02-my_iris.py

#split data training
# https://en.wikipedia.org/wiki/Iris_flower_data_set
import sklearn
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
from sklearn import model_selection
#tambahani prediksi
from sklearn.metrics import accuracy_score

iris = datasets.load_iris()
X  =iris.data
Y  =iris.target

X_train, X_test, Y_train, Y_test = model_selection.train_test_split (X, Y, test_size=0.2, random_state=75)

# data test 30 120 data training

model = KNeighborsClassifier()  # default 5
# model = KNeighborsClassifier(n_neighbors==5) #  or
model.fit(X_train, Y_train)  # proses  belajar menghasilkan model yang siap dipakai

# menyiapkan data untuk uji dan training beda
# data set harus seimbang dalam jumlah classs

# melakukan proses prediksi
test = [[5.1, 3.5, 1.4, 0.2]]
predict= model.predict(X_test)
print (predict)
print (accuracy_score(Y_test,predict))