https://github.com/sreelekhakummetha/diabetes-prediction
"Flask web app for diabetes prediction using ML"
https://github.com/sreelekhakummetha/diabetes-prediction
css dataset flask html joblib machine-learning numpy pandas scikitlearn-machine-learning
Last synced: about 2 months ago
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"Flask web app for diabetes prediction using ML"
- Host: GitHub
- URL: https://github.com/sreelekhakummetha/diabetes-prediction
- Owner: sreelekhakummetha
- License: mit
- Created: 2025-05-22T15:26:25.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-23T04:27:26.000Z (about 1 year ago)
- Last Synced: 2025-06-21T16:41:39.296Z (about 1 year ago)
- Topics: css, dataset, flask, html, joblib, machine-learning, numpy, pandas, scikitlearn-machine-learning
- Language: HTML
- Homepage:
- Size: 415 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🩺 Diabetes Prediction Web App 🩺
A web application built with **Flask** and **Machine Learning** that predicts whether a person is likely to have diabetes based on medical diagnostic measurements.
---
## 📌 Project Overview
This project uses a **Machine Learning classification model** trained on the Pima Indians Diabetes Database to predict diabetes. The app allows users to input patient health data through a simple web form and get instant predictions.
---
## 📊 Technologies Used
- **Python 3**
- **Flask**
- **Scikit-learn**
- **Pandas**
- **NumPy**
- **HTML / CSS (for frontend)**
- **Joblib** (for model serialization)
---
## 📁 Project Structure
diabetes-prediction-flask/
│
├── app.py # Flask web app
├── prediction.py # Model training and saving script
├── diabetes_model.pkl # Saved trained ML model
├── README.md # Project info and instructions
└── templates/
└── index.html
|__ screenshots/
|___ homepage.png
|___ prediction.png
# Frontend form for user input
---
## ⚙️ How to Run the Project Locally
1. **Clone the Repository**
```bash
git clone https://github.com/sreelekhakummetha/diabetes-prediction-flask.git
cd diabetes-prediction-flask
2. **create virtual environment
python -m venv venv
3.Activate virtual environment
.\venv\Scripts\activate
4.install requirements
5.Run falsk app
python app.py
6.Open the App
Visit http://127.0.0.1:5000/ in your web browser.
📊 Input Features
The app uses the following health parameters for prediction:
Pregnancies — Number of times pregnant
Glucose — Plasma glucose concentration
BloodPressure — Diastolic blood pressure (mm Hg)
SkinThickness — Triceps skinfold thickness (mm)
Insulin — 2-Hour serum insulin (mu U/ml)
BMI — Body mass index (weight in kg/(height in m)^2)
DiabetesPedigreeFunction — Diabetes pedigree function (hereditary factor)
Age — Age in years
📜 License
This project is open-source and free to use under the MIT License.
DEMO
## 📸 Screenshots
### 🔹 Home Page
screenshots/homepage.png
### 🔹 Prediction Result
screenshots/prediction.png