Breast Cancer Prediction

Table of contents

• Information
• Technologies
• Setup
• Sources
• Inspiration
• Launch
• Team

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Information

• The project Front-end is made using HTML, CSS, & Bootstrap.
  
• The project Back-end is made using Flask (Python) and Machine Learning using python.
  

General Information

• This project is to predict whether the patient breast tumor is malignant or benign upon certain dimensions of the tumor nuclei.
• The ML model has an accuracy up to 97%.
• We reduced the features from 29 feature to only 4 features to make good user experience.
• Although we reduced the features up to 86.2%, this did not make the model to under-fit and maintained high accuracy.
• The Feature Selection is made by two ways as a test of our understanding:
  • Automatic Feature Selection using certain modules.
  • Manual Feature Selection using a function which we made, based on correlation.

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Technologies

Project Front-end is created with:

• HTML version: 5
• CSS version: 3
• Bootstrap version: 5

Project Back-end is created with:

• Flask version: 2.1.0
• numpy version: 1.21.5
• pandas version: 1.4.2
• scikit_learn version: 1.0.2
• scipy version: 1.7.3
• Gunicorn version: 20.1.0

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Setup

To run this project, install it locally using pip:

$ cd ../"project_path"
$ pip install "required_modules"

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Sources

• ML Playlists
• ML Model

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Inspiration

This app Front-end design is inspired by @MrWebDesignerAnas - Mr Web Designer Anas

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Launch

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GitHub Repo

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Hosted Website

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• Data to test the website:

  • Malignant | Benign

    • P: 103.4 | P: 101.1

    • A: 741.6 | A: 686.6

    • R: 15.47 | R: 14.98

    • C: 0.1741 | C: 0.0909

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Team

• Mahmoud Salman (ML Model)
• Omar Anwar (Flask App)
• Maye Khaled (Front-end)
• Mayar Ehab (Front-end)

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