Description of Dataset : The Analysis presents data for the estimation of obesity levels in individuals from the countries of Mexico, Peru, and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2132 records, the records are labeled with the class variable NObesity (Obesity Level), which allows classification of the data using the values of Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III Height
Business Problem: Obesity is a growing concern worldwide, and its prediction is crucial for preventing and managing related health problems. Obesity is one of the biggest risk factors for a variety of chronic diseases, including heart disease and cancer. The World Health Organization (WHO) defines obesity as an abnormal or excessive deposition of fat that has the potential to severely impact health. Obesity can have a detrimental impact on health (BMI)
Objective: The analysis aims to develop a reliable and accurate model for predicting obesity levels based on various demographic and lifestyle factors
Business Questions and Hypothesis:
- Which all features are statistically associated with Obesity levels?
- Is there a significant difference in obesity prevalence between genders?
- Is Family history of overweight influence an individual's obesity status?
- Is Monitoring Calorie Consumption reduces the chance of obesity?
- Is Family History with Overweight and Consumption of High Caloric Food are highly associated?
- Which features are important for the prediction?
- What is the Accuracy, Recall, Precision, and F1 score of the model?
