End-to-end machine learning project involving exploratory data analysis, preprocessing, and model development using the U.S. Chronic Disease Indicators dataset. Implements supervised and unsupervised learning techniques, compares model performance using appropriate evaluation metrics, and derives data-driven insights from healthcare data.

Exploratory Data Analysis and Dashboard on U.S. Chronic Disease Indicators

This project analyzes public health data on chronic diseases impacting the U.S. It explores trends and risk factors using the CDC's Chronic Disease Indicators (CDIs) to inform public health interventions.

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Doing a long-form analysis of the U.S. Chronic Disease Indicators (CDI) dataset found here [https://www.kaggle.com/datasets/utkarshx27/us-chronic-disease-indicators-cdi]

A comprehensive Python-based analytical system for exploring chronic disease indicators from the Centers for Disease Control and Prevention (CDC). Built for the EDAV course with production-ready code, extensive visualizations, and in-depth statistical analysis.

I explored a real-world health dataset using machine learning to uncover insights and patterns. The project includes regression, classification, clustering, correlation analysis, and model evaluation with clear visualizations—strengthening my Python, data analysis, and ML skills.

US Chronic Diseases Indicators dataset comes from CDC's Division of Population Health that provides cross-cutting set of 124 indicators that were developed by consensus and that allows states and territories and large metropolitan areas to uniformly define, collect, and report chronic disease data that are important to public health practice and available for states, territories and large metropolitan areas. In addition to providing access to state specific indicator data, the CDI web site serves as a gateway to additional information and data resources. The CDI website enables public health professionals and policymakers to retrieve uniformly defined state-level and selected metropolitan-level data for chronic diseases and risk factors that have a substantial impact on public health. These indicators are essential for surveillance, prioritization, and evaluation of public health interventions for chronic disease.

In healthcare domain, large volumes of data are generated which is coined by “3 V’s” Volume, Velocity, and Variety. Chronic diseases are an important public health problem, which can result in morbidity, mortality, disability, and decreased quality of life. In this research, big data technique such as R is used to analyze US Chronic Disease Indicators (CDI) that allows states and territories and large metropolitan areas to uniformly define, collect, and report chronic disease data. Analysis shows that highest percentage of alcohol use among youth is seen for Texas and lowest percentage is seen for District of Columbia. The results indicate that Diabetes is the most prevalent disease among top 10 US chronic diseases. Based on the comparison, diabetes prevalence among women aged 18-44 years having sum less than 5000 depicts that Washington state is the highest among all states.

Analysis of U.S. Chronic Disease Indicators data (2019–2022) focusing on adult females with any disability. Includes visual trends and insights using Python and pandas.

Interactive data science project using CDC Chronic Disease Indicators to explore state-level trends in chronic conditions across the U.S. The dashboard enables geographic and temporal analysis, while time series models forecast short-term disease prevalence to support data-driven public health insights.

In my recent exploration of the U.S. Chronic Disease Indicators dataset, I dived deep into data cleaning, statistical analysis, and visual storytelling to uncover health trends across states, especially focusing on Diabetes and Obesity.

Exploratory data analysis and visualization of the CDC’s Chronic Disease Indicators dataset. Includes Python (Pandas, Seaborn, Matplotlib) workflows in Colab for analyzing smoking, obesity, diabetes, and other health trends across U.S. states and years. Designed as a recruiter‑ready portfolio project showcasing data cleaning, visualization