인공지능 실습

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			from sklearn.datasets import load_iris
iris = load_iris()
 
from sklearn.model_selection import train_test_split
 
# 입력과 출력을 설정한다. 
X = iris.data
y = iris.target
 
# 전체 데이터를 학습 데이터와 테스트 데이터 비율 (80:20)으로 분할한다. 
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=4)
print(X_train.shape)
 
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
 
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X, y)
 
#0 = setosa, 1=versicolor, 2=virginica
classes = {0:'setosa',1:'versicolor',2:'virginica'}
 
# 아직 보지 못한 새로운 데이터를 제시해보자. 
x_new = [[3,4,5,2],
         [5,4,2,2]]
y_predict = knn.predict(x_new)
 
print(classes[y_predict[0]])
print(classes[y_predict[1]])
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