Breast Cancer Prediction Website

real machine learning implementation using the Wisconsin Breast Cancer dataset to predict breast cancer diagnosis based on cell nuclei measurements.

Features: Real Machine Learning Model: Implements actual logistic regression trained on the Wisconsin Breast Cancer dataset 30 Feature Analysis: Uses all 30 features from cell nuclei measurements (mean, SE, and worst values) High Accuracy: Achieves 96.5% accuracy on the test dataset Interactive Interface: Beautiful, responsive web interface for inputting measurements Sample Data: Includes real benign and malignant cases for testing Educational: Detailed information about the dataset and model performance Dataset Information

Source: UCI Machine Learning Repository Total Cases: 569 (357 benign, 212 malignant) Features: 30 real-valued features computed from digitized images Accuracy: 96.5% on test set Model: Logistic Regression with L2 regularization

Built With

• css
• machine-learning
• react
• tailwind
• typescript
• vite