Problem Statement 

Mental health issues have become a major concern in the growing corporate world. Due to the competitive nature in the workplace, employees face high levels of anxiety, stress, and depression. This not only affects the well-being of the employee, but also has an impact on the overall growth and productivity of the organization. To address this problem, we use data intensive computing techniques to analyze a dataset consisting of the employee data and gain insights about the mental health in the corporate world.

                            Questions to Answer 

How important is mental health in the corporate world? How well are they offering the resources for mental health-related issues? Based on how the company prioritizes mental health and benefits a person, is that person going to take treatment or not?

                              Background 

The mental health problem in the corporate world has been increasing steadily from the past few years. Due to the rapid evolution and growth of the corporate world by new technologies, there are many factors that affect the mental health of the employees. Factors like higher workload, working for long hours, job insecurity, not able to maintain healthy work-life balance cause mental health problem in employees which lead to stress, anxiety and depression. These factors affect employee’s quality and well-being of life and results in lower productivity of the entire organization. With all these factors considered, mental health in the corporate world has become a significant problem and this problem needs to be addressed.

                    Project’s potential contribution 

This project has the potential to significantly contribute to the problem of mental health in corporate world, and its contribution is critical in many ways:

1. By using the data intensive computing approach, our project can identify the causes for mental health issues in employees. We can also find patterns using the data which cannot be found using other conventional traditional methods.

2. We can also build predictive models which identify the mental health risk of employees. By this we can take suitable actions beforehand and prevent further mental health crises.

3. By implementing this project, we can identify the strategies that can be used by the organizations for better well-being of the employees.

4. This project can also contribute to the research on mental health challenges in the corporate world and results can be used for further studies.

                        Data Cleaning/Processing 
   

Understanding the dataset

There are 18 columns in the dataset: -- Age -- Gender -- Country -- self-employed - Are you self-employed? -- treatment – Have you sought treatment for any mental health condition? -- work_interfere - f you have a mental health condition; do you feel that it interferes with your work? -- total_employees - How many employees does your company or organization have? -- tech_company - Is your employer primarily a tech company/organization? -- mental_health_benifits - Does your employer provide mental health benefits? -- resources_to_help - Does your employer provide resources to learn more about mental health issues and how to seek help? -- leave - How easy is it for you to take medical leave for a mental health condition? -- coworkers - Would you be willing to discuss a mental health issue with your coworkers? -- supervisor - Would you be willing to discuss a mental health issue with your direct supervisor(s)? -- mental_vs_physical - Do you feel that your employer takes mental health as seriously as physical health? -- family_history - Do you have a family history of mental illness? -- mental_health_interview - Would you bring up a mental health issue with a potential employer in an interview? -- physical_health_interview - Would you bring up a physical health issue with a potential employer in an interview? -- year