

Source: Kaggle
Number of Rows: ~5000
Key Columns:

• Employee_ID: Unique identifier for each employee.
• Age, Gender, Job_Role, Industry: Demographic and professional data.
• Work_Location: Remote, Hybrid, or Onsite.
• Stress_Level: High, Medium, or Low.
• Mental_Health_Condition: E.g., Anxiety, Depression.
• Company_Support_for_Remote_Work: Rating (1-5).
• Other factors like work-life balance, physical activity, and sleep quality.

Download Link: https://www.kaggle.com/datasets/waqi786/remote-work-and-mental-health/data

Setup Instructions

Prerequisites

• MongoDB: Install MongoDB and ensure it's running.
• MongoDB Compass or VS Code: For query execution and database management.
• Node.js (optional): If running the project with a script.

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/remote-work-mental-health.git
   

2. Import the dataset into MongoDB: mongoimport --db RW&MH --collection relation --type csv --headerline --file /path/to/dataset.csv

3. Open and execute the queries in the queries.mongodb.js file using VS Code or Compass.

Queries

Queries are saved as:

Project Queries.mongodb

Query Outputs

Query results are saved as:

Combined results: query-results.json Individual query results: q1.json to q12.json

Future Enhancements

Visualizations: Use tools like Tableau, Power BI, or Python (e.g., Matplotlib, Seaborn) to visualize the insights.

Frontend: Build an interface (e.g., with React.js) for interactive query execution and result display.

Contribution

This project is open for collaboration! Feel free to fork, clone, or contribute by submitting a pull request. For questions or feedback, contact:

Name: Taha Bin Tazeem Email: tahabintazeem@gmail.com GitHub: @tahabintazeem

License

This project is licensed under the MIT License.