diff --git a/knn_iris.py b/knn_iris.py
deleted file mode 100644
index db25417..0000000
--- a/knn_iris.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from sklearn.datasets import load_iris
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.neighbors import KNeighborsClassifier
-import numpy as np
-
-def knn_iris():
-    """
-    用KNN算法对鸢尾花进行预测
-    :return:
-    """
-    # 1) 获取数据
-    iris = load_iris()
-
-    # 2）划分数据集
-    x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=22)
-
-    # 3）特征工程：标准化
-    transfer = StandardScaler()
-    x_train = transfer.fit_transform(x_train)
-    x_test = transfer.transform(x_test)
-
-    # 4) KNN算法预估器
-    estimator = KNeighborsClassifier(n_neighbors=3)
-    estimator.fit(x_train, y_train)
-
-    # 5）模型估计
-    # 方法1：直接比对真实值和预测值
-    y_predict = estimator.predict(x_test) 
-    print("y_predict:\n", y_predict)
-    print("直接比对真实值和预测值:\n", y_test ==y_predict)
-
-    #方法2：计算准确值
-    score = estimator.score(x_test, y_test)
-    print("准确值为：\n", score)
-
-    # 6) 做出预测
-    X_new = np.array([[1.1, 5.9, 1.4, 2.2]])
-    prediction = estimator.predict(X_new)
-    print("预测目标类别是:{}".format(prediction))
-    print("预测目标花名是:", iris["target_names"][prediction])
-
-    return None
-
-if __name__ =="__main__":
-    knn_iris()
-
-