https://github.com/14richa/patient-readmission-analysis
This project focuses on predictive modeling to foresee hospital readmissions of diabetic patients within 30 days post-discharge. By leveraging a dataset spanning a decade (1999-2008) and covering records from 130 US hospitals, the aim is to enhance healthcare management and patient outcomes.
https://github.com/14richa/patient-readmission-analysis
analytics data jupyter-notebook numpy
Last synced: about 2 months ago
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This project focuses on predictive modeling to foresee hospital readmissions of diabetic patients within 30 days post-discharge. By leveraging a dataset spanning a decade (1999-2008) and covering records from 130 US hospitals, the aim is to enhance healthcare management and patient outcomes.
- Host: GitHub
- URL: https://github.com/14richa/patient-readmission-analysis
- Owner: 14Richa
- Created: 2023-11-11T18:52:00.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-04T22:15:09.000Z (over 2 years ago)
- Last Synced: 2025-05-16T15:16:20.965Z (about 1 year ago)
- Topics: analytics, data, jupyter-notebook, numpy
- Language: Jupyter Notebook
- Homepage:
- Size: 5.24 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Predictive Modeling for Hospital Readmission
## Overview
This project focuses on predictive modeling to foresee hospital readmissions of diabetic patients within 30 days post-discharge. By leveraging a dataset spanning a decade (1999-2008) and covering records from 130 US hospitals, the aim is to enhance healthcare management and patient outcomes.
## Data Sources
- The dataset spans a decade (1999-2008) and includes records from 130 US hospitals, focusing on diabetic patients.
- You can access the dataset via the following link: [Diabetes 130-US hospitals for years 1999-2008](https://archive.ics.uci.edu/dataset/296/diabetes+130-us+hospitals+for+years+1999-2008)
## Project Structure
- `diabetic_data.csv`: Contains the dataset used in the analysis.
- `Readmission_Predictions.ipynb`: Includes Jupyter notebook used for exploratory data analysis, data cleaning, and modeling.
- `requirements.txt`: Lists the Python packages and their versions required for this project.
- `Final_Report.pdf`: Contains the final report summarizing the analysis, findings, and conclusions.
## Python Version
This project was developed using `Python 3.9`.
## Setting Up the Development Environment
#### Create a virtual environment
`python3 -m venv env`
#### Activate environment
`source env/bin/activate`
#### Install dependencies
`pip install -r requirements.txt`