Factors Influencing Stress

This analysis was done using Python (pandas, numpy, matplotlib and seaborn) on Kaggle notebook.

Goal:
Gain insights on the factors that may or may not influence stress.

Data:
The dataset (Mental Health Dataset) is sourced from Kaggle. The respondents were mainly from the US. It included surveys from 2014 - 2016 (majority 2014).

Findings:

1. Employment
The dataset does not provide strong indications that occupations directly impact on stress level and work motivation.

2. Sex
The dataset overrepresented males by 5:1 ratio to females. This extreme skew would not have resulted in a fair analysis. Males and females experience the world differently, which also means their mental wellbeing is influenced differently. This is a recommended area for improvement for data collection to include more data from females.

3. Family history
The dataset contains respondents who mainly did not have any family history of mental illnesses. It is found that family history did not contribute to higher occurrence of mental ailment. But rather, there is a correlation between not having family history with increased stress level.

4. Living circumstances
The dataset could not provide any insights on how seasonal changes might influence stress level since the dataset was predominantly collected in August. However, it did suggest that spending time outdoors daily can greatly improve stress level. It revealed that by remaining indoors for as little as 2 days resulted an increase in stress level.
Additionally, being open to changing habits is positively correlated to decreased stress level. Consistently, refusing to change habits resulted in increased stress level.