ML_Hospital_Readmissions

The goal of the project is two-fold:

Binary Classification: Create a classification model that can accurately predict if a patient will be readmitted to the hospital within 30 days of being discharged. A robust prediction can enable healthcare providers to implement preventive measures and provide timely intervention, potentially saving millions of dollars in healthcare costs.
Multiclass Classification: The second objective is to develop a multiclass classifier that predicts the timeframe of a patient’s readmission, with the classes being “No”,“<30 days”, and “>30 days”. This model can provide more nuanced insights into patient risk levels and help hospitals tailor their post-discharge care and follow-up procedures accordingly.

Name		Name	Last commit message	Last commit date
.ipynb_checkpoints		.ipynb_checkpoints
Data		Data
Images		Images
Personal Workbooks		Personal Workbooks
learning_results		learning_results
submissions		submissions
1. Preprocessing and EDA.ipynb		1. Preprocessing and EDA.ipynb
2. Binary classification.ipynb		2. Binary classification.ipynb
3. Multiclass Classification.ipynb		3. Multiclass Classification.ipynb
Project description.pdf		Project description.pdf
README.md		README.md

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