https://github.com/wizardoftrap/breast-cancer-classification-using-naive-bayes

This project uses the Naive Bayes algorithm to classify breast cancer using the scikit-learn library. It includes data preprocessing, model training, evaluation metrics, and visualizations.
https://github.com/wizardoftrap/breast-cancer-classification-using-naive-bayes

Last synced: 21 days ago
JSON representation

This project uses the Naive Bayes algorithm to classify breast cancer using the scikit-learn library. It includes data preprocessing, model training, evaluation metrics, and visualizations.

• Host: GitHub
• URL: https://github.com/wizardoftrap/breast-cancer-classification-using-naive-bayes
• Owner: wizardoftrap
• Created: 2024-12-09T06:42:57.000Z (27 days ago)
• Default Branch: main
• Last Pushed: 2024-12-09T06:59:45.000Z (27 days ago)
• Last Synced: 2024-12-09T07:33:07.398Z (27 days ago)
• Language: Python
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# **Breast Cancer Classification using Naive Bayes**  

The goal of this project is to predict whether a tumor is malignant or benign based on various features extracted from breast cancer diagnostic data. The dataset used is the Breast Cancer Wisconsin Diagnostic dataset provided by scikit-learn.

---

## **Project Overview**  

- **Dataset:** Breast Cancer Wisconsin Diagnostic (from scikit-learn)  

- **Algorithm:** Gaussian Naive Bayes  

- **Metrics:** Accuracy, Confusion Matrix, Classification Report, ROC Curve  

## **Results**  

### **Confusion Matrix**  

The confusion matrix shows the true vs. predicted classifications:  

![Confusion Matrix](/Confusion_Matrix.png)  

---

### **ROC Curve**  

The ROC Curve visualizes the model's ability to distinguish between classes:  

![ROC Curve](/ROC_Curve.png)  

---

## **Technologies Used**  

- **Programming Language:** Python 3.x  

- **Libraries:**  

  - Scikit-learn  

  - Matplotlib  

  - Seaborn  

  - NumPy  

## **COLAB Link**

 https://colab.research.google.com/drive/1u4EnQU0eEsK_7UeFrrOUd8MqA9xYO9nA?usp=sharing