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Breast Cancer Prediction Using CART and Naive Bayes

Project Summary

This project applies Naive Bayes and CART (Classification and Regression Trees) decision tree models for predicting breast cancer outcomes using the breast-cancer.arff dataset obtained from the UC Irvine Machine Learning Repository. The project includes the application and evaluation of both models and visualization of the decision trees.

How It Works

Requirements

Before running the project, you need to install the required Python libraries. You can install them using the following command:

pip install -r requirements.txt

Dataset

The project uses the breast-cancer.arff file as its dataset, which should be present in the project's main directory.

Execution

To run the project, execute the main.py script. You can run this script using the following command:

python main.py

Results

When you run the project, it trains and tests the Naive Bayes and CART decision tree models. It visualizes the confusion matrix and ROC curve for both models and generates a tree visualization for the CART decision tree model.

License

This project is released under the MIT License.

About

This repository offers a comparative analysis of CART Decision Tree and Naive Bayes algorithms using the breast cancer dataset. It includes Python code, dataset, ROC visualizations, and a guide for predicting cancer occurrences, highlighting the effectiveness of each algorithm in a concise, impactful manner.

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