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https://github.com/armanx200/diabetes_model
🚀 A machine learning model predicting diabetes with logistic regression, feature scaling, and VIF analysis. 📊🩺
https://github.com/armanx200/diabetes_model
arman-kianian classification data-analysis data-science data-visualization feature-engineering healthcare logistic-regression machine-learning model-evaluation predictive-modeling python scaling scikit-learn statistical-analysis statsmodels
Last synced: 10 days ago
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🚀 A machine learning model predicting diabetes with logistic regression, feature scaling, and VIF analysis. 📊🩺
- Host: GitHub
- URL: https://github.com/armanx200/diabetes_model
- Owner: Armanx200
- Created: 2024-07-21T11:08:14.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-07-21T11:26:23.000Z (7 months ago)
- Last Synced: 2024-11-24T09:16:33.124Z (2 months ago)
- Topics: arman-kianian, classification, data-analysis, data-science, data-visualization, feature-engineering, healthcare, logistic-regression, machine-learning, model-evaluation, predictive-modeling, python, scaling, scikit-learn, statistical-analysis, statsmodels
- Language: Python
- Homepage: https://github.com/Armanx200
- Size: 1.49 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
# 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
1. **Clone the Repository**
```bash
git clone https://github.com/Armanx200/Diabetes_Model.git
cd Diabetes_Model
```
2. **Install Dependencies**
Ensure you have Python 3.12 or later. Install the required packages using:
```bash
pip install -r requirements.txt
```
3. **Run the Model**
Execute the following script to run the model:
```bash
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](https://github.com/Armanx200)
---