Mental Health Analysis - Students and Tech Employees

This project focuses on analyzing mental health data collected from students and tech employees. The primary goal is to showcase data wrangling skills by gathering, assessing, and cleaning datasets. Afterward, the project aims to answer key research questions through visualizations and data analysis.

Datasets Used:

1. Students Mental Health Assessments

   • Source: Kaggle - Students Mental Health Assessments
   • This dataset contains information about students' mental health, including stress levels, depression and anxiety scores, sleep quality, physical activity, and more.

2. Tech Employees Mental Health

   • Data collected through a survey of tech employees, analyzing factors such as remote work arrangement, social support levels, and the use of counseling services.

Key Features of the Project:

• Data Wrangling & Cleaning:
  This project involves assessing the datasets for quality and structural issues, followed by applying a cleaning strategy to ensure the data is suitable for analysis.

• Exploratory Data Analysis (EDA):
  The project includes a deep dive into the data to identify patterns and trends.

• Research Questions Explored:

  • Does the remote work arrangement affect the likelihood of using counseling services?
  • Does having a family history of mental health issues impact the use of counseling services?
  • Is there a difference in social support levels between students and employees?

• Potential Future Analysis:

  • How does age relate to the likelihood of using counseling services?
  • Are there any associations between social support levels and family history of mental health issues?

Installation & Setup:

To run this project locally:

1. Clone this repository:

   git clone https://github.com/ahmadsaad2/Mental-Health-Analysis-Students-Tech-Employees.git

2. Navigate to the project directory:

   cd Mental-Health-Analysis-Students-Tech-Employees

3. Install the required libraries:

   pip install -r requirements.txt

Technologies Used:

• Python: Main programming language
• Libraries:
  • pandas: Data manipulation
  • numpy: Numerical computations
  • matplotlib, seaborn: Data visualization
  • scikit-learn: Machine learning (if applicable)

Project Goals:

By working on this project, I aimed to:

• Clean and analyze mental health-related data.
• Explore and visualize relationships between variables like age, stress levels, family history, and social support.
• Answer key research questions and discuss the implications of these findings.

Contributions:

Feel free to fork this repository, open issues, or submit pull requests. All contributions are welcome!

License:

This project is licensed under the MIT License - see the LICENSE file for details.