From c3d3a263c1550f48290ffe8810ff9d06433ab3b8 Mon Sep 17 00:00:00 2001
From: acasaccio <135740620+acasaccio@users.noreply.github.com>
Date: Wed, 7 Aug 2024 20:36:09 -0500
Subject: [PATCH] Update README.md

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+# Data Science Projects
+
+## Overview
+
+Welcome to the Data Science Projects section of my portfolio. This folder contains comprehensive machine learning projects focused on addressing critical issues in healthcare. Each project includes a detailed proposal and an extensive machine learning modeling analysis, showcasing my ability to apply advanced data science techniques to real-world problems.
+
+## Contents
+
+### Heart Disease Prediction
+
+This project involves developing machine learning models to predict heart disease risk. The models, including a Voting Classifier, identified key risk factors and provided insights into feature importance using SHAP values.
+
+#### Documents:
+
+- **Proposal:** Outlines the scope, objectives, and data sources for the heart disease prediction project.
+- **Machine Learning Project:** Detailed analysis, including data preparation, model development, evaluation, and interpretation of results.
+
+#### Conclusion:
+
+The machine learning models developed in this project, particularly the Voting Classifier, successfully identified key risk factors for heart disease. Variables such as oral health, history of angina, age, e-cigarette usage, and self-reported general health significantly contribute to heart disease risk. The use of SHAP values enhanced the transparency and interpretability of the models.
+
+### Predicting Cost Categories Related to Length of Stay Using Supervised and Unsupervised Learning
+
+This project focuses on categorizing patients into cost clusters and predicting costs for excess inpatient days using a hybrid approach of supervised and unsupervised learning.
+
+#### Documents:
+
+- **Proposal:** Outlines the scope, objectives, and data sources for predicting cost categories related to the length of stay.
+- **Machine Learning Project:** Detailed analysis, including data preparation, model development, evaluation, and interpretation of results.
+
+#### Conclusion:
+
+The hybrid approach effectively categorized patients into cost clusters and provided accurate cost predictions for excess inpatient days. This nuanced estimation can significantly improve hospital administrators' resource allocation and financial planning.
+
+### Managing Provider Capacity in Primary Care
+
+This project demonstrates the feasibility of using predictive analytics to manage provider capacity in primary care, providing a structured method for re-assigning patient panels.
+
+#### Documents:
+
+- **Proposal:** Outlines the scope, objectives, and data sources for managing provider capacity in primary care.
+- **Machine Learning Project:** Detailed analysis, including data preparation, model development, evaluation, and interpretation of results.
+
+#### Conclusion:
+
+The models developed provide a structured method for re-assigning patient panels, potentially enhancing operational efficiency and provider satisfaction. Recommendations include periodic algorithm refinement, policy adjustments based on quantified effort scores, and further research incorporating additional variables for a more profound impact.
+
+## How to Use
+
+To view the contents:
+
+1. **Proposals:** Explore the detailed proposals for each project to understand the scope, objectives, and data sources.
+2. **Machine Learning Projects:** Review the comprehensive analyses, including data preparation, model development, evaluation, and interpretation of results.
+
+Feel free to contact me for any questions or further information.
+
+Thank you for exploring my Data Science Projects! Continue to check out other sections of my portfolio for more advanced work and projects.