Do classification, evaluate with several metrics and then visualize the results:
Use 3 or more classification algorithms including but not limited to: Random Forest, Support Vector Machines (SVM), k-NN.
Split dataset into training and testing datasets and perform cross validation to evaluate model consistency.
Evaluate the model on multiple characteristics including: Accuracy, Precision, Recall, F1-Score, AUC-ROC for binary classification problems (Not very relevant for our case).
Visualize the results using confusion matrix and ROC curves.
Do classification, evaluate with several metrics and then visualize the results: