Health-Conditions-Among-Children-Under-Age-18-in-the-U.S

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Health-Conditions-Among-Children-Under-Age-18-in-the-U.S

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This project aims to derive meaningful insights from the “Health Conditions Among Children Under Age 18 in the United States” dataset using core Python data analysis tools.

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# Health-Conditions-Among-Children-Under-Age-18-in-the-U.S
This project aims to derive meaningful insights from the “Health Conditions Among 
Children Under Age 18 in the United States” dataset using core Python data analysis tools. 
By leveraging libraries such as Pandas, NumPy, Matplotlib, and Seaborn, the project 
applies advanced data cleaning, analysis, and visualization techniques to explore critical 
public health indicators.

The objectives of this project are as follows:
• To perform a comprehensive exploratory data analysis (EDA) of national child health 
survey data using Pandas and NumPy. This includes identifying trends, patterns, and 
variations in health conditions such as asthma, ADHD, and other chronic illnesses 
across multiple time periods and age groups.

• To analyse health condition estimates across demographic segments, with a focus on 
age-based breakdowns. This analysis provides insights into how specific health 
conditions vary among children under 18, particularly in distinct age brackets like 
“Under 5,” “5–9,” and “10–17 years.”

• To visualize trends in health conditions over time using Matplotlib and Seaborn. Line 
plots, violin plots, and bar charts are used to highlight how the prevalence of certain 
conditions has evolved from 1997 to 2022, helping to uncover long-term health trends.

• To generate comparative and distributional visualizations, such as heatmaps, boxplots, 
and KDE plots, which allow for a deeper understanding of estimate variations across 
different categories and conditions.

• To compute statistical summaries and aggregate values, including mean estimates by 
condition, year, and age group, and to investigate the spread and concentration of 
health conditions using standard deviation and percentile-based analysis.

• To extract actionable insights that can support public health decision-making by 
identifying areas of concern, rising conditions, or vulnerable age groups. These findings 
aim to aid healthcare professionals, researchers, and policymakers in developing 
targeted child health strategies.

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