Breast Cancer Classification using Logistic Regression

Overview

This repository contains Python code for implementing a breast cancer classification model using logistic regression.
Logistic regression is a widely used machine learning algorithm for binary classification tasks, making it suitable for predicting whether a given tumor is malignant or benign based on certain features.

Dataset

The dataset used for training and testing the model is the Breast Cancer Wisconsin (Diagnostic) dataset, which is publicly available on the UCI Machine Learning Repository. The dataset includes features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The features describe various characteristics of cell nuclei present in the image.

Dataset source: Breast Cancer Wisconsin (Diagnostic) Data Set

Requirements

Make sure you have the following dependencies installed:

• Python 3.x
• NumPy
• pandas
• scikit-learn
• matplotlib
• seaborn

You can install these dependencies using the following command:

pip install numpy pandas scikit-learn matplotlib seaborn

Clone the repository:

git clone https://github.com/Cyberkid2311/Breast-Cancer-Classification.git

Navigate to the project directory:

cd breast-cancer-classification

Run the breast_cancer_classification.py script:

python breast_cancer_classification.py

This script will load the dataset, preprocess the data, split it into training and testing sets, train the logistic regression model, and evaluate its performance.