{"id":49412765,"url":"https://github.com/14richa/patient-readmission-analysis","last_synced_at":"2026-04-29T01:31:25.972Z","repository":{"id":210601009,"uuid":"717532711","full_name":"14Richa/Patient-Readmission-Analysis","owner":"14Richa","description":"This project focuses on predictive modeling to foresee hospital readmissions of diabetic patients within 30 days post-discharge. 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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.\n\n## Data Sources\n- The dataset spans a decade (1999-2008) and includes records from 130 US hospitals, focusing on diabetic patients.\n- 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)\n\n## Project Structure\n- `diabetic_data.csv`: Contains the dataset used in the analysis.\n- `Readmission_Predictions.ipynb`: Includes Jupyter notebook used for exploratory data analysis, data cleaning, and modeling.\n- `requirements.txt`: Lists the Python packages and their versions required for this project.\n- `Final_Report.pdf`: Contains the final report summarizing the analysis, findings, and conclusions.\n\n## Python Version\nThis project was developed using `Python 3.9`.\n\n## Setting Up the Development Environment\n\n#### Create a virtual environment\n\n`python3 -m venv env`\n\n#### Activate environment\n\n`source env/bin/activate`\n\n#### Install dependencies\n\n`pip install -r requirements.txt`\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F14richa%2Fpatient-readmission-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F14richa%2Fpatient-readmission-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F14richa%2Fpatient-readmission-analysis/lists"}