This repository contains a machine learning model for breast cancer detection developed using Google Colab. The model employs logistic regression to accurately classify breast tumors as malignant or benign based on various features extracted from medical imaging data.
- Utilizes Logistic Regression for binary classification of breast tumors.
- Achieves 98% accuracy in detecting breast cancer.
- Trained and validated on the Wisconsin Breast Cancer Dataset.
- Implements data preprocessing and feature selection techniques.
- Includes performance evaluation metrics such as accuracy score and visualizations of model performance and feature importance.
- Demonstrates the application of a simple yet effective machine learning algorithm in medical diagnosis, potentially assisting healthcare professionals in early breast cancer detection.
The model was trained and validated on the publicly available Wisconsin Breast Cancer Dataset.
The Colab notebook in this repository provides a step-by-step implementation of the model, from data loading and preprocessing to model training, evaluation, and result visualization.