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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
Last synced: about 1 month ago
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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 (5 months ago)
- Default Branch: master
- Last Pushed: 2024-07-23T12:23:33.000Z (4 months ago)
- Last Synced: 2024-10-11T15:41:35.026Z (about 1 month 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)