Streamlit Web App to predict the onset of diabetes based on diagnostic measures.

The data for the following example is originally from the National Institute of Diabetes and Digestive and Kidney Diseases and is available on Kaggle. The data contains information on females at least 21 years old of Pima Indian heritage. Since the data has been added to the data/ directory, cloning this repository would suffice.

The project was developed using python 3.6.7 with the following packages.

• Pandas
• Numpy
• Scikit-learn
• Pandas-profiling
• Joblib
• Streamlit

Installation with pip:

pip install -r requirements.txt

Open the terminal in you machine and run the following command to access the web application in your localhost.

streamlit run app.py

Alternatively you can build the Docker container and access the application at localhost:8051 on your browser.

docker build --tag app:1.0 .
docker run --publish 8051:8051 -it app:1.0

• diabetes_prediction_pipeline.ipynb : Jupyter Notebook with all the workings including pre-processing, modelling and inference.
• app.py : Streamlit App script
• requirements.txt : pre-requiste libraries for the project
• models/ : trained model files and scaler objects
• data/ : source data

This repository acts as a guide to [this blog post] where I talk about how I use Streamlit to build Machine Learning Applications quickly. Here we use a real-world example of predicting if a patient has diabetes and built a machine learning model. A Streamlit App was then built using a step-by-step approach in this project.

Kaggle, for providing the data for the machine learning pipeline.
Streamlit, for the open-source library for rapid prototyping.