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This project is done using the dataset inside the framework (scikit-learn):

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.

Algorithms Implemented:

Naive Bayes K-Nearest Neighbors (KNN) Decision Tree Random Forest Support Vector Machine (SVM) Logistic Regression Artificial Neural Network (ANN)

Results and Model Performance

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%

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