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.

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.

Project Setup / Installation Instructions

1. Clone the repository from GitHub:

   git clone https://github.com/ikanurfitriani/Diabetes-Prediction.git
   

2. Navigate to the project directory:

   cd Diabetes-Prediction
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Run the Streamlit application:

   streamlit run app.py
   

Screen Capture

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

• SS1

• SS2

Author

@Ika Nurfitriani