Diabetes Prediction Model 🩺📊

Welcome to the Diabetes Prediction Model repository! This project is aimed at predicting diabetes using logistic regression on a dataset. Below you'll find details on how to use the model, its performance, and how to get started.

📋 Project Overview

This project involves building a logistic regression model to predict diabetes based on various features. The dataset used includes information on demographics, health conditions, and lifestyle factors.

🚀 Getting Started

Clone the Repository

git clone https://github.com/Armanx200/Diabetes_Model.git
cd Diabetes_Model

Install Dependencies Ensure you have Python 3.12 or later. Install the required packages using:
```
pip install -r requirements.txt
```
Run the Model Execute the following script to run the model:
```
python Model.py
```

📊 Model Performance

The logistic regression model's performance is summarized below:

Accuracy: 96.07%
Confusion Matrix:
```
[[18140   157]
 [  629  1074]]
```

Classification Report:

              precision    recall  f1-score   support

           0       0.97      0.99      0.98     18297
           1       0.87      0.63      0.73      1703

    accuracy                           0.96     20000
   macro avg       0.92      0.81      0.86     20000
weighted avg       0.96      0.96      0.96     20000

📈 Visualizations

Here is a visualization of the model's performance:

🔧 Requirements

This project requires the following Python packages:

pandas
numpy
matplotlib
seaborn
scikit-learn
statsmodels

These dependencies are listed in requirements.txt.

📬 Contact

Feel free to reach out if you have any questions or suggestions:

GitHub: Armanx200