A machine learning web application that predicts the risk of ICU readmission based on patient information. The application is built with Python, Scikit‑Learn, and Streamlit and deployed online for interactive predictions.

📊 Project Overview

Hospital readmissions are costly and can indicate issues in patient care. 

This project uses machine learning to predict whether a patient is likely to be readmitted to the ICU.

The goal is to demonstrate:

• End‑to‑end ML workflow

• Model training and evaluation

• Interactive deployment using Streamlit

• Responsible sharing of models while protecting repository limits and data security

🤖 Model Training

The model was trained using Scikit‑Learn with the dataset included in the repository.

Typical training workflow:

1. Load dataset

2. Clean and preprocess data

3. Train ML classifier

4. Evaluate performance

5. Save trained model

The model file is not stored directly in GitHub because GitHub limits files to 100MB.

📦 Model Hosting

• The trained model is stored externally using Google Drive.

• This keeps the repository lightweight and within GitHub file size limits.

• When the app starts, it automatically downloads the model if it is not present.

⚠️ Note:

Only the file ID is used in the application code. No personal Google Drive credentials or sensitive data are exposed.

📊 Input Features

The prediction model uses the following patient features:

• Age

• Gender

• Time in hospital

• Number of lab procedures

• Diabetes status

• Hypertension status

• Number of medications

• Number of procedures

• Glucose level

• Blood pressure

• Diagnosis code

• BMI

• Smoking status