Diabetes_predictor

Using machine learning and a data set, diabetes can be detected and the corresponding visualizations can be seen.

See the live demo here:

Diabetes Predictor

Diabetes Detection and A1c Score Measurement

Overview

This is a web application developed in Python using the Streamlit framework. The application serves two primary purposes:

1. Diabetes Detection: It uses a Support Vector Machine (SVM) model to predict diabetes based on input features. The model achieves an accuracy of approximately 78%.

2. A1c Score Measurement: The application provides a tool to measure the A1c score, a critical indicator of blood glucose control over time.

The web application also offers various data visualizations, including scatter plots, line graphs, pie charts, and bar graphs.

Features

• Diabetes Detection: Use the application to predict the likelihood of diabetes based on user-provided information.
• A1c Score Measurement: Measure A1c scores and understand their implications.
• Data Visualizations: Explore and visualize data with scatter plots, line graphs, pie charts, and bar graphs.
• BMI:Calculate your BMI.
• 3D:See the 3D plot.
• ACCURACY:This model is 78% accurate.

Installation

1. Clone the repository:

   git clone https://github.com/CodeRreaper69/Diabetes_predictor
   cd Diabetes_predictor
   

Usage

2. Install the required dependencies:

   pip install -r requirements.txt

3.Data The dataset used for diabetes prediction is located in the data directory. You can find the A1c score measurement data in the same directory.

Model The diabetes detection model is implemented using a Support Vector Machine (SVM). You can find the model implementation in the model directory.