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.

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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:

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ROC Curve

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

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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