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.
Link deployment for public: https://diabetes-prediction-by-ika.streamlit.app/
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.
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.pydiabetes_model.pklscaler.pklDiabetes_Prediction-Ika_Nurfitriani.ipynbrequirements.txt
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.
- User Input: Enter the required parameters for the prediction.
- Pregnancies
- Glucose
- Blood Pressure
- Skin Thickness
- Insulin
- BMI
- Diabetes Pedigree Function
- Age
- Prediction: Click the
Predictbutton 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.
The following is a screen capture from the Diabetes Prediction App:
SS1
SS2
