Comparing 10 different machine learning models to find the best one for breast cancer classification

To replicate:

1. Download .ipynb file
2. Upload to Jupter Notebook or Google Colab
3. Run all cells

Logistic Regression

Logistic Regression is a machine learning model good for categorizing numerical data

Results from the notebook:

Model: Logistic Regression Confusion Matrix: [[ 62 1] [ 2 106]] Classification Report: precision recall f1-score support

       0       0.97      0.98      0.98        63
       1       0.99      0.98      0.99       108

accuracy                           0.98       171

macro avg 0.98 0.98 0.98 171 weighted avg 0.98 0.98 0.98 171

AUC Score: 0.9980893592004703

Logistic Regression has a precision of 0.97, recall of 0.98 and f1 score of 0.98

K-Nearest Neighbors

FIXME: description

Model: K-Nearest Neighbors Confusion Matrix: [[ 59 4] [ 3 105]] Classification Report: precision recall f1-score support

       0       0.95      0.94      0.94        63
       1       0.96      0.97      0.97       108

accuracy                           0.96       171

macro avg 0.96 0.95 0.96 171 weighted avg 0.96 0.96 0.96 171

AUC Score: 0.9776601998824221

FIXME: conclusion

FIXME: the rest of the changes

FIXME:

Rankings by f1 score: [ table ]

Rankings by precision:

[ table ]

Rankings by recall:

[ table]