Breast Cancer Classification Using Decision Tree Classifier

A pipeline to classify breast tumors (malignant vs. benign) using a Decision Tree model on clinical feature data.

Python 3 & Jupyter Notebook train_test_split MinMaxScaler DecisionTreeClassifier accuracy_score precision_score recall_score f1_score confusion_matrix
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Project Overview

The notebook begins by loading the Breast cancer.csv dataset with pandas and displaying the first rows . It drops non-predictive columns (id, Unnamed: 32), encodes the target diagnosis (M → 1, B → 0) , and scales features with a MinMaxScaler. After splitting into train/test sets, it trains a DecisionTreeClassifier(random_state=42), then evaluates performance via accuracy, precision, recall, F1-score, and confusion matrix.

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Project Details

  • Completion Date February 2025
  • Category Machine Learning
  • Project Type HTML File

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Breast Cancer Classification Using Decision Tree Classifier