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Health Data Analysis: Obesity, Nutrition, and Physical Activity

Overview

This project analyzes national and state-level health data to understand relationships between obesity rates, diet (fruit/vegetable intake), and physical activity.

The analysis includes data cleaning, feature engineering, statistical modeling, and clustering to identify patterns and key drivers of obesity across the United States.

Key Questions

  • How does physical activity relate to obesity rates?
  • Is low fruit/vegetable consumption associated with higher obesity?
  • Can states be grouped into clusters based on health behaviors?

Key Findings

  • Higher levels of physical inactivity are positively associated with higher obesity rates.
  • States with lower fruit and vegetable consumption tend to have higher obesity prevalence.
  • Clustering reveals distinct groups of states with similar health behavior patterns (e.g., high inactivity + high obesity).

Methods

Data Processing

  • Filtered national and state-level health data

  • Created standardized indicators:

    • obesity
    • overweight
    • fruit_lt1 (low fruit consumption)
    • veg_lt1 (low vegetable consumption)
    • pa_150 (meets physical activity guidelines)

Statistical Modeling

  • Linear regression models:

    • Obesity vs physical inactivity
    • Obesity vs physical activity (≥150 min/week)
    • Multivariate regression using diet + activity variables

  • Train/test split with evaluation metrics:

    • RMSE
    • MAE
    • R²

Clustering

  • K-means clustering on health indicators

  • Scaled variables for fair comparison

  • Evaluated cluster quality using:

    • Elbow method
    • Silhouette scores

Dimensionality Reduction

  • PCA used to visualize clusters in 2D space

Visualizations

The project includes:

  • Time-series trends (obesity vs overweight)
  • Boxplots of health indicators across states
  • Regression plots (obesity vs activity/diet)
  • Cluster visualizations (PCA projection)

Project Structure

scripts/
  data_processing.R     # data cleaning + feature engineering
  modeling.R            # regression, clustering, evaluation
  visualization.R       # plots

data/
  raw/                  # original dataset (not tracked)
  processed/            # cleaned data (optional)

results/
  figures/              # saved plots

How to Run

source("scripts/data_processing.R")
source("scripts/modeling.R")
source("scripts/visualization.R")

Dataset

Due to file size limitations, the dataset is not included in this repository.

Download the dataset and place it in:

data/raw/cleaned_nutrition_data.csv

Skills Demonstrated

  • Data cleaning and transformation (R, tidyverse)
  • Statistical modeling and evaluation
  • Machine learning (k-means clustering)
  • Data visualization (ggplot2)
  • Feature engineering and pipeline structuring

Future Improvements

  • Add additional predictors (income, education, demographics)
  • Try more advanced models (random forest, regression regularization)
  • Build an interactive dashboard (Shiny or web app)