Depression Dataset

Dataset Overview (Synthetic)

This dataset contains information on individuals with various attributes related to their personal and lifestyle factors. It is designed to facilitate analysis in areas such as health, lifestyle, and socio-economic status.

Features

• Name: The full name of the individual.
• Age: The age of the individual in years.
• Marital Status: The marital status of the individual. Possible values include Single, Married, Divorced, and Widowed.
• Education Level: The highest level of education attained by the individual. Possible values include High School, Associate Degree, Bachelor's Degree, Master's Degree, and PhD.
• Number of Children: The number of children the individual has.
• Smoking Status: Indicates whether the individual is a smoker or not. Possible values are Smoker,
  Former and Non-smoker.
• Physical Activity Level: The level of physical activity undertaken by the individual. Possible values include Sedentary, Moderate, and Active.
• Employment Status: The employment status of the individual. Possible values include Employed and Unemployed.
• Income: The annual income of the individual in USD.
• Alcohol Consumption: The level of alcohol consumption. Possible values include Low, Moderate, and High.
• Dietary Habits: The dietary habits of the individual. Possible values include Healthy, Moderate, and Unhealthy.
• Sleep Patterns: The quality of sleep. Possible values include Good, Fair, and Poor.
• History of Mental Illness: Whether the individual has a history of mental illness. Possible values are Yes and No.
• History of Substance Abuse: Whether the individual has a history of substance abuse. Possible values are Yes and No.
• Family History of Depression: Indicates if there is a family history of depression. Possible values are Yes and No.
• Chronic Medical Conditions: Whether the individual has chronic medical conditions. Possible values are Yes and No.

Usage

This dataset is intended for use in analyzing various health, lifestyle, and socio-economic factors. It is suitable for tasks such as predictive modeling, clustering, and exploratory data analysis.