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README.md

Breast Cancer Classification Portfolio Project

This project demonstrates a portfolio-ready implementation of breast cancer diagnosis prediction using Logistic Regression and Decision Tree classifiers in Python.

Project Overview

The dataset used is the Breast Cancer Wisconsin (Diagnostic) Data Set, containing features extracted from breast cancer biopsy images.

Goals

  • Explore and visualize the dataset.
  • Implement single-feature and multi-feature Logistic Regression classifiers.
  • Implement a Decision Tree classifier.
  • Compare classifier performance using accuracy, precision, recall, and confusion matrices.

Features Used

  • radius_mean
  • area_mean
  • concavity_mean
  • symmetry_mean

Requirements

  • Python 3.10+
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn

How to Run

  1. Clone this repository.
  2. Ensure the dataset cancer.csv is in the same directory.
  3. Install the required packages listed in requirements.txt.
  4. Run the notebook Logistic_Regression_Bc.ipynb.

Visualizations

  • Feature vs Diagnosis stripplots.
  • Confusion matrix heatmap.
  • Classifier comparison bar chart.

License

MIT License