Obesity

Overview

Obesity is a global health crisis influenced by multiple factors, including diet, physical activity, genetics, and lifestyle choices. This project utilizes a comprehensive dataset to uncover insights about obesity trends, identify key influencing factors, and propose actionable solutions.

Dataset Details

This dataset contains rich survey data covering lifestyle, dietary, and health-related variables. Below is a breakdown of the key attributes and their possible values:

• Sex

  • Male (712)
  • Female (898)

• Age

  • Integer values (in years)

• Height

  • Integer values (in centimeters)

• Overweight/Obese Families

  • Yes (266)
  • No (1344)

• Consumption of Fast Food

  • Yes (436)
  • No (1174)

• Frequency of Vegetable Consumption

  • Rarely (400)
  • Sometimes (708)
  • Always (502)

• Number of Main Meals Per Day

  • 1–2 meals (444)
  • 3 meals (928)
  • More than 3 meals (238)

• Food Intake Between Meals

  • Rarely (346)
  • Sometimes (564)
  • Usually (417)
  • Always (283)

• Smoking

  • Yes (492)
  • No (1118)

• Daily Liquid Intake

  • Less than 1 liter (456)
  • 1–2 liters (523)
  • More than 2 liters (631)

• Calorie Monitoring

  • Yes (286)
  • No (1324)

• Physical Activity Frequency

  • None (206)
  • 1–2 days/week (290)
  • 3–4 days/week (370)
  • 5–6 days/week (358)
  • 6+ days/week (386)

• Daily Technology Use

  • 0–2 hours (382)
  • 3–5 hours (826)
  • More than 5 hours (402)

• Mode of Transportation

  • Automobile (660)
  • Motorbike (94)
  • Bicycle (116)
  • Public transportation (602)
  • Walking (138)

• Target Class (Obesity Category)

  • Underweight (73)
  • Normal (658)
  • Overweight (592)
  • Obesity (287)

Step 1: Understanding the Dataset

• Description: The dataset includes survey data covering various obesity classes.
  • Key Features: Age, dietary habits, physical activity, smoking habits, and fast food consumption.
• Purpose: Build a foundation for:
  • Visualizing obesity trends.
  • Creating machine learning-based predictions.

Step 2: Data Exploration

• Objective: Familiarize yourself with the dataset structure.
  • Import libraries and load the dataset.
  • Examine the data types, missing values, and distributions.
• Learning Opportunity: Practice exploratory data analysis (EDA) to discover patterns and relationships.

Step 3: Data Visualization

• Focus Areas:
  • Analyze obesity class distributions.
  • Explore correlations between lifestyle factors and obesity:
    • Age and obesity class.
    • Impact of fast food consumption.
    • Relationship with physical activity levels.
• Visualization Tools: Matplotlib, Seaborn.
  • Pro Tip: Use a correlation heatmap to highlight key relationships.

Step 4: Machine Learning Implementation

1. Data Preparation:

   • Split data into training and testing sets.
   • Standardize features to ensure accurate model performance.

2. Modeling:

   • Train and evaluate multiple algorithms:
     • Linear Regression
     • Random Forest
     • K-Nearest Neighbors
     • Decision Tree
     • Support Vector Regression
   • Compare performance using metrics like RMSE and ( R^2 ).

3. Prediction Insights:

   • Understand which factors have the most predictive power.

Step 5: Answering Key Questions

Leverage machine learning and EDA to address these critical health-related questions:

1. Does eating more vegetables reduce obesity?
2. Are individuals eating more than three meals a day more likely to be obese?
3. How do obesity rates differ between smokers and non-smokers?
4. How does obesity vary across age groups?
5. Does low physical activity and high fast food consumption lead to higher obesity rates?

Step 6: Insights and Recommendations

• Findings:
  • Relationships between lifestyle choices and obesity.
  • The role of dietary habits and physical activity in maintaining a healthy weight.
  • Age-based obesity trends and risk factors.
• Recommendations:
  • Encourage healthy eating habits.
  • Promote regular physical activity.
  • Target interventions based on age-specific trends.