Prototype Diabetes Model

This repository contains a prototype machine learning model developed by the Mathemedics 2000 team. The model leverages linear regression to analyze the Pima Indian diabetes dataset provided by the Indian government. The primary function of this model is to predict whether a patient has diabetes based on their medical report, using binary comparisons.

Table of Contents

• Features
• Installation
• Usage
• Dataset
• Contributing
• License

Features

• Linear Regression Model: Utilizes linear regression for prediction.
• Binary Comparison: Provides a binary output indicating diabetes presence.
• Pima Indian Diabetes Dataset: Uses a well-known dataset from the Indian government.

Installation

Prerequisites

Ensure you have the following installed:

• Python 3.7 or higher
• Git

Steps

1. Clone the repository

   git clone https://github.com/jhaabhijeet864/Prototype_Diabetes_Model.git
   cd Prototype_Diabetes_Model

2. Create a virtual environment

   python -m venv venv

3. Activate the virtual environment

   • On Windows:

     .\venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install the required dependencies

   pip install -r requirements.txt

Usage

Training the Model

1. Prepare the dataset

   Ensure the dataset is placed in the appropriate directory as mentioned in the dataset section.

2. Run the training script

   python train_model.py

   This script will train the model on the Pima Indian diabetes dataset.

Making Predictions

1. Use the trained model

   python predict.py --input <path_to_input_file>

   Replace <path_to_input_file> with the path to the file containing the patient's medical report.

Example

Here is an example of how to make a prediction:

python predict.py --input sample_patient_data.csv

The output will indicate whether the patient has diabetes or not based on the provided medical report.

Dataset

The Pima Indian diabetes dataset can be downloaded from Kaggle. Ensure to place the dataset in the data directory of this repository.

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature-branch).
6. Open a Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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