Obtain a constant result using kNN and decision tree in python

I'm doing a project using the breast cancer dataset. I implemented the kNN model and the decision tree model but when I try to find the best value for k the result changes every time I run the code.

# Finding the optimum number of neighbors 

list1 = []

for k in range(1,20):
    kNN = KNeighborsClassifier(n_neighbors=k)
    kNN.fit(X_train, y_train)
    
    y_pred = kNN.predict(X_test)

    # compute Accuracy on test set
    a = accuracy_score(y_true=y_test, y_pred=y_pred)
    print ("k:", k, " | Accuracy:",  a)
    
    list1.append(accuracy_score(y_test,y_pred))
    
plt.plot(list(range(1,20)), list1)
plt.show()

all_train_acc = []
all_test_acc = []
leaves_vals = range(2,20)

for max_leaves in range(2,20):
    # train and predict
    dt = tree.DecisionTreeClassifier(max_leaf_nodes=max_leaves, random_state = None)
    dt.fit(X_train,y_train)

    # compute Accuracy
    train_acc = accuracy_score(y_true=y_train, y_pred=dt.predict(X_train))
    test_acc = accuracy_score(y_true=y_test, y_pred=dt.predict(X_test))
    print ("Max leaves:", max_leaves, " - Train Accuracy:", train_acc, " - Test Accuracy:", test_acc)
    
    all_train_acc.append(train_acc)
    all_test_acc.append(test_acc)
    

plt.figure()
plt.plot(leaves_vals, all_train_acc)
plt.plot(leaves_vals, all_test_acc)
plt.legend(['train','test'])
plt.xlabel('max leaves')

I'd like to have the same resul every time the code runs, because I have to write a report on the project and I need to show why I get some conclusions and things like that. And a value that changes every time will make it difficult. Thank you