BREAST CANCER PREDICTION MODEL

This is a machine learning model which helps in predicting Breast Cancer

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Table Of Contents

• About the Project
• Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Roadmap
• Contributing
• License
• Authors
• Acknowledgements

About The Project

A breast cancer prediction model utilizing machine learning techniques is a sophisticated algorithmic system designed to analyze various factors and patterns associated with breast cancer. It typically integrates vast datasets. Through iterative training and validation processes using labeled data, the model becomes increasingly accurate in its predictions, enabling healthcare professionals to intervene early and provide personalized care to patients at higher risk of breast cancer.

Built With

In this project, I used Google Colab for coding and some open-source Python library like numpy and pandas, etc

Getting Started

To get a local copy up and running follow these simple example steps.

1. Go to code section.
2. Copy the link.
3. OR Download the the complete file.

Prerequisites

Go to Google Colab, then you can straight start coding.

Installation

1. import numpy as np
2. import pandas as pd
3. import sklearn.datasets

Usage

This model prediction the type of tumor found in breast cancer. It show [0] Malignant and [1] for benign.

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

• If you have suggestions for adding or removing projects, feel free to open an issue to discuss it, or directly create a pull request after you edit the README.md file with necessary changes.
• Please make sure you check your spelling and grammar.
• Create individual PR for each suggestion.
• Please also read through the Code Of Conduct before posting your first idea as well.

Creating A Pull Request

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Authors

• Md Mushir Alam - Computer Science Student - Md Mushir Alam - **

Acknowledgements

• Md Mushir Alam