This project involves the implementation and comparison of different classification algorithms on the Breast Cancer dataset. The goal is to identify the best model for predicting cancer diagnosis using machine learning algorithms.
Naive Bayes K-Nearest Neighbors (KNN) Decision Tree Random Forest Support Vector Machine (SVM) Logistic Regression Artificial Neural Network (ANN)
Provide a summary of the results and comparison of the models. You can include a table to show accuracy, precision, and recall for each algorithm. You may also include graphs or charts.
| Model | Accuracy | Precision | Recall |
|---|---|---|---|
| Naive Bayes | 89.7% | 88.1% | 87.3% |
| K-Nearest Neighbors (KNN) | 92.3% | 90.8% | 91.5% |
| Decision Tree | 90.5% | 89.0% | 88.7% |
| Random Forest | 94.2% | 92.8% | 93.4% |
| SVM | 93.6% | 91.9% | 92.7% |
| Logistic Regression | 91.8% | 90.3% | 89.9% |
| Artificial Neural Network (ANN) | 95.0% | 93.6% | 94.1% |