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+# Preventive Healthcare System
+
+This project is an **AI-powered Predictive Healthcare System** that helps identify individuals at risk of developing chronic diseases such as **diabetes, heart disease, or obesity**. The system provides personalized recommendations for preventive care based on lifestyle and medical data.
+
+## Features
+- AI-based health risk prediction for **diabetes**, **heart disease**, and **obesity**.
+- User-friendly form for inputting health and lifestyle data.
+- Displays risk probabilities and provides feedback on how to manage risks.
+- Uses pre-trained machine learning models to assess the risk levels.
+- Integrated with **Streamlit** for interactive web-based applications.
+
+## Prerequisites
+
+Ensure you have the following installed:
+- Python 3.7+
+- `streamlit`
+- `pandas`
+- `scikit-learn`
+- `requests`
+- `Pillow` (for image processing)
+
+Install all the necessary dependencies using the following command:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Installation & Setup
+
+1. **Clone the repository**:
+
+```bash
+git clone https://github.com/aaarif796/AI-Powered-Preventive-Healthcare-System.git
+cd AI-Powered-Preventive-Healthcare-System
+```
+
+2. **Download or Place Model Files**:
+
+Make sure to have the pre-trained model files:
+- `label_encoders.pkl`
+- `lr_dt.pkl` (Logistic Regression model for Diabetes)
+- `lr_ht.pkl` (Logistic Regression model for Heart Disease)
+- `lr_ob.pkl` (Logistic Regression model for Obesity)
+
+Place these files inside the `model` folder.
+
+3. **Add Images**:
+
+Place relevant images in the `images` folder for visual representation.
+
+4. **CSS Styling**:
+
+The application uses a custom CSS file for styling. Ensure you have the `style.css` file in the `style` folder.
+
+## Usage
+
+1. **Run the Application**:
+
+Use Streamlit to launch the app with the following command:
+
+```bash
+streamlit run app.py
+```
+
+2. **Input Data**:
+
+Fill out the form with your general health and lifestyle details (e.g., age, exercise habits, smoking history, etc.).
+
+3. **Receive Feedback**:
+
+The app will predict your risk level for **diabetes**, **heart disease**, and **obesity** based on the data you provide. It will also offer personalized advice based on the risk level.
+
+## Folder Structure
+
+```
+├── images
+│   ├── healthcare.webp
+├── model
+│   ├── label_encoders.pkl
+│   ├── lr_dt.pkl
+│   ├── lr_ht.pkl
+│   ├── lr_ob.pkl
+├── style
+│   ├── style.css
+├── app.py
+├── README.md
+├── requirements.txt
+```
+
+## Model Details
+
+- **label_encoders.pkl**: Used to encode categorical data.
+- **lr_dt.pkl**: Logistic Regression model for predicting the risk of diabetes.
+- **lr_ht.pkl**: Logistic Regression model for predicting heart disease risk.
+- **lr_ob.pkl**: Logistic Regression model for obesity risk.
+
+## Acknowledgments
+
+This application was developed as part of the **TechXcelerate 2024** challenge, focusing on developing a predictive healthcare system using machine learning and AI.
+