Depression Dataset Analysis
This dataset contains various lifestyle, health, and socioeconomic factors linked to mental health. It includes attributes like age, education, income, employment, sleep, and more. A key feature is the identification of individuals with a history of mental illness.
Dataset Overview
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Number of Rows: 365,467
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Number of Columns: 17
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Key Columns:
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Age
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Marital Status
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Education Level
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Income
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Smoking Status
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Physical Activity
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Sleep Patterns
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History of Mental Illness
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Chronic Medical Conditions
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Objective: Investigating how different variables affect mental health and well-being.
Dataset Structure
Python Code Used: .info() and .head()
Details: The dataset contains mostly categorical and numeric data types. No missing values detected in any column.
Statistical Summary
Python Code Used: .describe()
Details: The numerical features (Age, Income) display typical statistical properties (mean, min, max). Average age is 49 years, and the average income is around $50,662. Some columns (like the number of children) are important in further analysis.
Other investigations conducted on the Dataset
There were no empty rows or columns. All columns had consistent data types: object, int64, and float64, with 413,768 rows in each column.
Distribution of Mental Health History
Findings: About 30.4% of the individuals in the dataset have a history of mental illness, while 69.6% do not.
Average Income by Mental Health History
Findings: Individuals with a history of mental illness have significantly lower incomes ($42,254) than those without ($54,335).
Age Distribution by Mental Health History
Findings: Both groups (with and without mental illness) display similar age distributions, with most individuals in their late 40s.
Physical Activity by Mental Health History
Findings: Those with mental health issues are slightly more sedentary compared to those without.
Sleep Patterns by Mental Health History
Findings: Individuals with mental illness report worse sleep quality (33% report poor sleep) compared to those without.
Average Income by Education Level
Findings: Individuals with PhDs have the highest average income ($104,619), followed by those with Master's and Bachelor's degrees.
Income Distribution for Employed vs Unemployed
Findings: Unemployed individuals earn significantly less than those employed, with much smaller variance in income.
Alcohol Consumption by Mental Health History
Findings: Both groups have similar alcohol consumption patterns, with a slight increase in high consumption for those with mental illness.
Smoking Status by Mental Health History
Findings: The smoking status between the two groups is relatively similar.
Employment Status by Mental Health History
Findings: Individuals with mental illness are more likely to be unemployed (46%) compared to those without mental illness (31%).
Distribution of Number of Children by Mental Health History
Findings: Both groups have similar distributions in terms of family size.
Family Size by Income
Findings: Larger families tend to have higher incomes, but income stabilizes at a certain family size.
Alcohol Consumption vs Income
Findings: Income does not vary drastically across alcohol consumption levels.
Marital Status by Mental Health History
Findings: Married individuals form the largest group for both mental health statuses.
Relationship Between Age and Income
Findings: There’s a slight positive correlation between age and income, peaking around middle age.
Key Findings and Outcomes
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Individuals with a history of mental illness earn significantly less than those without.
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Unemployment rates are higher among those with mental health issues.
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Physical inactivity is more common in individuals with mental illness.
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Poor sleep quality is strongly linked to a history of mental illness.
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Higher education correlates with lower rates of mental illness.
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Alcohol consumption shows no major differences across mental health groups.
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Family size does not significantly impact mental health outcomes.
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Marital status has no strong effect on mental health.
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Mental health challenges are present across all age groups.