USD Probability and Statistics for Artificial Intelligence (AAI-500-02) Final Project

In this repository, we'll be using a NHCS dataset on patient use of drugs to understand patterns of health care delivery and utilization in the U.S. with a focus on opioid and drug overdose from 2020-2023.

For our final project, we'll perform the following operations on this dataset:

• Data Cleaning/Preparation
• Exploratory Data Analysis
• Model Selection
• Model Analysis
• Conclusion and Recommendations.

Dataset Overview

The NHCS collects data on patient care in hospital-based settings to offer insights into health care delivery patterns in the U.S. While the data from 2020-2023 is preliminary and not nationally representative, it can provide valuable insights into the use of opioids and other overdose drugs.

Dataset Summary

• Data from 25 hospitals for inpatient and 25 hospitals for emergency departments (ED).
• Data spans from January 1, 2020, to May 27, 2023.
• The dataset includes information on various indicators related to drug use, such as overall drug use, comorbidities, drug, and polydrug overdose.

Repository Structure

• data/: Folder containing the raw data.
• notebooks/: Jupyter notebooks for data analysis, model selection, and evaluation.
• README.md: This file.

Report Sections

1. Introduction

   • Background of the NHCS and its importance.
   • Overview of the opioid crisis and the relevance of the dataset.

2. Data Cleaning/Preparation

   • Data wrangling steps.
   • Handling missing values, outliers, and data transformations.

3. Exploratory Data Analysis

   • Data distributions, trends, and patterns.
   • Visualizations of key metrics and features.

4. Model Selection

   • Criteria for model selection.
   • Comparisons of different models and their performance metrics.

5. Model Analysis

   • Detailed analysis of the selected model.
   • Feature importance, model evaluation, and validation.

6. Conclusion and Recommendations

   • Key findings from the analysis.
   • Recommendations for health care policy, hospital practices, and further research.

7. Appendix

   • Output of code from the technical Jupyter Notebook.

Getting Started

1. Clone this repository.
2. run jupyter notebook
3. Navigate to the notebooks/ directory and open the project notebook.
4. Follow the instructions in the notebook to run the analysis.

Tools and Technologies

• Python
• Jupyter Notebook
• Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn

Contributors

Brian Morris, Will Kencel

License

This project is licensed under the MIT License.