the project aims to uncover insights into the prevalence of mental health issues, factors influencing them, and potential coping strategies employed by participants, please refer to the GitHub repository linked here.

Depression and Mental Health Data Analysis

Dataset Overview

The RHMCD-20 dataset was used for the analysis, containing survey data related to depression and mental health. The dataset includes information from various demographics, such as age, gender, occupation, and responses to survey questions related to mental health indicators during quarantine.

Steps Taken

Step 1: Data Exploration

1. Size of the Dataset: Checked the size of the dataset (number of rows and columns).
2. Column Exploration: Explored the columns and their data types.
3. Missing Values and Duplicates: Checked for missing values and duplicates (none found).
4. Descriptive Statistics: Described the dataset to understand central tendencies and variability.

Step 2: Exploring Demographics

1. Distribution Across Demographics: Explored the distribution of participants across different age groups, genders, and occupations.
2. Prevalence of Mental Health Indicators: Analyzed the prevalence of specific mental health indicators by demographics.

Step 3: Descriptive Analysis

1. Prevalent Mental Health Indicators: Analyzed prevalent mental health indicators reported by participants.
2. Days Spent Indoors: Explored the distribution of the number of days participants spent indoors during quarantine.
3. Impact of Quarantine Frustrations: Examined the impact of quarantine frustrations on mental health indicators.

Step 4: Perceptions and Coping Mechanisms

1. Social Interaction and Support: Explored participants’ perception of social interaction and support.
2. Coping Mechanisms: Analyzed coping mechanisms reported by participants during quarantine.

Step 5: Analyzing Trends Over Time

1. Changes Over Time: Examined how the prevalence of mental health indicators has changed over time.
2. Identifying Trends: Investigated noticeable trends or patterns in the data.

Step 6: Specific Inquiries

1. Extreme Mood Swings: Calculated the proportion of participants reporting extreme mood swings.
2. Factors Influencing Mood Swings: Analyzed factors associated with a higher likelihood of extreme mood swings.

Challenges Faced

• Dealing with categorical data and ensuring proper encoding.
• Identifying meaningful patterns in the data without causal inference.

Conclusions

• Participants with higher levels of quarantine frustrations tend to report increased stress and changes in habits.
• Weight changes during quarantine are prevalent among participants, and further analysis by mental health history can provide insights.
• Extreme mood swings are reported by approximately 32% of participants.
• Coping struggles and changes in work interest and social weakness are prevalent among participants.

Author

• Name: Mohammed Mebarek Mecheter
• Email: mohammedmecheter@gmail.com
• GitHub: Mohammed Mebarek Mecheter

Feel free to use, modify, and extend this analysis for further research.