Diabetes

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The dataset comprises 250,000 records and includes information on various health-related factors and conditions, designed to facilitate diabetes prediction and analysis. The dataset includes the following features:

1. Diabetes_012: A categorical variable indicating the presence of diabetes, with possible values of 0 (no diabetes), 1 (pre-diabetes), or 2 (diabetes).
2. HighBP: A binary indicator of whether the individual has high blood pressure.
3. HighChol: A binary indicator of whether the individual has high cholesterol.
4. CholCheck: A binary variable denoting whether the individual has had a cholesterol check in the past year.
5. BMI: The body mass index of the individual, a continuous variable used to categorize weight status.
6. Smoker: A binary indicator showing if the individual is a smoker.
7. Stroke:A binary variable indicating whether the individual has experienced a stroke.
8. HeartDiseaseorAttack: A binary indicator for the presence of heart disease or a heart attack.
9. PhysActivity:A binary variable showing whether the individual engages in physical activity.
10. HvyAlcoholConsump:A binary indicator of heavy alcohol consumption.
11. AnyHealthcare: A binary variable indicating whether the individual has access to any form of healthcare.
12. NoDocbcCost: A binary indicator of whether the individual did not visit a doctor due to cost concerns.
13. GenHlth: A categorical variable representing the individual's general health status, ranging from excellent to poor.
14. MentHlth: A continuous variable indicating the number of days in the past month the individual experienced poor mental health.
15. Sex: The gender of the individual.
16. Age: The age of the individual.
17. Income:The individual's income level, categorized into brackets.
18. Diabetes_binary:A binary outcome variable indicating the presence or absence of diabetes (1 for diabetes, 0 for no diabetes).

This dataset is intended for research and analysis in the field of health and diabetes prediction. It provides a comprehensive view of various health indicators and conditions, making it suitable for developing predictive models and understanding the relationship between diabetes and other health factors.