- Boosting
- Decision Tree
- Neural Networks
- KNN
- SVM
Utilized Scikit-learn and Jupyter Notebook in these exercises
Using Breast Cancer Wisconsin (Diagnostic) Database: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
"Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image."
Number of Instances: 569 Number of Attributes: 30 numeric, predictive attributes and the class
'feature_names':'mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension'
'target_names': ['malignant', 'benign']