Diabetes Readmission Prediction Project

An end-to-end solution for predicting hospital readmissions for diabetes patients using machine learning.

Project Overview

This project provides a comprehensive solution for predicting diabetes patient readmissions, including:

1. Data Processing Pipeline: Scripts for data cleaning, preprocessing, and feature engineering
2. Machine Learning Models: Training and evaluation of predictive models
3. Interactive Dashboard: Visualization and monitoring of readmission risks
4. Resource Allocation: Recommendations for optimizing healthcare resources

🛠️ Tools & Libraries Used

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• Python — Core programming language
• Dash — Web application framework for dashboards
• Dash Bootstrap Components — Bootstrap-styled components for Dash
• Plotly — Interactive graphing library
• NumPy — Numerical computations
• Pandas — Data manipulation and analysis
• CSS — Styling the dashboard components
• OS and Base64 — System operations and image encoding

Team Members

• Shridhar Kumar
• Kritika Gahlawat
• Biswajit Gorai
• Neha Rana
• Saswata Ghosh

📊 Data Visualization Project - Setup Instructions

Follow the steps below to set up and run the Dash-based data visualization application on your local machine.

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🚀 Steps to Run the Application

1. Clone the Repository to E Drive
   Open your terminal and run:

   cd /e/
   git clone https://github.com/Shridhar7-8/Data-Visualization-Project.git

2. Install the Required Dependencies Navigate to the cloned directory and install the Python packages:

   cd E:/Data-Visualization-Project
   pip install -r requirements.txt

3. Run the Dash Application Move to the app directory and run the script:

   cd E:/Data-Visualization-Project/src/visualization
   python dash1.py