ahmed-walid / human-body-level-classification

Human Body Level Classification

Project Purpose

The main objective of this project is to apply machine learning concepts and algorithms to a real-world problem. The selected problem is “Body Level Classification”.

Steps

• Analysis for the given dataset to find the best way to approach it.
• Solving any issues related to the dataset.
• Applying different experiments and leverage any techniques to achieve the highest performance (F1-Score)

Dataset Characteristics

Quick Statistics

• 1477 data samples.
• 16 attributes (+1 for classes).
• 4 output classes.

Attributes Description

• Gender: Male or female.
• Age: Numeric value.
• Height: Numeric value (in meters).
• Weight: Numeric value (in kilograms).
• Fam_Hist: Does the family have a history with obesity?
• H_Cal_Consump: High caloric food consumption.
• Veg_Consump: Frequency of vegetables consumption.
• Meal_Count: Average number of meals per day.
• Food_Between_Meals: Frequency of eating between meals.
• Smoking: Is the person smoking?
• Water_Consump: Frequency of water consumption.
• H_Cal_Burn: Does the body have high calories burn rate?
• Phys_Act: How often does the person do physical activities?
• Time_E_Dev: How much time does person spend on electronic devices.
• Alcohol_Consump: Frequency of alcohols consumption.
• Transport: Which transports does the person usually use?
• Body_Level: Class of human body level.