Glucovisor is a Flask web application designed for predicting diabetes based on user-provided health data. It utilizes a SVM machine learning model to make predictions and provides users with an interactive interface for obtaining predictions and exploring the results.

The dataset used for training the machine learning model can be found here.

• Flask: A lightweight web framework for building web applications in Python.
• Tailwind CSS: A utility-first CSS framework for rapidly building custom user interfaces.
• Plotly: A library for creating interactive and dynamic visualizations.
• Joblib: Used for saving and loading machine learning models.
• scikit-learn: A machine learning library for building and evaluating models.

1. User-friendly Interface: An intuitive web interface for users to input their health data.

2. Predictive System: Utilizes a machine learning model to predict whether the user has diabetes.

3. Interactive Visualizations: Employs Plotly for creating dynamic visualizations during exploratory data analysis.

1. Clone the repo

   git clone https://github.com/Hardvan/Glucovisor.git

2. Navigate to the folder

   cd Glucovisor

3. Create a virtual python environment by typing the following in the terminal

   python -m venv .venv

4. Activate the virtual environment

   Windows:

   .\.venv\Scripts\activate

   Linux:

   source .venv/bin/activate

5. Install dependencies by typing the following in the terminal

   pip install -r requirements.txt

6. Run the app

   python app.py

7. Click on the link in the terminal to open the website

   It will look something like this:

   Running on http://127.0.0.1:5000