Breast Cancer Classification using Decision Tree

This project applies a Decision Tree Classifier to the Breast Cancer Wisconsin dataset, a popular dataset available in sklearn.datasets. The goal is to classify tumors as malignant (cancerous) or benign (non-cancerous) based on 30 numerical features.

Features

• The model is trained and evaluated using standard techniques:

  • Train-Test Split
  • Accuracy Score
  • Confusion Matrix
  • Classification Report (Precision, Recall, F1-score)

• It also includes a visualization of both:

  • the confusion matrix (as a heatmap).
  • the decision tree structure itself (using plot_tree from sklearn.tree).

Tools

• sklearn
• matplotlib
• pandas
• python