This repository contains a comparison of different Naive Bayes classifiers (Bernoulli, Gaussian, and Multinomial) for predicting benign and malignant cancer cases. The project includes confusion matrices for each classifier to evaluate their performance.
Accurate cancer diagnosis is crucial for effective treatment. This project aims to compare the performance of three Naive Bayes classifiers in predicting benign and malignant cases. The classifiers used are:
- Bernoulli Naive Bayes
- Gaussian Naive Bayes
- Multinomial Naive Bayes
- Python 3.x
- scikit-learn
- matplotlib
- pandas
The dataset used in this project is the Breast Cancer Wisconsin (Original) Data Set.