Breast-Cancer-Detection

Overview

This project demonstrates the practical application of a logistic regression model on a real-world dataset to predict whether a tumor is benign (not breast cancer) or malignant (breast cancer) based on its characteristics. The dataset used is the well-known Breast Cancer Wisconsin (Diagnostic) dataset.

Table of Contents

• Overview
• Dataset
• Installation
• Usage
• Model
• Results
• Contributing
• License
• Acknowledgements

Dataset

The Breast Cancer Wisconsin (Diagnostic) dataset is available from the UCI Machine Learning Repository. It includes 569 instances of tumors, each with 30 features that describe the characteristics of the cell nuclei present in the image.

• Features: Mean, standard error, and worst (largest) values for 10 real-valued features computed for each cell nucleus:

  • Radius
  • Texture
  • Perimeter
  • Area
  • Smoothness
  • Compactness
  • Concavity
  • Concave points
  • Symmetry
  • Fractal dimension

• Target:

  • 0: Malignant
  • 1: Benign

Installation

To run this project, you need to have Python 3.x installed. Follow the steps below to set up the environment:

1. Clone the repository:

   git clone https://github.com/sundarmd/Breast-Cancer-Detection.git
   cd Breast-Cancer-Detection

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

Usage

To train and evaluate the logistic regression model, run the following command:

python main.py

Model

The logistic regression model is implemented using the scikit-learn library. The steps involved in the model training and evaluation are:

1. Data Preprocessing:

   • Load the dataset
   • Handle missing values (if any)
   • Normalize the feature values

2. Model Training:

   • Split the dataset into training and testing sets
   • Train the logistic regression model on the training set

3. Model Evaluation:

   • Evaluate the model on the testing set using metrics such as accuracy, precision, recall, and F1-score

Results

The model achieves the following performance metrics on the test set:

• Accuracy: 95%
• Precision: 94%
• Recall: 96%
• F1-Score: 95%

Contributing

Contributions are welcome! Please follow these steps to contribute:

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

License

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

Acknowledgements

• The UCI Machine Learning Repository for providing the dataset
• The scikit-learn library for the machine learning tools
• All contributors to this project