https://github.com/ikanurfitriani/diabetes-prediction

This repository contains code archives for Diabetes Prediction with Machine Learning
https://github.com/ikanurfitriani/diabetes-prediction

decision-tree diabetes-dateset-analysis diabetes-prediction diabetes-prediction-model ipynb-jupyter-notebook jupyter-notebook knn-algorithm linear-regression logistic-regression-algorithm naive-bayes-algorithm python random-forest streamlit-webapp svm-model

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This repository contains code archives for Diabetes Prediction with Machine Learning

• Host: GitHub
• URL: https://github.com/ikanurfitriani/diabetes-prediction
• Owner: ikanurfitriani
• License: mit
• Created: 2024-06-30T08:55:16.000Z (3 months ago)
• Default Branch: master
• Last Pushed: 2024-07-23T12:23:33.000Z (2 months ago)
• Last Synced: 2024-09-23T22:02:14.831Z (2 days ago)
• Topics: decision-tree, diabetes-dateset-analysis, diabetes-prediction, diabetes-prediction-model, ipynb-jupyter-notebook, jupyter-notebook, knn-algorithm, linear-regression, logistic-regression-algorithm, naive-bayes-algorithm, python, random-forest, streamlit-webapp, svm-model
• Language: Jupyter Notebook
• Homepage: https://diabetes-prediction-by-ika.streamlit.app/
• Size: 2.27 MB
• Stars: 5
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Diabetes Prediction

This repository contains a Streamlit application for predicting diabetes based on user input parameters. The prediction is made using a pre-trained machine learning model.

## Deployment

Link deployment for public:

https://diabetes-prediction-by-ika.streamlit.app/

## Contents

- `app.py`: The main Streamlit application script.

- `diabetes_model.pkl`: The trained machine learning model used for prediction.

- `scaler.pkl`: The scaler used to normalize the input features.

- `Diabetes_Prediction-Ika_Nurfitriani.ipynb`: A Jupyter Notebook used for model training and evaluation.

- `requirements.txt`: To specify the Python packages and their versions that are required to run diabetes prediction application.

## Installation

To run this application, you'll need to have Python installed along with the necessary libraries. Ensure you have the following libraries installed:

- streamlit

- pandas

- numpy

- scikit-learn

- pickle

- others

You can install these libraries using the following command:

```

pip install -r requirements.txt

```

Ensure that you have the following files in your working directory:

- `app.py`

- `diabetes_model.pkl`

- `scaler.pkl`

- `Diabetes_Prediction-Ika_Nurfitriani.ipynb`

- `requirements.txt`

## Running the Application

To start the Streamlit application, use the following command:

```

streamlit run app.py

```

This will launch the application locally. Open the provided URL in your web browser to interact with the diabetes prediction model.

## Usage

1. User Input: Enter the required parameters for the prediction.

- Pregnancies

- Glucose

- Blood Pressure

- Skin Thickness

- Insulin

- BMI

- Diabetes Pedigree Function

- Age

2. Prediction: Click the `Predict` button to get the prediction.

- The application will display whether the person is diabetic or non-diabetic.

- If available, the prediction probabilities will also be displayed.

## Screen Capture

The following is a screen capture from the Diabetes Prediction App:

- `SS1`

 

- `SS2`



## Author

[@Ika Nurfitriani](https://github.com/ikanurfitriani)