ML-Diabetes-Prediction

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ML-Diabetes-Prediction

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Train a machine learning model to predict diabetes status and deploy it to a flask based web app, with visuals using Plotly dash.

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# Training a Machine Learning Model to Predict Diabetes Status and Deploying it to a Web Based Application

This project utilizes machine learning to provide a prediction of a patients diabetes diagnosis status based on the answers of a questionnaire regarding a number of related symptoms. The trained machine learning model is then deployed to a flask based web application hosted on Heroku. An instance of the plotly dash platform integrated into the flask app to provide interactive data visualizations.

- The web application can be viewed [here](https://m-learning-diabetes-prediction.herokuapp.com/).
- A Jupyter notebook with the code used to train the model is available in this repository. Direct link [here](https://github.com/Dylan-Cairns/ml-diabetes-prediction/blob/master/diabetes_jupyter_notebook.ipynb).
- An explanation of the process used to train the model available on the about page of the webapp [here](https://m-learning-diabetes-prediction.herokuapp.com/about).

Technology used:
- python
- jupyter notebooks
- sklearn
- pandas
- flask
- plotly dash
- jquery
- bootstrap 5
- heroku

### Screenshots

Landing Page:

![Screenshot 1](https://raw.githubusercontent.com/Dylan-Cairns/ML-Diabetes-Prediction/master/screenshots/appscreen1.png)

Prediction functionality:

![Screenshot 2](https://raw.githubusercontent.com/Dylan-Cairns/ML-Diabetes-Prediction/master/screenshots/appscreen2.png)

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![Screenshot 4](https://raw.githubusercontent.com/Dylan-Cairns/ML-Diabetes-Prediction/master/screenshots/appscreen4.png)

Data visualizations:

![Screenshot 3](https://raw.githubusercontent.com/Dylan-Cairns/ML-Diabetes-Prediction/master/screenshots/appscreen3.png)

### Learning Outcomes
This project gave me experience developing a workflow for machine learning model training, as well as exposure to common machine learning tools like anaconda, jupyter notebooks, sklearn, pandas, and seaborn.

It also gave me exposure to the flask web framework, a few different ways to think about design and application structure, as well as how to integrate the flask-based plotly dash into an existing flask application.

I also got a chance to work with asynchronous functions using jquery and ajax while building the diagnosis form.

ML-Diabetes-Prediction

ML-Diabetes-Prediction Latest Version Download

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