make the program using Juypiter Notebook to carry out the training and testing process of the Decision Tree Method. The dataset used is the Breast Cancer dataset.

The steps that you have to do is as follows: 1. Load Breast Cancer dataset that has been provided by the sklearn library using from sklearn.datasets import load_breast_cancer 2. Split the dataset into 5-fold using functions in sklearn (https: // scikit- learn.org/stable/tutorial/statistical_inference/model_selection.html#cv-generators-tut) 3. Perform the training and testing process using the Decision Tree using the dataset for each fold and report the results (in the form of accuracy for each fold) on the report or include it in Jupyter Notebook (as a reference see function sklearn.tree.DecisionTreeClassifier (..)). 4. Decision Plot Tree used for each fold and include it in the report or report show up on the collected Jupyter Notebook (for the Decision Tree plot can be seen at https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html#sphx-glr-auto- examples-tree-plot-iris-dtc-py).