Breast Cancer Classification Using Machine Learning

This project focuses on classifying breast cancer using multiple machine learning algorithms to compare their performance. The dataset is preprocessed and fed into various classifiers, including Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, and Artificial Neural Networks (ANN).

Dataset Info

The dataset used contains features extracted from breast cancer cell images. These features help in predicting whether a tumor is benign or malignant.

Preprocessing

Data preprocessing steps include handling missing values, feature scaling, and splitting the dataset into training and testing sets.

Classification Models

The following models were implemented and evaluated:

1. Naive Bayes
2. K-Nearest Neighbors (KNN)
3. Decision Tree
4. Random Forest
5. Support Vector Machine (SVM)
6. Logistic Regression
7. Artificial Neural Networks (ANN)

Comparison

The performance of these models was compared using accuracy, precision, recall, and F1-score metrics to determine the best approach for breast cancer classification.

How to Run

1. Clone this repository:

   git clone https://github.com/your-username/breast-cancer-classification-ml.git