Obesity Prediction Dataset

Overview of the Dataset

This dataset contains 17 attributes and 2,111 records, with each record classified into seven obesity levels based on the target variable, NObesity (Obesity Level). These levels range from Insufficient Weight to Obesity Type III, offering granular insights into obesity patterns.

Detailed Data Attributes

1. Gender: Gender of the individual.
2. Age: Age in years.
3. Height: Height in meters.
4. Weight: Weight in kilograms.
5. Family History: Does the individual have a family history of overweight issues?
6. FAVC: Frequency of consuming high-caloric food.
7. FCVC: Frequency of consuming vegetables during meals.
8. NCP: Number of main meals consumed daily.
9. CAEC: Consumption of food between meals.
10. Smoke: Does the individual smoke?
11. CH2O: Daily water intake.
12. SCC: Monitoring of daily caloric intake.
13. FAF: Frequency of physical activity.
14. TUE: Daily usage of technological devices like smartphones, video games, and computers.
15. CALC: Frequency of alcohol consumption.
16. MTRANS: Mode of transportation used (e.g., walking, car, bike).
17. Obesity Level (Target Column): Classification of obesity level:

• Insufficient Weight
• Normal Weight
• Overweight Level I
• Overweight Level II
• Obesity Type I
• Obesity Type II
• Obesity Type III

Advantages of Using the Obesity Prediction Dataset

1. Comprehensive Data: Covers key lifestyle factors and eating habits, making it suitable for predictive modeling and health research.
2. Versatile Applications: Ideal for machine learning, classification tasks, and trend analysis.
3. Global Relevance: Data collected from three countries provides a broader understanding of obesity patterns.
4. Supports Preventive Strategies: Enables the development of targeted interventions for obesity management and awareness.
5. Easy Integration: Structured data allows seamless integration into analytics tools and machine learning pipelines.

Applications of the Dataset

• Healthcare Research: Identify factors contributing to obesity and develop preventive strategies.
• Machine Learning Models: Train predictive models for obesity classification.
• Public Health Initiatives: Create awareness campaigns based on regional obesity trends.
• Behavioral Studies: Analyze the impact of lifestyle habits on obesity.

Why Choose This Dataset?

The Obesity Prediction Dataset is more than just a collection of numbers; it is a gateway to understanding one of the most pressing health challenges globally. By leveraging this data, researchers can uncover insights that pave the way for a healthier future.