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TheRealSaiTama / Breast-Cancer-Prediction

simple breast cancer prediction model
MIT License
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Breast Cancer Prediction Model

Breast Cancer Prediction

Overview

This repository contains a breast cancer prediction model built using machine learning techniques. The model is deployed using Flask, providing a user-friendly web interface to predict whether a breast tumor is benign or malignant.

Model Details

The breast cancer prediction model is developed using a dataset containing various features extracted from breast tumor images. The model leverages state-of-the-art machine learning algorithms to predict the diagnosis. For this project, we utilized the popular scikit-learn library to build and train the model.

Dataset

The dataset used to train and test the model is obtained from a reliable source (mention the source, like UCI Machine Learning Repository or Kaggle). It consists of labeled samples with features related to breast tumor characteristics.

Requirements

To run the Flask application and use the breast cancer prediction model, you need the following dependencies:

You can install the required packages using pip. For example:

pip install flask scikit-learn pandas numpy

How to Use

  1. Clone this repository to your local machine:
git clone https://github.com/your-username/breast-cancer-prediction.git
  1. Change into the project directory:
cd breast-cancer-prediction
  1. Run the Flask application:
python app.py

  1. Once the Flask app is running, open your web browser and go to http://localhost:5000 to access the breast cancer prediction interface.

  2. Enter the required tumor features in the provided input fields and click the "Predict" button to get the prediction result.

Contributing

Contributions to improve the model, web interface, or any other aspect of this project are welcome. If you find any bugs or have suggestions for enhancements, please feel free to create an issue or submit a pull request.

License

This project is licensed under the MIT License.

Acknowledgments

Special thanks to IAP and the creators of the dataset used in this project for providing valuable resources.