Machine learning project analyzing the U.S. Chronic Disease Indicators dataset. Includes data preprocessing, EDA, and four trained models—Logistic Regression, KNN, Decision Tree, and Random Forest—with evaluation metrics and visualizations for classification

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Power BI dashboard analyzing U.S. Chronic Disease Indicators to identify demographic disparities, geographic patterns, temporal trends, and post-COVID impacts on chronic disease mortality.

The U.S. Chronic Disease Indicators dataset, collaboratively developed by the CDC and epidemiological organizations, offers 115 standardized indicators for assessing chronic disease prevalence and informing public health interventions.

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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.

scripts to prepare U.S. Chronic Disease Indicators for visualization

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

Repo for U.S. Chronic Disease Indicators: Nutrition, Physical Activity, and Weight Status from Scratch

U.S. Chronic Disease Indicators (CDI) Metadata Updated: February 26, 2020 CDC's Division of Population Health 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.

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]

Using the CDCs Chronic Disease Indicators (CDI) dataset to explore how behaviors and socioeconomic conditions intersect with chronic disease burden across U.S. populations.

Chronic Diseases in the U.S.: Overview, Gender Gap, and State Comparison

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.

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Learn to chat with data using multiple approaches with LLM frameworks, such as LangChain, LlamaIndex, and OpenAI's Custom GPT.

Interactive Tableau dashboard analyzing obesity, diabetes, and other chronic disease indicators across U.S. states using CDC data.

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.

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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.

This project analyzes 204,130 state-level health records from the U.S. Chronic Disease Indicators dataset provided by the Centers for Disease Control and Prevention.

Chronic disease analysis project exploring US trends in asthma, diabetes, and cardiovascular disease using CDC data. Features advanced preprocessing, comparative and geospatial analysis, and an interactive Dash dashboard for actionable public health insights.

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.

Conversational AI agent for querying U.S. Chronic Disease Indicators using natural language — powered by GPT-4o, LangGraph, and PostgreSQL + pgvector for hybrid SQL and semantic search.

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.

Physical activity and overweight obesity are two of ten leading health indicators for the prevention of long-term chronic disease as defined by the U.S Department of Health and Human Services.

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.